A collection of papers and projects related to LLMs and corresponding data-centric methods.
Other publicly-available materials: [Slide]
If you find our survey useful, please cite the paper:
@article{LLMDATASurvey,
title={A Survey of LLM × DATA},
author={Xuanhe Zhou, Junxuan He, Wei Zhou, Haodong Chen, Zirui Tang, Haoyu Zhao, Xin Tong, Guoliang Li, Youmin Chen, Jun Zhou, Zhaojun Sun, Binyuan Hui, Shuo Wang, Conghui He, Zhiyuan Liu, Jingren Zhou, Fan Wu},
year={2025},
journal={arXiv preprint arXiv:2505.18458},
url={https://arxiv.org/abs/2505.18458}
}
@article{tangllmasanalyst,
title={LLM/Agent-as-Data-Analyst: A Survey},
author={Zirui Tang, Weizheng Wang, Zihang Zhou, Yang Jiao, Bangrui Xu, Boyu Niu, Xuanhe Zhou, Guoliang Li, Yeye He, Wei Zhou, Yitong Song, Cheng Tan, Bin Wang, Conghui He, Xiaoyang Wang, Fan Wu},
year={2025},
journal={arXiv preprint arXiv:2509.23988},
url={https://arxiv.org/abs/2509.23988}
}
The IaaS concept for LLM data (phonetically echoing Infrastructure as a Service) defines the characteristics of high-quality datasets along four key dimensions: (1) Inclusiveness ensures broad coverage across domains, tasks, sources, languages, styles, and modalities. (2) Abundance emphasizes sufficient and well-balanced data volume to support scaling, fine-tuning, and continual learning without overfitting. (3) Articulation requires clear, coherent, and instructive content with step-by-step reasoning to enhance model understanding and task performance. (4) Sanitization involves rigorous filtering to remove private, toxic, unethical, and misleading content, ensuring data safety, neutrality, and compliance.
We observe the evolution of LLM/Agent-as-Data-Analyst techniques follows a five-dimension trajectory: (1) Data Modality (homogeneous → heterogeneous); (2) Analysis Functionality (literal → semantic); (3) Knowledge Scope (closed-world →open-world); (4) Tool Integration (tool-coupled → tool-assisted); (5) Development Autonomy (manual → fully autonomous).
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CommonCrawl: A massive web crawl dataset covering diverse languages and domains; widely used for LLM pretraining. [Source]
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The Stack: A large-scale dataset of permissively licensed source code in multiple programming languages; used for code LLMs. [HuggingFace]
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RedPajama: A replication of LLaMA’s training data recipe with open datasets; spans web, books, arXiv, and more. [Github]
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SlimPajama-627B-DC: A deduplicated and filtered subset of RedPajama (627B tokens); optimized for clean and efficient training. [HuggingFace]
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Alpaca-CoT: Instruction-following dataset enhanced with Chain-of-Thought (CoT) reasoning prompts; used for dialogue fine-tuning. [Github]
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LLaVA-Pretrain: A multimodal dataset with image-text pairs for training visual language models like LLaVA. [HuggingFace]
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Wikipedia: Structured and encyclopedic content; a foundational source for general-purpose language models. [HuggingFace]
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C4: A cleaned version of CommonCrawl data, widely used in models like T5 for high-quality web text. [HuggingFace]
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BookCorpus: Contains free fiction books; often used to teach models long-form language understanding. [HuggingFace]
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Arxiv: Scientific paper corpus from arXiv, covering physics, math, CS, and more; useful for academic language modeling. [HuggingFace]
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PubMed: Biomedical literature dataset from the PubMed database; key resource for medical domain models. [Source]
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StackExchange: Community Q&A data covering domains like programming, math, philosophy, etc.; useful for QA and dialogue tasks. [Source]
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OpenWebText2: A high-quality open-source web text dataset based on URLs commonly cited on Reddit; GPT-style training corpus. [Source]
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OpenWebMath: A dataset of math questions and answers; designed to improve mathematical reasoning in LLMs. [HuggingFace]
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Falcon-RefinedWeb: Filtered web data used in training Falcon models; emphasizes data quality through rigorous preprocessing. [HuggingFace]
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CCI 3.0: A large-scale multi-domain Chinese web corpus, suitable for training high-quality Chinese LLMs. [HuggingFace]
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OmniCorpus: A unified multimodal dataset (text, image, audio) designed for general-purpose AI training. [Github]
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WanJuan3.0: A diverse and large-scale Chinese dataset including news, fiction, QA, and more; released by OpenDataLab. [Source]
- OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Hugo Laurençon, Lucile Saulnier, Léo Tronchon, et al. NeurIPS 2023. [Paper] - Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Richard Zemel, et al. ICCV 2015. [Paper]
- MedicalGPT: Training Medical GPT Model
Ming Xu. 2025. [Github] - BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
Dakuan Lu, Hengkui Wu, Jiaqing Liang, et al. arXiv 2023. [Paper]
- Free dolly: Introducing the world’s first truly open instruction-tuned llm
Mike Conover, Matt Hayes, Ankit Mathur, et al. 2023. [Source]
- MedicalGPT: Training Medical GPT Model [Github]
- DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
Shengbin Yue, Wei Chen, Siyuan Wang, et al. arXiv 2023. [Paper]
- MedicalGPT: Training Medical GPT Model [Github]
- UltraFeedback: Boosting Language Models with Scaled AI Feedback
Ganqu Cui, Lifan Yuan, Ning Ding, et al. ICML 2024. [Paper]
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI. arXiv 2025. [Paper] - Kimi k1.5: Scaling Reinforcement Learning with LLMs
Kimi Team. arXiv 2025. [Paper]
- DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue
Feiyuan Zhang, Dezhi Zhu, James Ming, et al. arXiv 2025. [Paper] - Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
Junde Wu, Jiayuan Zhu, Yunli Qi, et al. arXiv 2024. [Paper] - ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization
Yunxiao Shi, Xing Zi, Zijing Shi, et al. arXiv 2024. [Paper] - PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
Saber Zerhoudi, Michael Granitzer. arXiv 2024. [Paper] - DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services [Paper]
- MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Xiang Yue, Yuansheng Ni, Kai Zhang, et al. CVPR 2024. [Paper] - LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models
Haitao Li, You Chen, Qingyao Ai, et al. NeurIPS 2024. [Paper] - What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, et al. AAAI 2021. [Paper] - Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, et al. arXiv 2021. [Paper]
- STeCa: Step-level Trajectory Calibration for LLM Agent Learning
Hanlin Wang, Jian Wang, Chak Tou Leong, Wenjie Li. arXiv 2025. [Paper] - Large Language Model-Based Agents for Software Engineering: A Survey
Junwei Liu, Kaixin Wang, Yixuan Chen, et al. arXiv 2024. [Paper] - Advancing LLM Reasoning Generalists with Preference Trees
Lifan Yuan, Ganqu Cui, Hanbin Wang, et al. arXiv 2024. [Paper] - Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan, et al. arXiv 2024. [Paper] - Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Ning Ding, Yulin Chen, Bokai Xu, et al. EMNLP 2023. [Paper]
- Project Gutenberg: A large collection of free eBooks from the public domain; supports training language models on long-form literary text. [Source]
- Open Library: A global catalog of books with metadata and some open-access content; useful for multilingual and knowledge-enhanced language modeling. [Source]
- GitHub: The world’s largest open-source code hosting platform; supports training models for code generation and understanding. [Source]
- GitLab: A DevOps platform for hosting both private and open-source projects; provides high-quality programming and documentation data. [Source]
- Bitbucket: A source code hosting platform by Atlassian; suitable for mining enterprise-level software development data. [Source]
- CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al. LREC-COLING 2024. [Paper] - The Stack: 3 TB of permissively licensed source code
Denis Kocetkov, Raymond Li, Loubna Ben Allal, et al. arXiv 2022. [Paper] - mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer
Linting Xue, Noah Constant, Adam Roberts, et al. NAACL 2021. [Paper] - Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, et al. JMLR 2020. [Paper] - CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, et al. arXiv 2019. [Paper] - Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books [Paper]
- Beautiful Soup: A Python-based library for parsing HTML and XML documents; supports extracting structured information from static web pages. [Source]
- Selenium: A browser automation tool that enables interaction with dynamic web pages; suitable for scraping JavaScript-heavy content. [Github]
- Playwright: A browser automation framework developed by Microsoft; supports multi-browser environments and is ideal for high-quality, concurrent web scraping tasks. [Source]
- Puppeteer: A Node.js library that provides a high-level API to control headless Chrome or Chromium; useful for scraping complex pages, taking screenshots, or generating PDFs. [Source]
- An Empirical Comparison of Web Content Extraction Algorithms
Janek Bevendorff, Sanket Gupta, Johannes Kiesel, Benno Stein. SIGIR 2023. [Paper] - Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction
Adrien Barbaresi. ACL 2021 Demo. [Paper] - Fact or Fiction: Content Classification for Digital Libraries
Aidan Finn, N. Kushmerick, Barry Smyth. DELOS Workshops / Conferences 2001. [Paper]
- PaddleOCR: An open-source Optical Character Recognition (OCR) toolkit based on the PaddlePaddle deep learning framework; supports multilingual text detection and recognition, ideal for extracting text from images and document layout analysis. [Github]
- YOLOv10: Real-Time End-to-End Object Detection
Ao Wang, Hui Chen, Lihao Liu, et al. NeurIPS 2024. [Paper] - UMIE: Unified Multimodal Information Extraction with Instruction Tuning
Lin Sun, Kai Zhang, Qingyuan Li, Renze Lou. AAAI 2024. [Paper] - ChatEL: Entity Linking with Chatbots
Yifan Ding, Qingkai Zeng, Tim Weninger. LREC-COLING 2024. [Paper] - Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
Haoran Wei, Lingyu Kong, Jinyue Chen, et al. ECCV 2024. [Paper] - General OCR Theory: Towards OCR - 2.0 via a Unified End - to - end Model
Haoran Wei, Chenglong Liu, Jinyue Chen, et al. arXiv 2024. [Paper] - Focus Anywhere for Fine-grained Multi-page Document Understanding
Chenglong Liu, Haoran Wei, Jinyue Chen, et al. arXiv 2024. [Paper] - MinerU: An Open-Source Solution for Precise Document Content Extraction
Bin Wang, Chao Xu, Xiaomeng Zhao, et al. arXiv 2024. [Paper] - WebIE: Faithful and Robust Information Extraction on the Web
Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, et al. ACL 2023. [Paper] - ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
Tom Ayoola, Shubhi Tyagi, Joseph Fisher, et al. NAACL 2022 Industry Track. [Paper] - Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction
Keshav Kolluru, Muqeeth Mohammed, Shubham Mittal, et al. ACL 2022. [Paper] - LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Yupan Huang, Tengchao Lv, Lei Cui, et al. ACM Multimedia 2022. [Paper] - Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, et al. ICML 2021. [Paper] - Tesseract: an open-source optical character recognition engine
Anthony Kay. Linux Journal, Volume 2007. [Paper]
- Analysis of the Reasoning with Redundant Information Provided Ability of Large Language Models
Wenbei Xie. arXiv 2023. [Paper] - Scaling Laws and Interpretability of Learning from Repeated Data
Danny Hernandez, Tom Brown, Tom Conerly, et al. arXiv 2022. [Paper]
- BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
Guosheng Dong, Da Pan, Yiding Sun, et al. arXiv 2024. [Paper] - Deduplicating Training Data Makes Language Models Better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, et al. ACL 2022. [Paper] - Suffix arrays: a new method for on-line string searches
Udi Manber, Gene Myers. SIAM Journal on Computing 1993. [Paper]
- BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline [Paper]
- LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Arham Khan, Robert Underwood, Carlo Siebenschuh, et al. arXiv 2024. [Paper] - SimiSketch: Efficiently Estimating Similarity of streaming Multisets
Fenghao Dong, Yang He, Yutong Liang, et al. arXiv 2024. [Paper] - DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication
Igor Nunes, Mike Heddes, Pere Vergés, et al. KDD 2023. [Paper] - Formalizing BPE Tokenization
Martin Berglund (Umeå University), Brink van der Merwe (Stellenbosch University). NCMA 2023. [Paper] - SlimPajama-DC: Understanding Data Combinations for LLM Training
Zhiqiang Shen, Tianhua Tao, Liqun Ma, et al. arXiv 2023. [Paper] - Deduplicating Training Data Makes Language Models Better [Paper]
- Noise-Robust De-Duplication at Scale
Emily Silcock, Luca D'Amico-Wong, Jinglin Yang, Melissa Dell. arXiv 2022. [Paper] - In Defense of Minhash over Simhash
Anshumali Shrivastava, Ping Li. AISTATS 2014. [Paper] - Similarity estimation techniques from rounding algorithms
Moses S. Charikar. STOC 2002. [Paper] - On the Resemblance and Containment of Documents
A. Broder. Compression and Complexity of SEQUENCES 1997. [Paper]
- SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training
Nan He, Weichen Xiong, Hanwen Liu, et al. ACL 2024. [Paper]
- FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplication
Eric Slyman, Stefan Lee, Scott Cohen, Kushal Kafle. CVPR 2024. [Paper] - Effective Pruning of Web-Scale Datasets Based on Complexity of Concept Clusters
Amro Abbas, Evgenia Rusak, Kushal Tirumala, et al. ICLR 2024. [Paper] - D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan, Ari Morcos. NeurIPS 2023. [Paper] - SemDeDup: Data-efficient learning at web-scale through semantic deduplication
Amro Abbas, Kushal Tirumala, Dániel Simig, et al. ICLR 2023. [Paper] - OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal, et al. arXiv 2022. [Paper] - Learning Transferable Visual Models From Natural Language Supervision [Paper]
- OpenCLIP
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, et al. 2021. [Paper] - LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, et al. NeurIPS 2021. [Paper]
- DataComp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al. NeurIPS 2023. [Paper] - SemDeDup: Data-efficient learning at web-scale through semantic deduplication [Paper]
- Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection
Shuhei Yokoo. arXiv 2021. [Paper] - Learning Transferable Visual Models From Natural Language Supervision [Paper]
- Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, et al. ICLR 2025. [Paper] - Data-efficient Fine-tuning for LLM-based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li, et al. SIGIR 2024. [Paper] - SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Yexiao He, Ziyao Wang, Zheyu Shen, et al. NeurIPS 2024. [Paper] - SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
Yu Yang, Siddhartha Mishra, Jeffrey Chiang, et al. NeurIPS 2024. [Paper] - Effective Pruning of Web-Scale Datasets Based on Complexity of Concept Clusters [Paper]
- WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
Can Xu, Qingfeng Sun, Kai Zheng, et al. ICLR 2024. [Paper] - Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Ming Li, Yong Zhang, Shwai He, et al. ACL 2024. [Paper] - Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
Dheeraj Mekala, Alex Nguyen, Jingbo Shang. ACL 2024. [Paper] - Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, et al. ACL 2024. [Paper] - From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Ming Li, Yong Zhang, Zhitao Li, et al. NAACL 2024. [Paper] - Improving Pretraining Data Using Perplexity Correlations
Tristan Thrush, Christopher Potts, Tatsunori Hashimoto. arXiv 2024. [Paper] - Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs
The Mosaic Research Team. 2023. [Paper] - Instruction Tuning with GPT-4
Baolin Peng, Chunyuan Li, Pengcheng He, et al. arXiv 2023. [Paper] - DINOv2: Learning Robust Visual Features without Supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, et al. arXiv 2023. [Paper] - The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black, et al. arXiv 2021. [Paper] - Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, et al. OpenAI blog 2019. [Paper] - Bag of Tricks for Efficient Text Classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov. EACL 2017. [Paper] - The Shapley Value: Essays in Honor of Lloyd S. Shapley
A. E. Roth, Ed. Cambridge: Cambridge University Press, 1988. [Source]
- SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection
Han Shen, Pin-Yu Chen, Payel Das, Tianyi Chen. ICLR 2025. [Paper] - SCAR: Data Selection via Style-Consistency-Aware Response Ranking for Efficient Instruction Tuning of Large Language Models
Zhuang Li, Yuncheng Hua, Thuy-Trang Vu, et al. ACL 2025. [Paper] - QuRating: Selecting High-Quality Data for Training Language Models
Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen. ICML 2024. [Paper] - What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
Wei Liu, Weihao Zeng, Keqing He, et al. ICLR 2024. [Paper] - LAB: Large-Scale Alignment for ChatBots
Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, et al. arXiv 2024. [Paper] - Biases in Large Language Models: Origins, Inventory, and Discussion
Roberto Navigli, Simone Conia, Björn Ross. ACM JDIQ, 2023. [Paper]
- Emergent and predictable memorization in large language models
Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, et al. NeurIPS 2023. [Paper] - When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
Max Marion, Ahmet Üstün, Luiza Pozzobon, et al. arXiv 2023. [Paper] - Instruction Mining: Instruction Data Selection for Tuning Large Language Models
Yihan Cao, Yanbin Kang, Chi Wang, Lichao Sun. arXiv 2023. [Paper] - Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone, et al. arXiv 2023. [Paper] - MoDS: Model-oriented Data Selection for Instruction Tuning
Qianlong Du, Chengqing Zong, Jiajun Zhang. arXiv 2023. [Paper] - Economic Hyperparameter Optimization With Blended Search Strategy
Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021. [Paper] - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al. NAACL 2019. [Paper] - Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener, Silvio Savarese. ICLR 2018. [Paper]
- spaCy: An industrial-strength Natural Language Processing (NLP) library that supports tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and more; well-suited for fast and accurate text processing and information extraction. [Source]
- CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
Zhuoyi Yang, Jiayan Teng, Wendi Zheng, et al. ICLR 2025. [Paper] - HunyuanVideo: A Systematic Framework For Large Video Generative Models
Weijie Kong, Qi Tian, Zijian Zhang, et al. arXiv 2025. [Paper] - Wan: Open and Advanced Large-Scale Video Generative Models
Team Wan et al. arXiv 2025. [Paper] - Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
Hang Zhang, Xin Li, Lidong Bing. EMNLP 2023 (System Demonstrations). [Paper] - Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, et al. IEEE S&P 2023. [Paper] - DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
Zhengliang Liu, Yue Huang, Xiaowei Yu, et al. arXiv 2023. [Paper] - Baichuan 2: Open Large-scale Language Models
Aiyuan Yang, Bin Xiao, Bingning Wang, et al. arXiv 2023. [Paper] - Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
Haoning Wu, Erli Zhang, Liang Liao, et al. arXiv 2022. [Paper] - LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs [Paper]
- YOLOX: Exceeding YOLO Series in 2021
Zheng Ge, Songtao Liu, Feng Wang, et al. arXiv 2021. [Paper] - FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
Alan Akbik, Tanja Bergmann, Duncan Blythe, et al. NAACL 2019 Demos. [Paper]
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A Survey on Data Selection for Language Models
Alon Albalak, Yanai Elazar, Sang Michael Xie, et al. arXiv 2024. [Paper] -
A Survey on Data Selection for LLM Instruction Tuning
Jiahao Wang, Bolin Zhang, Qianlong Du, et al. arXiv 2024. [Paper]
- spaCy: [Source]
- Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis
Ruiyang Qin, Jun Xia, Zhenge Jia, et al. DAC 2024. [Paper] - CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training
David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, et al. NeurIPS 2024. [Paper] - Efficient Continual Pre-training for Building Domain Specific Large Language Models
Yong Xie, Karan Aggarwal, Aitzaz Ahmad. Findings of ACL 2024. [Paper] - Data Selection for Language Models via Importance Resampling
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang. NeurIPS 2023. [Paper]
- DSDM: model-aware dataset selection with datamodels
Logan Engstrom, Axel Feldmann, Aleksander Mądry. ICML 2024. [Paper] - LESS: Selecting Influential Data for Targeted Instruction Tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al. ICML 2024. [Paper] - TSDS: Data Selection for Task-Specific Model Finetuning
Zifan Liu, Amin Karbasi, Theodoros Rekatsinas. arXiv 2024. [Paper] - Datamodels: Understanding Predictions with Data and Data with Predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom, et al. ICML 2022. [Paper]
- Autonomous Data Selection with Language Models for Mathematical Texts
Yifan Zhang, Yifan Luo, Yang Yuan, et al. ICLR 2024. [Paper]
- Mixtera: A Data Plane for Foundation Model Training Maximilian Böther, Xiaozhe Yao, Tolga Kerimoglu, Dan Graur, Viktor Gsteiger, Ana Klimovic. arXiv 2025. [Paper]
- Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
Clara Na, Ian Magnusson, Ananya Harsh Jha, et al. EMNLP 2024. [Paper] - Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models
Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, et al. COLING 2024. [Paper]
- BiMix: Bivariate Data Mixing Law for Language Model Pretraining
Ce Ge, Zhijian Ma, Daoyuan Chen, et al. arXiv 2024. [Paper] - Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining
Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al. arXiv 2024. [Paper] - SlimPajama-DC: Understanding Data Combinations for LLM Training [Paper]
- Evaluating Large Language Models Trained on Code [Paper]
- Exploring the limits of transfer learning with a unified text-to-text transformer [Paper]
- CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, et al. NAACL 2019. [Paper] - A mathematical theory of communication
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- ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting
Rui Pan, Jipeng Zhang, Xingyuan Pan, et al. ACL 2025. [Paper] - DoGE: Domain Reweighting with Generalization Estimation
Simin Fan, Matteo Pagliardini, Martin Jaggi. ICML 2024. [Paper] - An overview of bilevel optimization
Benoît Colson, Patrice Marcotte, Gilles Savard. AOR 2007. [Paper]
- Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
Guangyuan Ma, Yongliang Ma, Xing Wu, et al. AAAI 2025. [Paper] - DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
Sang Michael Xie, Hieu Pham, Xuanyi Dong, et al. NeurIPS 2023. [Paper] - Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, et al. arXiv 2023. [Paper]
- RegMix: Data Mixture as Regression for Language Model Pre-training
Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al. ICLR 2025. [Paper] - Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Jiasheng Ye, Peiju Liu, Tianxiang Sun, et al. ICLR 2025. [Paper] - CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models
Jiawei Gu, Zacc Yang, Chuanghao Ding, et al. EMNLP 2024. [Paper] - TinyLlama: An Open-Source Small Language Model
Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, Wei Lu. arXiv 2024. [Paper] - BiMix: Bivariate Data Mixing Law for Language Model Pretraining [Paper]
- D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
Haoran Que, Jiaheng Liu, Ge Zhang, et al. arXiv 2024. [Paper] - Data Proportion Detection for Optimized Data Management for Large Language Models
Hao Liang, Keshi Zhao, Yajie Yang, et al. arXiv 2024. [Paper] - DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining [Paper]
- Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al. NeurIPS 2022. [Paper] - LightGBM: a highly efficient gradient boosting decision tree
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Victor Giannakouris, Immanuel Trummer. Proceedings of the VLDB Endowment, Volume 17, Issue 12, 2024. [Paper]
- DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs
Wei Zhou, Peng Sun, Xuanhe Zhou, et al. arXiv 2025. [Paper] - Query Performance Explanation through Large Language Model for HTAP Systems
Haibo Xiu, Li Zhang, Tieying Zhang, et al. ICDE 2025. [Paper] - D-Bot: Database Diagnosis System using Large Language Models
Xuanhe Zhou, Guoliang Li, Zhaoyan Sun, et al. Proceedings of the VLDB Endowment, Volume 17, Issue 10. 2024. [Paper] - LLM As DBA
Xuanhe Zhou, Guoliang Li, Zhiyuan Liu. arXiv 2023. [Paper]
- GaussMaster: An LLM-based Database Copilot System
Wei Zhou, Ji Sun, Xuanhe Zhou, et al. arXiv 2025. [Paper] - Panda: Performance Debugging for Databases using LLM Agents
Vikramank Singh, Kapil Eknath Vaidya, Vinayshekhar Bannihatti Kumar, et al. CIDR 2024. [Paper] - D-Bot: Database Diagnosis System using Large Language Models [Paper]
- LLM As DBA [Paper]
- LLM for Data Management
Guoliang Li, Xuanhe Zhou, Xinyang Zhao. PVLDB 17(12). 2024. [Paper] - LLM-Enhanced Data Management
Xuanhe Zhou, Xinyang Zhao, Guoliang Li. arXiv 2024. [Paper] - D-Bot: Database Diagnosis System using Large Language Models [Paper]
- Probabilistic classification and clustering in relational data
Ben Taskar, Eran Segal, Daphne Koller. IJCAI'01. 2021. [Paper] - Multilinear tensor regression for longitudinal relational data
Peter D. Hoff. Ann. Appl. Stat. 9 (3) 1169 - 1193, September 2015. [Paper] - Outlier detection in relational data: A case study in geographical information systems
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E. F. Codd. Communications of the ACM, Volume 13, Issue 6. 1970. [Paper]
- Cracking SQL Barriers: An LLM-based Dialect Translation System
Wei Zhou, Yuyang Gao, Xuanhe Zhou, Guoliang Li. Proc. ACM Manag. Data, Vol. 3, No. 3, Article 141, pp. 1-26 (2025). [Paper] - CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Models [Paper]
- Natural Language to SQL: State of the Art and Open Problems
Yuyu Luo, Guoliang Li, Ju Fan, Chengliang Chai, Nan Tang. Proc. VLDB Endow., Vol. 18, No. 12, pp. 5466-5471 (2025). [Paper] - A Survey on Employing Large Language Models for Text-to-SQL Tasks
Liang Shi, Zhengju Tang, Nan Zhang, et al. ACM Comput. Surv., Vol. 58, No. 2, Article 54, pp. 1-37 (2025). [Paper] - Bridging the Semantic Gap Between Text and Table: A Case Study on NL2SQL
Lin Long, Xijun Gu, Xinjie Sun, et al. International Conference on Representation Learning 2025 (ICLR 2025). [Paper] - A Survey of Text-to-SQL in the Era of LLMs: Where Are We, and Where Are We Going?
Xinyu Liu, Shuyu Shen, Boyan Li, et al. IEEE Transactions on Knowledge and Data Engineering, 2025. [Paper] - Finsql: Model-agnostic llms-based text-to-sql framework for financial analysis [Paper]
- Codes: Towards building open-source language models for text-to-sql [Paper]
- Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL
Ju Fan, Zihui Gu, Songyue Zhang, et al. Proceedings of the VLDB Endowment, Volume 17, Issue 11 (2024). [Paper] - PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency [Paper]
- CHESS: Contextual Harnessing for Efficient SQL Synthesis [Paper]
- DIN-SQL: decomposed in-context learning of text-to-SQL with self-correction [Paper]
- OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment [Paper]
- The Dawn of Natural Language to SQL: Are We Fully Ready? [Paper]
-
Data Interpreter: An LLM Agent for Data Science [Paper]
-
Collaboration between Intelligent Agents and Large Language Models: A Novel Approach for Enhancing Code Generation Capability
Xingyuan Bai, Shaobin Huang, Chi Wei, et al. Expert Systems with Applications, 2025. [Paper] -
Contextualized Data-Wrangling Code Generation in Computational Notebooks
Junjie Huang, Daya Guo, Chenglong Wang, et al. ASE '24 (2024). [Paper] -
Natural Language to Code Generation in Interactive Data Science Notebooks
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, et al. ACL 2023. [Paper] -
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al. Journal of Machine Learning Research 24 (2023). [Paper] -
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, et al. ACL 2020 (2020). [Paper]
Multi-Step QA
- Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance
Xixi Wang, Miguel Costa, Jordanka Kovaceva, et al. Findings of EMNLP 2025 (2025). [Paper] - TAT-LLM: A Specialized Language Model for Discrete Reasoning over Financial Tabular and Textual Data [Paper]
- Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Zilong Wang, Hao Zhang, Chun-Liang Li, et al. ICLR 2024 (2024). [Paper] - TaPERA: Enhancing Faithfulness and Interpretability in Long-Form Table QA by Content Planning and Execution-based Reasoning [Paper]
- S3HQA: A three-stage approach for multi-hop text-table hybrid question answering [Paper]
- Reactable: Enhancing react for table question answering [Paper]
End-to-End QA
- MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions
Jian Wu, Linyi Yang, Dongyuan Li, et al. ICLR 2025 (2025). [Paper] - Improved Baselines with Visual Instruction Tuning
Haotian Liu, Chunyuan Li, Yuheng Li, et al. CVPR 2024 (2024). [Paper] - Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks [Paper]
- Tablegpt2: A large multimodal model with tabular data integration [Paper]
- Cabinet: Content relevance based noise reduction for table question answering [Paper]
- Tablemaster: A recipe to advance table understanding with language models [Paper]
- Multimodal table understanding [Paper]
- Tabpedia: Towards comprehensive visual table understanding with concept synergy [Paper]
- Judging llm-as-a-judge with mt-bench and chatbot arena [Paper]
- Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era
Chenxi Liu, et al. IJCAI 2025 Survey Track (2025). [Paper] - Association between forecasting models’ precision and nonlinear patterns of daily river flow time series
Farhang Rahmani & Mohammad Hadi Fattahi. Modeling Earth Systems and Environment, 2022. [Paper] - HMCKRAutoEncoder: An Interpretable Deep Learning Framework for Time Series Analysis
Jilong Wang, Rui Li, Renfa Li, et al. IEEE Transactions on Emerging Topics in Computing, 2022. [Paper] - The Performance of LSTM and BiLSTM in Forecasting Time Series
Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin. IEEE International Conference on Big Data, 2019. [Paper] - A Comparison of ARIMA and LSTM in Forecasting Time Series
Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin. ICMLA, 2018. [Paper] - Time Series Databases and InfluxDB
Syeda Noor Zehra Naqvi, Sofia Yfantidou, et al. Université libre de Bruxelles, Advanced Databases, 2017. [Paper]
TS2NL
- TimeRAG: Boosting LLM Time Series Forecasting via Retrieval-Augmented Generation
Silin Yang, Dong Wang, Haoqi Zheng, Ruochun Jin. ICASSP 2025. [Paper] - TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
Geon Lee, Wenchao Yu, Kijung Shin, et al. AAAI 2025. [Paper] - Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
Yushan Jiang, Wenchao Yu, Geon Lee, et al. arXiv:2503.01013 [cs.LG] (2025). [Paper] - From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
Xinlei Wang, Maike Feng, Jing Qiu, et al. NeurIPS 2024. [Paper] - Dynamic Dynamic Time Warping
Karl Bringmann, Nick Fischer, Ivor van der Hoog, et al. SODA 2024. [Paper] - Can Large Language Models be Anomaly Detectors for Time Series?
Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, et al.DSAA 2024. [Paper] - Exploring Large Language Models for Climate Forecasting
Yang Wang, Hassan A. Karimi. arXiv:2411.13724 [cs.LG] (2024). [Paper] - Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
Xinli Yu, Zheng Chen, Yuan Ling, et al. arXiv:2306.11025 [cs.LG] (2023). [Paper]
Alignment
- SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs
Fengze Li, Yue Wang, Yangle Liu, et al. arXiv:2506.20167 [cs.CL] (2025). [Paper] - TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment
Chenxi Liu, Qianxiong Xu, Hao Miao, et al. AAAI 2025. [Paper] - CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Peiyuan Liu, Hang Guo, Tao Dai, et al. AAAI 2025. [Paper] - LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen. ACM Transactions on Intelligent Systems and Technology, Volume 16, Issue 3, Article No. 60, Pages 1 - 20 (2025). [Paper] - Large Language Models are Few-Shot Multivariate Time Series Classifiers
Yakun Chen, Zihao Li, Chao Yang, et al. Data Mining and Knowledge Discovery, Volume 39, Issue 5 (2025). [Paper] - Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Ming Jin, Shiyu Wang, Lintao Ma, et al. ICLR 2024. [Paper] - S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Zijie Pan, Yushan Jiang, Sahil Garg, et al. ICML 2024. [Paper]
- A Comparison of Current Graph Database Models
Renzo Angles. IEEE 28th International Conference on Data Engineering Workshops, 2012. [Paper]
Natural Language To Graph Analysis Query
- Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL
Yuanyuan Liang, Keren Tan, Tingyu Xie, et al. CIKM '24, 2024. [Paper] - NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language [Paper]
- r3-NL2GQL: A model coordination and knowledge graph alignment approach for NL2GQL [Paper]
- Graph learning in the era of llms: A survey from the perspective of data, models, and tasks [Paper]
- Leveraging biomolecule and natural language through multi-modal learning: A survey [Paper]
LLM-based Semantic Analysis
- Retrieval-Then-Reasoning
- G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Xiaoxin He, Yijun Tian, Yifei Sun, et al. NeurIPS 2024. [Paper] - Subgraph retrieval enhanced model for multi-hop knowledge base question answering [Paper]
- Unikgqa: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph [Paper]
- Execution-Then-Reasoning
-
Interactive-kbqa: Multi-turn inter-actions for knowledge base question answering with large language models [Paper]
-
MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering
Guanming Xiong, Haochen Li, Wen Zhao. arXiv:2502.13428 [cs.CL] (2025). [Paper] -
Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering [Paper]
Graph Task Based Fine-tuning Methods
- InstructGraph: Boosting Large Language Models via Graph-Centric Instruction Tuning and Preference Alignment
Jianing Wang, Junda Wu, Yupeng Hou, et al. Findings of ACL 2024. [Paper] - GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
Stefan Dernbach, Khushbu Agarwal, Alejandro Zuniga, et al. AAAI Symposium Series, 3(1), 82-89 (2024). [Paper] - Semi-Supervised Learning With Graph Learning-Convolutional Networks
Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, Bin Luo. CVPR 2019, pp. 11313-11320. [Paper] - Language is all a graph needs [Paper]
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model [Paper]
- Graphgpt: Graph instruction tuning for large language models [Paper]
- Inductive representation learning on large graphs [Paper]
- Agent Based Methods
- KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search
Haoran Luo, Haihong E, Yikai Guo, et al. ICML 2025. [Paper] - Structgpt: A general framework for large language model to reason over structured data [Paper]
- Call me when necessary: Llms can efficiently and faithfully reason over structured environments [Paper]
- Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Tao Yu, Rui Zhang, Kai Yang, et al. EMNLP 2018. [Paper] - Compositional Semantic Parsing on Semi-Structured Tables
Panupong Pasupat, Percy Liang. ACL 2015, Pages 1470–1480. [Paper]
- ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
Zhe Xie, Zeyan Li, Xiao He, et al. Proceedings of the VLDB Endowment, 2025. [Paper] - Relational Data Generation with Graph Neural Networks and Latent Diffusion Models
Valter Hudovernik. TRL @ NeurIPS 2024 Poster, 2024. [Paper] - Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, et al. ICLR 2024. [Paper] - ITF-GAN: Synthetic Time Series Dataset Generation and Manipulation by Interpretable Features
Hendrik Klopries, Andreas Schwung. Knowledge-Based Systems, Volume 283, Issue C (2024). [Paper] - REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers
Aivin V. Solatorio, Olivier Dupriez. arXiv:2302.02041 [cs.LG] (2023). [Paper] - Synthetic Data Generation of Many-to-Many Datasets via Random Graph Generation
Kai Xu, Georgi Ganev, Emile Joubert, et al. ICLR 2023. [Paper] - SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
Tao Yu, Michihiro Yasunaga, Kai Yang, et al. EMNLP 2018. [Paper] - Codes: Towards building open-source language models for text-to-sql [Paper]
- A Temporal Knowledge Graph Generation Dataset Supervised Distantly by Large Language Models
Jun Zhu, Yan Fu, Junlin Zhou, Duanbing Chen. Scientific Data, 12:734 (2025). [Paper] - A Framework for Large-Scale Synthetic Graph Dataset Generation
Sajad Darabi, Piotr Bigaj, Dawid Majchrowski, et al. IEEE Transactions on Neural Networks and Learning Systems, Volume 36, Issue 8, Pages 14258 - 14268 (2025). [Paper]
Markup Extraction
-
Language models enable simple systems for generating structured views of heterogeneous data lakes [Paper]
-
WebFormer: The Web-page Transformer for Structure Information Extraction
Qifan Wang, Yi Fang, Anirudh Ravula, et al. WWW '22, 2022. [Paper]
Markup Query
-
XPath Agent: An Efficient XPath Programming Agent Based on LLM for Web Crawler
Yu Li, Bryce Wang, Xinyu Luan. arXiv:2502.15688 [cs.IR] (2025). [Paper] -
Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation
Jinwei Lu, Yuanfeng Song, Zhiqian Qin, et al. arXiv:2502.11201 [cs.DB] (2025). [Paper]
Markup Understanding
- Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding
Hongshen Xu, Lu Chen, Zihan Zhao, et al. WSDM '24, 2024. [Paper] - DOM-LM: Learning Generalizable Representations for HTML Documents
Xiang Deng, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Huan Sun. arXiv:2201.10608 [cs.CL] (2022). [Paper] - MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding
Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. ACL 2022. [Paper]
Table Representation
- ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Zirui Tang, Boyu Niu, Xuanhe Zhou, et al. arXiv:2508.18190 [cs.AI] (2025). Extension of SIGMOD 2026 paper. [Paper] - Reasoning and Retrieval for Complex Semi-structured Tables via Reinforced Relational Data Transformation
Haoyu Dong, Yue Hu, Yanan Cao. SIGIR '25, Pages 1382 - 1391 (2025). [Paper] - Can an LLM Find Its Way Around a Spreadsheet?
Cho-Ting Lee, Andrew Neeser, Shengzhe Xu, et al. ICSE 2025. [Paper] - Auto-Tables: Relationalize Tables without Using Examples
Peng Li, Yeye He, Cong Yan, et al. SIGMOD Record, Volume 53, Issue 1, Pages 76 - 85 (2024). [Paper] - TUTA: Tree-based Transformers for Generally Structured Table Pre-training
Zhiruo Wang, Haoyu Dong, Ran Jia, et al. KDD '21, Pages 1780 - 1790 (2021). [Paper]
Table Prompting
-
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
Haoyu Dong, Jianbo Zhao, Yuzhang Tian, et al. arXiv:2407.09025 [cs.AI] (2024). [Paper] -
HySem: A Context Length Optimized LLM Pipeline for Unstructured Tabular Extraction
Narayanan PP, Anantharaman Palacode Narayana Iyer. TRL @ NeurIPS 2024 Poster, 2024. [Paper]
Table Querying
- ST-Raptor: LLM-Powered Semi-Structured Table Question Answering [Paper]
- SpreadsheetLLM: encoding spreadsheets for large language models [Paper]
Traditional Approaches
- DVQA: Understanding Data Visualizations via Question Answering
Kushal Kafle, Brian Price, Scott Cohen, Christopher Kanan. CVPR 2018, pp. 5648-5656. [Paper]
Chart Captioning
- ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
Mengsha Liu, Daoyuan Chen, Yaliang Li, et al. LREC-COLING 2024, Pages 3057–3074 (2024). [Paper] - UniChart: A Universal Vision-Language Pretrained Model for Chart Comprehension and Reasoning
Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, Shafiq Joty. EMNLP 2023. [Paper] - FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Ashish Singh, Ashutosh Singh, Prateek Agarwal, et al. arXiv:2307.10867 [cs.CL] (2023). [Paper] - Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
Jason Obeid, Enamul Hoque. INLG 2020, Pages 138–147 (2020). [Paper] - An Architecture for Data-to-Text Systems
Ehud Baruch Reiter. ENLG 07, 2007. [Paper] - Describing Complex Charts in Natural Language: A Caption Generation System
Vibhu O. Mittal, Giuseppe Carenini, Johanna D. Moore, Steven Roth. Computational Linguistics, 1998. [Paper]
Chart Question Answering
- EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding
Muye Huang, Han Lai, Xinyu Zhang, et al. AAAI 2025, 39(4), 3680-3688. [Paper] - Charts-of-Thought: Enhancing LLM Visualization Literacy Through Structured Data Extraction
Amit Kumar Das, Mohammad Tarun, Klaus Mueller. IEEE VIS 2025. [Paper] - ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
Zhengzhuo Xu, Bowen Qu, Yiyan Qi, et al. ICLR 2025. [Paper] - ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild
Ahmed Masry, Megh Thakkar, Aayush Bajaj, et al. COLING 2025, Pages 625–643 (2025). [Paper] - ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
Yifan Wu, Lutao Yan, Leixian Shen, et al. EMNLP 2024, Pages 12174–12200 (2024). [Paper] - VizAbility: Enhancing Chart Accessibility with LLM-based Conversational Interaction
Joshua Gorniak, Yoon Kim, Donglai Wei, et al. UIST '24, Article No. 89, Pages 1 - 19 (2024). [Paper] - ChartLlama: A Multimodal LLM for Chart Understanding and Generation
Yucheng Han, Chi Zhang, Xin Chen, et al. arXiv:2311.16483 [cs.CV] (2023). [Paper] - ChartBench: A Benchmark for Complex Visual Reasoning in Charts
Zhengzhuo Xu, Sinan Du, Yiyan Qi, et al. arXiv:2312.15915 [cs.CV] (2023). [Paper] - mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, et al. arXiv:2304.14178 [cs.CL] (2023). [Paper]
Chart-to-Code
- ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
Cheng Yang, Chufan Shi, Yaxin Liu, et al. ICLR 2025. [Paper] - Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation
Lei Chen, Xuanle Zhao, Zhixiong Zeng, et al. arXiv:2508.13587 [cs.AI] (2025). [Paper] - Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback
Fatemeh Pesaran Zadeh, Juyeon Kim, Jin-Hwa Kim, Gunhee Kim. EMNLP 2024, Pages 11459–11480 (2024). [Paper]
- Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding
Andong Deng, Zhongpai Gao, Anwesa Choudhuri, et al. CVPR 2025. [Paper] - TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval
Leqi Shen, Tianxiang Hao, Tao He, et al. ICLR 2025. [Paper] - Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
Haibo Wang, Zhiyang Xu, Yu Cheng, et al. EMNLP 2025 Findings. [Paper] - Video Token Merging for Long Video Understanding
Seon-Ho Lee, Jue Wang, Zhikang Zhang, David Fan, Xinyu Li. NeurIPS 2024. [Paper] - TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
Shimin Chen, Xiaohan Lan, Yitian Yuan, Zequn Jie, Lin Ma. arXiv:2411.18211 [cs.CV] (2024). [Paper]
-
From Image to Video, what do we need in multimodal LLMs?
Suyuan Huang, Haoxin Zhang, Linqing Zhong, et al. arXiv:2404.11865 [cs.CV] (2024). [Paper] -
LLMs Meet Long Video: Advancing Long Video Question Answering with An Interactive Visual Adapter in LLMs
Yunxin Li, Xinyu Chen, Baotain Hu, Min Zhang. arXiv:2402.13546 [cs.CL] (2024). [Paper]
-
Predicting Team Well-Being through Face Video Analysis with AI
Moritz Müller, Ambre Dupuis, Tobias Zeulner, et al. Applied Sciences, 14(3), 1284 (2024). [Paper] -
AI Based Multimodal Emotion and Behavior Analysis of Interviewee
Aaditya Jadhav, Rushikesh Ghodake, Karthik Muralidharan, G Tarun Varma. IJSREM, May 2023. [Paper]
-
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
Yuqian Yuan, Hang Zhang, Wentong Li, et al. CVPR 2025, pp. 18970-18980 (2025). [Paper] -
Video Summarisation with Incident and Context Information using Generative AI
Ulindu De Silva, Leon Fernando, Kalinga Bandara, Rashmika Nawaratne. IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, 2024. [Paper] -
Abnormal Event Detection in Videos using LSTM Convolutional Autoencoder
Abdelhafid Berroukham, Khalid Housni, Mohammed Lahraichi. ISCV 2024 - International Conference on Intelligent Systems and Computer Vision, 2024. [Paper]
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Utilizing Multimodal Large Language Models for Video Analysis of Posture in Studying Collaborative Learning
Ridwan Whitehead, Andy Nguyen, Sanna Järvelä. Journal of Learning Analytics, Vol. 12 No. 1 (2025). [Paper] -
Artificial Intelligence–Powered 3D Analysis of Video-Based Caregiver-Child Interactions
Zhenzhen Weng, Laura Bravo-Sánchez, Zeyu Wang, et al. Science Advances, Vol. 11, Issue 8 (2025). [Paper]
- VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Shihao Wang, Guo Chen, De-an Huang, et al. arXiv:2507.13353 [cs.CV] (2025). [Paper] - DisCo: Disentangled Control for Realistic Human Dance Generation
Tan Wang, Linjie Li, Kevin Lin, et al. CVPR 2024, pp. 9326-9336 (2024). [Paper] - Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, et al. ICCV 2023, pp. 15954-15964 (2023). [Paper] - Align Your Latents: High-Resolution Video Synthesis With Latent Diffusion Models
Andreas Blattmann, Robin Rombach, Huan Ling, et al. CVPR 2023, pp. 22563-22575 (2023). [Paper] - SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
Wenxuan Zhang, Xiaodong Cun, Xuan Wang, et al. CVPR 2023, pp. 8652-8661 (2023). [Paper] - DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models
Yifeng Ma, Shiwei Zhang, Jiayu Wang, et al. arXiv 2023. [Paper] - Imagen Video: High Definition Video Generation with Diffusion Models
Jonathan Ho, William Chan, Chitwan Saharia, et al. arXiv 2022. [Paper] - Make-A-Video: Text-to-Video Generation without Text-Video Data
Uriel Singer, Adam Polyak, Thomas Hayes, et al. arXiv 2022. [Paper] - NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
Jian Liang, Chenfei Wu, Xiaowei Hu, et al. NeurIPS 2022. [Paper]
- SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding
Jian Chen, Ruiyi Zhang, Yufan Zhou, et al. ICLR 2025. [Paper] - AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models
Sohan Patnaik, Rishabh Jain, Balaji Krishnamurthy, Mausoom Sarkar. CVPR 2025, pp. 23701-23711 (2025). [Paper] - VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation
Manan Suri, Puneet Mathur, Franck Dernoncourt, et al. NAACL 2025, pp. 6088-6109 (2025). [Paper] - LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation
Hengyu Shi, Junhao Su, Junfeng Luo, Jialin Gao. arXiv 2025. [Paper] - Efficient End-to-End Visual Document Understanding with Rationale Distillation
Wang Zhu, Alekh Agarwal, Mandar Joshi, et al. NAACL 2024. [Paper] - VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
Ofir Abramovich, Niv Nayman, Sharon Fogel, et al. ECCV 2024, pp. 241-259 (2024). [Paper] - PosterLlama: Bridging Design Ability of Language Model to Content-Aware Layout Generation
Jaejung Seol, Seojun Kim, Jaejun Yoo. ECCV 2024, pp. 451-468 (2024). [Paper] - SciPostLayout: A Dataset for Layout Analysis and Layout Generation of Scientific Posters
Hao Wang, Shohei Tanaka, Yoshitaka Ushiku. CVPR Workshops 2024, pp. 8136-8141 (2024). [Paper] - OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
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