A Training Data Recipe to Accelerate A* Search with Language Models (2024.findings-emnlp)
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| Challenge: | Recent studies compare Large Language Models (LLMs) to System 1 . a* and A* are heuristic search algorithms with a wall-clock speed-up of search. |
| Approach: | They propose a method to disentangle the requirements of A* from the requirements for LLMs to generalise on this task. |
| Outcome: | The proposed method reduces the number of iterations required to find the solutions by 15x and a wall-clock speed-up of search up to 5x. |
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The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
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ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)
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Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
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A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)
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Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang
| Challenge: | achieving data-efficient post-training of Large Language Models is a key research question. |
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