1 citations · 1 across the 5 of their papers we have counts for
6 papers
Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers
Kaiyu He, Zhang Mian, Peilin Wu +2
While Large Language Models (LLMs) excel at factual retrieval, they often struggle with the "curse of two-hop reasoning" in compositional tasks. Recent research suggests that param…
ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models
Qing Zhang, Bing Xu, Xudong Zhang +9
The remarkable performance of Large Language Models (LLMs) highly relies on crafted prompts. However, manual prompt engineering is a laborious process, creating a core bottleneck f…
HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation
Peilin Wu, Mian Zhang, Kun Wan +4
Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answering. However, suboptimal search behavi…
GEAR: A General Evaluation Framework for Abductive Reasoning
Kaiyu He, Peilin Wu, Mian Zhang +4
Since the advent of large language models (LLMs), research has focused on instruction following and deductive reasoning. A central question remains: can these models discover new k…
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo +11
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation
Haijin Wang, Mianrong Zhang, Zheng Chen +4
Carbon footprint accounting is crucial for quantifying greenhouse gas emissions and achieving carbon neutrality.The dynamic nature of processes, accounting rules, carbon-related po…