55 citations · 63 across the 10 of their papers we have counts for
8 papers · 1 filter
Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future
Yidong Wang, Xin Wang, Cunxiang Wang +9
Self-Rewarding Language Models propose an architecture in which the Large Language Models(LLMs) both generates responses and evaluates its own outputs via LLM-as-a-Judge prompting,…
RewardAnything: Generalizable Principle-Following Reward Models
Zhuohao Yu, Jiali Zeng, Weizheng Gu +7
Reward Models, essential for guiding Large Language Model optimization, are typically trained on fixed preference datasets, resulting in rigid alignment to single, implicit prefere…
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Xuanwang Zhang, Yunze Song, Yidong Wang +10
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hall…
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-e…
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which…
Cofca: A Step-Wise Counterfactual Multi-hop QA benchmark
Jian Wu, Linyi Yang, Zhen Wang +2
While Large Language Models (LLMs) excel in question-answering (QA) tasks, their real reasoning abilities on multiple evidence retrieval and integration on Multi-hop QA tasks remai…