6 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
Qiyuan Zhang, Yufei Wang, Tiezheng YU +9
With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing text generation quality in a wide range of task…
Entropy Law: The Story Behind Data Compression and LLM Performance
Mingjia Yin, Chuhan Wu, Yufei Wang +7
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much…
Mitigating Large Language Model Hallucination with Faithful Finetuning
Minda Hu, Bowei He, Yufei Wang +3
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks. However, they are prone to generating fluent yet untruthful resp…
Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step
Zezhong Wang, Xingshan Zeng, Weiwen Liu +7
Current research found the issue of Early Answering in large language models (LLMs), where the models already have an answer before generating the Chain-of-Thought (CoT). This phen…
Learning to Edit: Aligning LLMs with Knowledge Editing
Yuxin Jiang, Yufei Wang, Chuhan Wu +9
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inp…
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang +6
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications. However, existing benchmarks predominantly fo…