5 papers
Averaging Bias: Human Faithfulness Annotations are not Locally Faithful
Huajian Zhang, Yiyang Feng, Jiawei Zhou
Evaluation of faithfulness of text summarization treats a model generated summary as faithful only if every of its sentences is supported by the source document: a strict conjuncti…
Self-Improvement of Large Language Models: A Technical Overview and Future Outlook
Haoyan Yang, Mario Xerri, Solha Park +4
As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability. As models approach…
Unraveling Misinformation Propagation in LLM Reasoning
Yiyang Feng, Yichen Wang, Shaobo Cui +3
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, positioning them as promising tools for supporting human problem-solving. However, what happens…
Nuance Matters: Probing Epistemic Consistency in Causal Reasoning
Shaobo Cui, Junyou Li, Luca Mouchel +2
To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating inte…
Exploring Defeasibility in Causal Reasoning
Shaobo Cui, Lazar Milikic, Yiyang Feng +4
Defeasibility in causal reasoning implies that the causal relationship between cause and effect can be strengthened or weakened. Namely, the causal strength between cause and effec…