3 accepted papers
Large Language Models (LLMs), such as the GPT series, have achieved remarkable performance in question answering through large-scale pretraining. However, LLMs often lack transparency in their reasoning processes and struggle with hallucination. To overcome these challenges, we propose Dual-NeSy, a…
Entailment trees are essential for enhancing interpretability and transparency in tasks like question answering and natural language understanding. However, existing approaches often lack logical consistency, as they rely on static reward structures or ignore the intricate dependencies within multi-…
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses. A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses. In this paper, we i…