52 citations · 53 across the 3 of their papers we have counts for
11 papers · 1 filter
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Seonghyeon Ye, Doyoung Kim, Sungdong Kim +6
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on…
FactKG: Fact Verification via Reasoning on Knowledge Graphs
Jiho Kim, Sungjin Park, Yeonsu Kwon +3
In real world applications, knowledge graphs (KG) are widely used in various domains (e.g. medical applications and dialogue agents). However, for fact verification, KGs have not b…
Can Large Language Models Capture Dissenting Human Voices?
Noah Lee, Na Min An, James Thorne
Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. Augmented by instruction fine-tuning, LLMs have also been shown to generalize in…
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy
Jiwoo Hong, Yejin Cho, Jaemin Jung +2
We address an important gap in detecting political bias in news articles. Previous works that perform document classification can be influenced by the writing style of each news ou…
Data-Efficient Autoregressive Document Retrieval for Fact Verification
James Thorne
Document retrieval is a core component of many knowledge-intensive natural language processing task formulations such as fact verification and question answering. Sources of textua…
Evidence-based Factual Error Correction
James Thorne, Andreas Vlachos
This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied…