52 citations · 83 across the 9 of their papers we have counts for
14 papers
VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception
Jiyoung Lee, Seungho Kim, Seunghyun Won +6
AI alignment refers to models acting towards human-intended goals, preferences, or ethical principles. Given that most large-scale deep learning models act as black boxes and canno…
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…
Database Reasoning Over Text
James Thorne, Majid Yazdani, Marzieh Saeidi +3
Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as "List…
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Rami Aly, Zhijiang Guo, Michael Schlichtkrull +5
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation…
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Max Glockner, Ieva Staliūnaitė, James Thorne +3
Automated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute th…
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…
Neural Databases
James Thorne, Majid Yazdani, Marzieh Saeidi +3
In recent years, neural networks have shown impressive performance gains on long-standing AI problems, and in particular, answering queries from natural language text. These advanc…
KILT: a Benchmark for Knowledge Intensive Language Tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan +10
Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models d…
Elastic weight consolidation for better bias inoculation
James Thorne, Andreas Vlachos
The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. Whil…