12 papers · 1 filter
Do Language Models Update their Forecasts with New Information?
Zhangdie Yuan, Zifeng Ding, Andreas Vlachos
Prior work has largely treated forecasting as a static task, failing to consider how forecasts and the confidence in them should evolve as new evidence emerges. To address this gap…
AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web
Rui Cao, Zifeng Ding, Zhijiang Guo +2
Textual claims are often accompanied by images to enhance their credibility and spread on social media, but this also raises concerns about the spread of misinformation. Existing d…
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Eric Chamoun, Nedjma Ousidhoum, Michael Schlichtkrull +1
Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications. Recent studies manually analyzed NLP re…
Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking
Mubashara Akhtar, Michael Schlichtkrull, Andreas Vlachos
Current automated fact-checking (AFC) approaches typically evaluate evidence either implicitly via the predicted verdicts or through exact matches with predefined closed knowledge…
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models
Xiaochen Zhu, Georgi Karadzhov, Chenxi Whitehouse +1
Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. Token-level diffusion doesn't model word-…
PRobELM: Plausibility Ranking Evaluation for Language Models
Zhangdie Yuan, Eric Chamoun, Rami Aly +2
This paper introduces PRobELM (Plausibility Ranking Evaluation for Language Models), a benchmark designed to assess language models' ability to discern more plausible from less pla…