6 papers
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…
Multimodal Claim Extraction for Fact-Checking
Joycelyn Teo, Rui Cao, Zhenyun Deng +3
Automated Fact-Checking (AFC) relies on claim extraction as a first step, yet existing methods largely overlook the multimodal nature of today's misinformation. Social media posts…
TCP: a Benchmark for Temporal Constraint-Based Planning
Zifeng Ding, Sikuan Yan, Zhangdie Yuan +3
Temporal reasoning and planning are essential capabilities for large language models (LLMs), yet most existing benchmarks evaluate them in isolation and under limited forms of comp…
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…
Image Tokens Matter: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing
Weixing Wang, Zifeng Ding, Jindong Gu +4
Large Vision-Language Models (LVLMs) with discrete image tokenizers unify multimodal representations by encoding visual inputs into a finite set of tokens. Despite their effectiven…
FOReCAst: The Future Outcome Reasoning and Confidence Assessment Benchmark
Zhangdie Yuan, Zifeng Ding, Andreas Vlachos
Forecasting is an important task in many domains, such as technology and economics. However existing forecasting benchmarks largely lack comprehensive confidence assessment, focus…