Scientific papers require models to integrate evidence across text, equations, figures, tables, code, and datasets while preserving its provenance. Beyond answer correctness, scientific reading requires verifiable outputs from operations such as evidence localization, definition extraction, and consistency checking. We...
Shenxi Wu, Yu-Hong Liu, Hao-Song Zhang et al.· 0 citations
In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates for their predictions. Existing approaches to uncertainty estimation for LLMs require access to log-probabilities output by the model or require fine-tuning access. Ho...
K. D. Hayes, Arka Pal, Hao-Song Zhang et al.· 0 citations
Scientific papers require models to reason jointly over text, equations, figures, tables, code, and datasets while preserving the provenance of supporting evidence. Existing benchmarks typically evaluate these capabilities in isolation, leaving unclear whether multimodal models can support realistic scientific-reading...
Shenxi Wu, Yu-Hong Liu, Hao-Song Zhang et al.· 0 citations
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