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20242026
most citedBetter Instruction-Following Through Minimum Bayes Risk

1 citations · 1 across the 6 of their papers we have counts for

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cs.CL2026

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

Seungone Kim, Dongkeun Yoon, Kiril Gashteovski +55

With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remain in question: many scientis…

cs.CL2025

M-Prometheus: A Suite of Open Multilingual LLM Judges

José Pombal, Dongkeun Yoon, Patrick Fernandes +5

The use of language models for automatically evaluating long-form text (LLM-as-a-judge) is becoming increasingly common, yet most LLM judges are optimized exclusively for English,…

cs.CL2025

Scaling Evaluation-time Compute with Reasoning Models as Evaluators

Seungone Kim, Ian Wu, Jinu Lee +8

As language model (LM) outputs get more and more natural, it is becoming more difficult than ever to evaluate their quality. Simultaneously, increasing LMs' "thinking" time through…

cs.CL2024★ 1 cited

Better Instruction-Following Through Minimum Bayes Risk

Ian Wu, Patrick Fernandes, Amanda Bertsch +3

General-purpose LLM judges capable of human-level evaluation provide not only a scalable and accurate way of evaluating instruction-following LLMs but also new avenues for supervis…

cs.CL2024

Synthetic Multimodal Question Generation

Ian Wu, Sravan Jayanthi, Vijay Viswanathan +4

Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of hi…