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20212026
most citedHumanity's Last Exam

23 citations · 35 across the 40 of their papers we have counts for

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7 papers · 1 filter

cs.CL2024

MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models

Jianhong Tu, Zhuohao Ni, Nicholas Crispino +8

We present a novel visual instruction tuning strategy to improve the zero-shot task generalization of multimodal large language models by building a firm text-only knowledge base.…

cs.AI2024★ 3 cited

JudgeBench: A Benchmark for Evaluating LLM-based Judges

Sijun Tan, Siyuan Zhuang, Kyle Montgomery +5

LLM-based judges have emerged as a scalable alternative to human evaluation and are increasingly used to assess, compare, and improve models. However, the reliability of LLM-based…

cs.CL2024

Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning

Eric Pasewark, Kyle Montgomery, Kefei Duan +2

We present a new method for large language models to solve compositional tasks. Although they have shown strong performance on traditional language understanding tasks, large langu…

cs.CL2024

Measuring Social Norms of Large Language Models

Ye Yuan, Kexin Tang, Jianhao Shen +2

We present a new challenge to examine whether large language models understand social norms. In contrast to existing datasets, our dataset requires a fundamental understanding of s…

cs.LG2024★ 4 cited

Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study

Chenguang Wang, Ruoxi Jia, Xin Liu +1

Pre-training image representations from the raw text about images enables zero-shot vision transfer to downstream tasks. Through pre-training on millions of samples collected from…

cs.CL2024

Measuring Vision-Language STEM Skills of Neural Models

Jianhao Shen, Ye Yuan, Srbuhi Mirzoyan +2

We introduce a new challenge to test the STEM skills of neural models. The problems in the real world often require solutions, combining knowledge from STEM (science, technology, e…

cs.LG2024

Preference Poisoning Attacks on Reward Model Learning

Junlin Wu, Jiongxiao Wang, Chaowei Xiao +3

Learning reward models from pairwise comparisons is a fundamental component in a number of domains, including autonomous control, conversational agents, and recommendation systems,…