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cs.AI2026
Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume
Gregory Kang Ruey Lau, Hieu Dao, Nicole Kan Hui Lin +1
Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could…
cs.AI2026
Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble
Bingchen Wang, Zi-Yu Khoo, Jingtan Wang
Large language models are increasingly used as proxies for human subjects in social science research, yet external validity requires that synthetic agents faithfully reflect the pr…