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From the 1 of 29 linked papers with an AI index.

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20242026
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cs.CL2026

Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning

Zijie Liu, Jinhao Duan, Gaowen Liu +2

Machine unlearning for vision-language models (VLMs) remains underexplored. Unlike language models, VLMs combine a language backbone with visual components, which makes unlearning…

cs.CL2026

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation

Haozhi Fan, Jinhao Duan, Kaidi Xu

Despite the rapid advancement of Large Language Models (LLMs), uncertainty quantification in LLM generation is a persistent challenge. Although recent approaches have achieved stro…

cs.CL2026

EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce

Kaiyan Zhao, Zijie Meng, Zheyong Xie +4

Large Language Model (LLM)-based agents are increasingly deployed in e-commerce applications to assist customer services in tasks such as product inquiries, recommendations, and or…

cs.CL2025

Sparse Neurons Carry Strong Signals of Question Ambiguity in LLMs

Zhuoxuan Zhang, Jinhao Duan, Edward Kim +1

Ambiguity is pervasive in real-world questions, yet large language models (LLMs) often respond with confident answers rather than seeking clarification. In this work, we show that…

cs.CL2025

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

Zhiyuan Wang, Jinhao Duan, Qingni Wang +4

Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approach…

cs.CL2025

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making

Jinhao Duan, James Diffenderfer, Sandeep Madireddy +3

As Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making in the real world, it is essential to know when to trust LLM d…