2 papers
cs.AI2026
APeB: Benchmarking Personalization Ability of Large Language Model Agents
Garry Yang, Zizhe Chen, Xinru Chen +9
LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy inte…
cs.CL2025
Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models
Jialiang Wu, Yi Shen, Sijia Liu +4
Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess…