6 papers · 1 filter
Factuality on Demand: Controlling the Factuality-Informativeness Trade-off in Text Generation
Ziwei Gong, Yanda Chen, Julia Hirschberg +4
Large language models (LLMs) encode knowledge with varying degrees of confidence. When responding to queries, models face an inherent trade-off: they can generate responses that ar…
Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?
Xu Hu, Yifan Zhang, Songtao Wei +4
Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assumed to improve factual correctness. Howev…
An Analysis of Large Language Models for Simulating User Responses in Surveys
Ziyun Yu, Yiru Zhou, Chen Zhao +1
Using Large Language Models (LLMs) to simulate user opinions has received growing attention. Yet LLMs, especially trained with reinforcement learning from human feedback (RLHF), ar…
Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes
Lo Pang-Yun Ting, Chengshuai Zhao, Yu-Hua Zeng +3
Clinical note generation aims to produce free-text summaries of a patient's condition and diagnostic process, with discharge instructions being a representative long-form example.…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
Parallel Structures in Pre-training Data Yield In-Context Learning
Yanda Chen, Chen Zhao, Zhou Yu +2
Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update. Howe…