36 citations · 71 across the 16 of their papers we have counts for
16 papers
LLM Internal States Reveal Hallucination Risk Faced With a Query
Ziwei Ji, Delong Chen, Etsuko Ishii +4
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recogni…
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang, Delong Chen, Nayeon Lee +1
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus o…
Contrastive Learning for Inference in Dialogue
Etsuko Ishii, Yan Xu, Bryan Wilie +4
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speake…
Mitigating Framing Bias with Polarity Minimization Loss
Yejin Bang, Nayeon Lee, Pascale Fung
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use…
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
Willy Chung, Samuel Cahyawijaya, Bryan Wilie +2
Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP), yet remain under-explored for task-oriented dialogue systems (TODS), especially…
Towards Mitigating Hallucination in Large Language Models via Self-Reflection
Ziwei Ji, Tiezheng Yu, Yan Xu +3
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. However, the practical deployment still faces…