31 citations · 58 across the 30 of their papers we have counts for
7 papers · 1 filter
Beyond Local Surprise: Grounded Dialogue as Selective Belief Revision under Referential Uncertainty
Ziming Liu, Bhanu Chaitanya Jasti, Ziyang Xu +3
When a speaker refers to a scene that the listener cannot directly see, the listener must decide whether to preserve its current understanding or revise it as new utterances arrive…
Experiences Build Characters: The Linguistic Origins and Functional Impact of LLM Personality
Xi Wang, Mengdie Zhuang, Jiqun Liu
Human problem-solving is enriched by a diversity of styles and personality traits, yet the development of Large Language Models (LLMs) has largely prioritized uniform performance b…
Judging with Personality and Confidence: A Study on Personality-Conditioned LLM Relevance Assessment
Nuo Chen, Hanpei Fang, Piaohong Wang +3
Recent studies have shown that prompting can enable large language models (LLMs) to simulate specific personality traits and produce behaviors that align with those traits. However…
Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing
Nuo Chen, Hanpei Fang, Jiqun Liu +3
Recent research has explored LLMs as scalable tools for relevance labeling, but studies indicate they are susceptible to priming effects, where prior relevance judgments influence…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment
Nuo Chen, Jiqun Liu, Xiaoyu Dong +3
Cognitive biases are systematic deviations in thinking that lead to irrational judgments and problematic decision-making, extensively studied across various fields. Recently, large…
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Xi Wang, Hossein A. Rahmani, Jiqun Liu +1
Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However…