6 papers
Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
Yoonah Park, Haesung Pyun, Yohan Jo
While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to their internal knowledge. In partic…
Human Psychometric Questionnaires Mischaracterize LLM Behavior
Woojung Song, Dongmin Choi, Yoonah Park +3
We examine whether human psychometric questionnaires can serve as reliable tools for characterizing and predicting LLM behavior in everyday user interactions. We analyze eight open…
Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction
Sejun Park, Yoonah Park, Jongwon Lim +1
Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is imperative to consider the target…
CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions
Tae Soo Kim, Yoonjoo Lee, Yoonah Park +3
Personalization of Large Language Models (LLMs) often assumes users hold static preferences that reflect globally in all tasks. In reality, humans hold dynamic preferences that cha…
Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples
Haesung Pyun, Yoonah Park, Yohan Jo
In dialogue state tracking (DST), in-context learning comprises a retriever that selects labeled dialogues as in-context examples and a DST model that uses these examples to infer…
Expanding Search Space with Diverse Prompting Agents: An Efficient Sampling Approach for LLM Mathematical Reasoning
Gisang Lee, Sangwoo Park, Junyoung Park +5
Large Language Models (LLMs) have exhibited remarkable capabilities in many complex tasks including mathematical reasoning. However, traditional approaches heavily rely on ensuring…