Papers by Claire Jin
Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs (2024.findings-acl)
Copied to clipboard
| Challenge: | Advancements in large language models (LLMs) are revolutionizing interactive game design, but they may exhibit flaws such as hallucinations, forgetfulness, or misinterpretation of prompts. |
| Approach: | They propose a method for automatically identifying LLM bugs from player game logs . their method surpasses unstructured bug-catching methods and fills the gap . |
| Outcome: | The proposed method surpasses unstructured bug-catching methods and fills the gap in detection of logical and design flaws. |
AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models are increasingly used to automate data analysis, but data science tasks often admit multiple statistically valid solutions. |
| Approach: | They propose a framework to evaluate LLM-generated code and assess its reproducibility . they introduce two reproducibility-enhancing prompting strategies and benchmark them against standard prompting . |
| Outcome: | The proposed framework improves reproducibility of large language models . it provides a foundation for transparent, reliable, and efficient human–AI collaboration in data science. |