publications

2024

1. Studying the Effects of Collaboration in Interactive Theme Discovery Systems

   Alvin Po-Chun Chen, Dananjay Srinivas, Alexandra Barry, and 2 more authors

   2024

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   NLP-assisted solutions have gained considerable traction to support qualitative data analysis. However, there does not exist a unified evaluation framework that can account for the many different settings in which qualitative researchers may employ them. In this paper, we take a first step in this direction by proposing an evaluation framework to study the way in which different tools may result in different outcomes depending on the collaboration strategy employed. Specifically, we study the impact of synchronous vs. asynchronous collaboration using two different NLP-assisted qualitative research tools and present a comprehensive analysis of significant differences in the consistency, cohesiveness, and correctness of their outputs.

   @misc{chen2024studyingeffectscollaborationinteractive,
     title = {Studying the Effects of Collaboration in Interactive Theme Discovery Systems},
     author = {Chen, Alvin Po-Chun and Srinivas, Dananjay and Barry, Alexandra and Seniw, Maksim and Pacheco, Maria Leonor},
     year = {2024},
     eprint = {2408.09030},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
   }

2. Mothman at SemEval-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization

   Alvin Po-Chun Chen, Ray Groshan, and Sean Von Bayern

   In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), Jun 2024

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   Extensive research exists on the performance of large language models on logic-based tasks, whereas relatively little has been done on their ability to generate creative solutions on lateral thinking tasks. The BrainTeaser shared task tests lateral thinking and uses adversarial datasets to prevent memorization, resulting in poor performance for out-of-the-box models. We propose a system for iterative, chain-of-thought prompt engineering which optimizes prompts using human evaluation. Using this shared task, we demonstrate our system’s ability to significantly improve model performance by optimizing prompts and evaluate the input dataset.

   @inproceedings{chen-etal-2024-mothman,
     title = {Mothman at {S}em{E}val-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization},
     author = {Chen, Alvin Po-Chun and Groshan, Ray and Von Bayern, Sean},
     editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Tayyar Madabushi, Harish and Da San Martino, Giovanni and Rosenthal, Sara and Ros{\'a}, Aiala},
     booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
     month = jun,
     year = {2024},
     address = {Mexico City, Mexico},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.semeval-1.263},
     doi = {10.18653/v1/2024.semeval-1.263},
     pages = {1876--1888},
   }