Papers by Chao-Chun Hsu
CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support (2024.findings-acl)
Copied to clipboard
Chao-Chun Hsu, Erin Bransom, Jenna Sparks, Bailey Kuehl, Chenhao Tan, David Wadden, Lucy Wang, Aakanksha Naik
| Challenge: | Literature review requires researchers to synthesize a large amount of information. |
| Approach: | They propose to use LLMs to generate hierarchical organizations from a set of studies . they use a human-in-the-loop process to correct errors in LLM-generated hierarchies . |
| Outcome: | The proposed model improves assignment of studies to categories by 12.6 F1 points. |
CodeScout: Contextual Problem Statement Enhancement for Software Agents (2026.findings-acl)
Copied to clipboard
Manan Suri, Xiangci Li, Mehdi Shojaie, Songyang Han, Chao-Chun Hsu, Shweta Garg, Aniket Anand Deshmukh, Varun Kumar
| Challenge: | Current AI-powered code assistance tools struggle with ambiguous problem statements . failures on such ambiguously requests are highly correlated with longer trajectories . |
| Approach: | They propose a contextual query refinement approach that transforms ambiguous user requests into comprehensive, actionable problem statements through lightweight pre-exploration of the target codebase. |
| Outcome: | Empirical results show that CodeScout improves resolution rates with 27 additional issues resolved compared to baseline method. |
CODESTRUCT: Code Agents over Structured Action Spaces (2026.acl-long)
Copied to clipboard
| Challenge: | LLM-based code agents treat repositories as unstructured text, fail to produce valid patches . authors propose a structure-aware interface that exposes a codebase as a programmable action space . |
| Approach: | They propose to reframe the codebase as a structured action space where agents operate on named AST entities rather than text spans. |
| Outcome: | Evaluated on six LLMs, the proposed framework improves Pass@1 accuracy by 1.2-5.0% and reduces token consumption by 12-38%. |
EmotionLines: An Emotion Corpus of Multi-Party Conversations (L18-1)
Copied to clipboard
| Challenge: | Emotion is a critical characteristic to distinguish people from machines. |
| Approach: | They propose a dataset with emotions labeling on all utterances in each dialogue . they use Friends TV scripts and Facebook messenger dialogues to collect the data . |
| Outcome: | The proposed dataset is the first with emotions labeling on all utterances in each dialogue based on their textual content. |
Answer Generation for Retrieval-based Question Answering Systems (2021.findings-acl)
Copied to clipboard
| Challenge: | Question Answering systems are a core component of many commercial applications . answer sentence selection (AS2) models are trained to select the best answer sentence . |
| Approach: | They propose to train a sequence to sequence transformer model to generate an answer from a set of candidates. |
| Outcome: | The proposed model improves accuracy by 32 points over the state-of-the-art model on English AS2 datasets. |
Decision-Focused Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing summarization methods define relevance based on textual information alone without incorporating insights about a particular decision. |
| Approach: | They propose a method that summarizes relevant information for a decision using full text . they then build a model that makes the decision based on the full text while accounting for textual non-redundancy. |
| Outcome: | The proposed method outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness. |
Characterizing the Value of Information in Medical Notes (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Obtaining and analyzing information is critical for the diagnosis, prognosis, treatment, and prevention of disease. |
| Approach: | They propose a probing framework to select parts of notes that enable more accurate predictions than using all notes. |
| Outcome: | The proposed framework achieves better predictive performance with only 6.8% of all tokens for readmission prediction. |