Papers by Wonbin Kweon
Rectifying Demonstration Shortcut in In-Context Learning (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) can solve tasks with a few demonstrations, but often rely on their pre-trained semantic priors rather than the input-label relationships to proceed with ICL prediction. |
| Approach: | They propose a demonstration-aware calibration method to improve LLMs' ability to learn new input-label relationships from demonstrations. |
| Outcome: | The proposed method improves the original ICL task and the task learning setting, and the results are generalized across three LLM families. |
Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) demonstrate remarkable reasoning capabilities comparable to human cognitive abilities. |
| Approach: | They propose a sentence-level rationale reduction framework that leverages likelihood-based criteria to selectively remove redundant reasoning sentences while preserving reasoning capabilities. |
| Outcome: | The proposed framework improves performance by 7.71% while reducing token generation by 19.87% compared to models trained with complete reasoning paths. |
Topic Coverage-based Demonstration Retrieval for In-Context Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior methods to retrieve demonstrations based on embedding similarity or generation probability, resulting in irrelevant or redundant examples. |
| Approach: | They propose a topic coverage-based retrieval framework that selects demonstrations to comprehensively cover topic-level knowledge relevant to both the test input and the model. |
| Outcome: | The proposed framework covers all the necessary knowledge for the test input and the model. |