Papers by Kyungmin Lee
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation (2025.coling-main)
Copied to clipboard
| Challenge: | Recent studies show that using large language models (LLMs) is the most reliable method to evaluate QA models, but suffers from limited interpretability, high cost, and environmental harm. |
| Approach: | They propose to use soft exact match (EM) with entity-driven answer set expansion to expand gold answer set to include diverse surface forms. |
| Outcome: | The proposed method outperforms traditional evaluation methods while offering the benefits of high interpretability and reduced environmental harm. |
Fooling the LVLM Judges: Visual Biases in LVLM-Based Evaluation (2025.emnlp-main)
Copied to clipboard
| Challenge: | a recent study has found that large vision–language models are vulnerable to visual biases that inflate scores without altering semantic content. |
| Approach: | They propose a novel meta-evaluation benchmark that exhibits diverse score distributions. |
| Outcome: | The proposed model exhibits vulnerability across all domains, and combines multiple biases amplifies their effects, and pairwise evaluations are similarly susceptible. |
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Vision-Language Models (LVLMs) generate detailed and coherent responses from visual inputs but are prone to generate hallucinations due to an over-reliance on language priors. |
| Approach: | They propose a method that reduces the text context and controls only the image-related POS tokens to maintain text quality by reducing the text contextualization. |
| Outcome: | The proposed method achieves state-of-the-art performance on object hallucination benchmarks and achieves Pareto optimality among the existing methods. |
On Sample-Efficient Code Generation (2023.emnlp-industry)
Copied to clipboard
Hojae Han, Yu Jin Kim, Byoungjip Kim, Youngwon Lee, Kyungjae Lee, Kyungmin Lee, Moontae Lee, Kyunghoon Bae, Seung-won Hwang
| Challenge: | Existing approaches to code generation rely on rejection sampling to generate multiple code snippets then select the best. |
| Approach: | They propose a framework that prioritizes sampling on test problems that models can solve. |
| Outcome: | The proposed framework reduces sampling costs while maintaining comparable code generation performance. |
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)
Copied to clipboard
Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyungmin Kim, Jung-Woo Ha, Sang-Woo Lee
| Challenge: | Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. |
| Approach: | They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality. |
| Outcome: | The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services. |