Papers by Keonwoo Kim
DRAFT: Dense Retrieval Augmented Few-shot Topic classifier Framework (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for few-shot topic classification are limited due to the volume of information pouring in from the Internet . a new framework is proposed to train a classifier for few shot topics . |
| Approach: | They propose a framework to train a classifier for few-shot topic classification using a customized dataset and a dense retriever model. |
| Outcome: | The proposed framework shows superior performance on few-shot topic classification tasks compared to baselines that use in-context learning . |
An Empirical Study of Group Conformity in Multi-Agent Systems (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. |
| Approach: | They analyze how LLM agents shape public opinion through debates on five contentious topics by simulating over 2,500 debates. |
| Outcome: | The proposed models show that LLM agents adopt specific stances over time and align with numerically dominant groups or more intelligent agents, exerting a greater influence. |
DEBATE: Devil’s Advocate-Based Assessment and Text Evaluation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for evaluating the quality of machine-generated texts have a relatively low correlation with human performance. |
| Approach: | They propose an NLG evaluation framework based on multi-agent scoring system augmented with a concept of Devil’s Advocate. |
| Outcome: | The proposed evaluation framework outperforms the previous state-of-the-art methods in two meta-evaluation benchmarks in NLG evaluation, SummEval and TopicalChat. |