Papers by Kai Ong
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)
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| Challenge: | Existing methods for metaphor interpretation are slow due to lack of annotated datasets and effective pre-trained language models. |
| Approach: | They propose a large annotated dataset and a PLM for the metaphor interpretation task. |
| Outcome: | The proposed method improves on metaphor identification and interpretation with comparable baselines on the new dataset. |
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
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| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
| Approach: | They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations. |
| Outcome: | The proposed framework facilitates better response generation via human-like persona refinement. |
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code (2024.emnlp-main)
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Hyungjoo Chae, Taeyoon Kwon, Seungjun Moon, Yongho Song, Dongjin Kang, Kai Ong, Beong-woo Kwak, Seonghyeon Bae, Seung-won Hwang, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have made great progress in code generation, however, they still produce errors. |
| Approach: | They propose a RL environment that provides feedback on code editing by analyzing the performance of the revised code in unit tests. |
| Outcome: | The proposed model outperforms baselines in enhancing open-source code LLMs’ code editing, making them comparable with closed-source LLM. |
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)
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Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo
| Challenge: | a human-like chatbot requires commonsense reasoning to comprehend and respond to information . however, identifying and aggregating key evidence within a single hop is a challenge . a knowledge distillation framework is proposed that leverages LLMs as unreliable teachers . |
| Approach: | They propose a framework that leverages large language models as unreliable teachers to facilitate multi-hop reasoning over a dialogue context. |
| Outcome: | The proposed framework leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. |
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models (2024.emnlp-main)
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Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Ong, Beong-woo Kwak, Moohyeon Kim, Sunghwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo
| Challenge: | Prior work has used LLMs to generate programming language and applied external compilers for such tasks. |
| Approach: | They propose a framework that expresses task-level logic with pseudocode and tailors it to each instance and simulates execution of it. |
| Outcome: | The proposed framework outperforms baselines in diverse reasoning tasks. |
InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care (2025.emnlp-demos)
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| Challenge: | Existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triages rates. |
| Approach: | They propose to use an AI-driven multilingual TT system to provide decision support for triage. |
| Outcome: | The proposed system achieves word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction. |