Papers by Daniel Rim
To Chat or Task: a Multi-turn Dialogue Generation Framework for Task-Oriented Dialogue Systems (2025.acl-industry)
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| Challenge: | Large language models (LLMs) are designed to handle complex task requests, but lack of specific datasets for training and evaluation of such systems . |
| Approach: | They propose a framework to generate a dataset for in-vehicle speech recognition systems . they train an in-car context sensor that correctly identifies the functional intent of the driver . |
| Outcome: | The proposed framework outperforms baseline models across experimental settings. |
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models (2024.findings-acl)
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| Challenge: | Language models (LMs) demonstrate exceptional capabilities on tasks, but are vulnerable to extraction attacks. |
| Approach: | They propose Privacy Protection via Optimal Parameters (POP) which induces the model to forget about some of its training data. |
| Outcome: | The proposed method outperforms the state-of-the-art in retaining LM performance on 9 classification and 4 dialogue benchmarks. |
LLM ContextBridge: A Hybrid Approach for Intent and Dialogue Understanding in IVSR (2025.coling-industry)
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| Challenge: | In-vehicle speech recognition systems struggle with interpreting user intent accurately due to limitations in contextual understanding and ambiguity resolution. |
| Approach: | They propose a hybrid architecture that integrates Pretrained Language Model-based intent classification with Large Language Models to enhance both command recognition and dialogue management. |
| Outcome: | The proposed architecture improves recognition accuracy and user experience in multi-turn dialogues. |
EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA (2026.acl-long)
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Myunghoon Kang, Dahyun Jung, Suhyune Son, Seonmin Koo, Changwoo Chun, Daniel Rim, Haeyoung Kwon, Yuna Hur, Heuiseok Lim
| Challenge: | Table-based question answering (table QA) is a powerful tool for analyzing large language models. |
| Approach: | They propose to use noisy multi-table sets to generate sub-tables for table-based question answering. |
| Outcome: | The proposed framework efficiently filters out irrelevant information while incorporating pertinent table values. |
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport (2025.acl-long)
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| Challenge: | Instruction-following large language models (LLMs) inadvertently disclose private, sensitive information to their users, underscoring the need for machine unlearning techniques to remove selective information from the models. |
| Approach: | They propose an optimal transport-based unlearning method that utilizes the Wasserstein distance from the model’s initial parameters to achieve more effective and fine-grained unlearning. |
| Outcome: | The proposed method surpasses existing methods and establishes a new standard for secure and adaptable LLMs that can accommodate user data removal requests without the need for full retraining. |
DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation (2023.findings-acl)
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| Challenge: | Recent studies suggest that neural classifiers make overly confident predictions for examples from unseen distributions. |
| Approach: | They propose a new evaluation metric, DENSITY, which measures how likely a response would appear in the distribution of human conversations. |
| Outcome: | The proposed metric measures how likely a response would appear in the distribution of human conversations. |