Papers by Joo-Kyung Kim
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding (N18-3)
Copied to clipboard
| Challenge: | Existing approaches to classify a given utterance into domains are costly and time-consuming. |
| Approach: | They propose a shortlisting-reranking neural model for large-scale domain classification for IPDAs . they use extensive experiments on 1,500 IPDA domains to test their effectiveness . |
| Outcome: | The proposed model is tested on 1,500 IPDA domains. |
Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge graph–grounded dialog generation fail to leverage the rich knowledge of pretrained language models. |
| Approach: | They propose a method for dialog generation that integrates dialog history with a knowledge graph. |
| Outcome: | The proposed method achieves state-of-the-art in knowledge graph–grounded dialog generation on OpenDialKG and KOMODIS datasets. |
SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language model (LLM) based search agents are more likely to produce harmful outputs than base models. |
| Approach: | They propose a query-level shaping term that rewards safe queries and penalizes unsafe ones. |
| Outcome: | The proposed approach reduces harmfulness by over 70% across three red-teaming datasets while producing safe, helpful responses. |
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent Long-Context Language Models (LCLMs) do not capture how evidence should be connected . a new framework that integrates thought templates into LCLM frameworks is proving useful . |
| Approach: | They propose a framework that iteratively refines reusable reasoning patterns derived from prior problem solving to improve their templates. |
| Outcome: | The proposed framework outperforms baselines on knowledge-intensive multi-hop reasoning benchmarks and practical scenarios without retrieval. |
Supervised Domain Enablement Attention for Personalized Domain Classification (D18-1)
Copied to clipboard
| Challenge: | Recent IPDAs cover more than several thousands of diverse domains including Alexa Skills, Google Actions, and Cortana Skills. |
| Approach: | They propose a supervised enablement attention mechanism that utilizes sigmoid activation for the attention weighting and self-distillation to leverage the attention information of other enabled domains. |
| Outcome: | The proposed approach improves domain classification performance on real-world domains. |
MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on prompting and developing workflows with frozen LLMs. |
| Approach: | They propose a multi-agentic framework for collaborative LLMs with reinforcement learning that leverages multi-gendered frameworks to enhance collaboration. |
| Outcome: | The proposed model improves collaboration performance across multiple datasets with generalization to unseen domains compared to existing models. |
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have focused on assessing the model’s overall accuracy without evaluating it on different reasoning cases. |
| Approach: | They propose a novel idea to identify and improve multi-modal multi-hop reasoning in VQA by using two new language prompts to find a reasoning path to reach its answer. |
| Outcome: | The proposed model improves multi-modal multi-hop reasoning in visual question answering (VQA) it finds that the proposed model is easy to answer, simply demanding “single-hop” reasoning, whereas only a few questions require “multi-hop.” |