Papers by Jihyuk Kim
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)
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Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee
| Challenge: | Existing large reasoning models are limited by their closed nature and high API costs and safety issues. |
| Approach: | They propose to build a long CoT dataset with existing short CoT LLMs that are not trained for inference-time scaling. |
| Outcome: | The proposed model achieves quality comparable to—or slightly below—R1 and is able to think longer and provide control over the thought budget to better manage the overthinking problem. |
Retrieval-augmented Video Encoding for Instructional Captioning (2023.findings-acl)
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| Challenge: | Instructional videos provide a detailed multimodal context of each procedure in instruction. key-object degeneracy is a problem for machine systems, causing incorrect captions. |
| Approach: | They propose a retrieval-based framework to augment the model representations in the presence of key-object degeneracy. |
| Outcome: | The proposed framework can be extended over baselines using modalities with key-object degeneracy. |
Relevance-assisted Generation for Robust Zero-shot Retrieval (2023.emnlp-industry)
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| Challenge: | Despite strong in-domain performance, dense retrievers have shown poor generalization to out-of-domain zero-shot tasks where no training queries are available. |
| Approach: | They propose to generate domain-specific pseudo queries for fine-tuning with domain-relevant relevance between PQ and documents. |
| Outcome: | The proposed approach is more robust to domain shifts, validated on BEIR zero-shot tasks. |
Structure-Augmented Keyphrase Generation (2021.emnlp-main)
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| Challenge: | Creating keyphrases that are likely to be words absent from the given document is challenging . |
| Approach: | They propose novel keyphrase generation tasks that augment missing context by adding keyphrases to documents. |
| Outcome: | The proposed keyphrase generation task outperforms the state-of-the-art in two keyphrase tasks. |
FaVe: Factored and Verified Search Rationale for Long-form Answer (2025.findings-acl)
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| Challenge: | Existing solutions for long-form question-answering (LFQA) use chain-of-thought (CoT) with retrieval-augmented generation (RAG). |
| Approach: | They propose to integrate chain-of-thought (CoQ) with retrieval-augmented generation to improve answer comprehensiveness and verifiability. |
| Outcome: | The proposed approach outperforms ChatGPT baselines while maintaining efficiency. |
tRAG: Term-level Retrieval-Augmented Generation for Domain-Adaptive Retrieval (2025.naacl-long)
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| Challenge: | Neural retrieval models suffer when there is a domain shift between training and test data distributions. |
| Approach: | They propose to generate domain-adapted pseudo-queries using large language models (LLMs) to improve term recall of unseen query terms by using term-level Retrieval-Augmented Generation (tRAG). |
| Outcome: | The proposed method significantly improves recall for unseen terms by 10.6% and outperforms LLM and retrieval-augmented generation baselines on overall retrieval performance. |
Collective Relevance Labeling for Passage Retrieval (2022.naacl-main)
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| Challenge: | Existing approaches to improve IR labels are incomplete and require computational overheads. |
| Approach: | They propose to distill knowledge for informed labeling without high computation overheads at evaluation time. |
| Outcome: | The proposed model outperforms state-of-the-art models while distilling the rankings better. |
Adaptive Retrieval for Reasoning (2026.acl-long)
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| Challenge: | Existing reasoning-based rerankers suffer from bounded recall. |
| Approach: | They propose a framework that leverages adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive retrieval tasks by 5.6%pt. |
Pseudo-Relevance for Enhancing Document Representation (2022.emnlp-main)
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| Challenge: | a novel approach to document retrieval can be used to encode documents as vectors . a few query-relevant terms can be pruned out to reduce index overhead . |
| Approach: | They propose to enhance the document representation for the bi-encoder approach in dense document retrieval. |
| Outcome: | The proposed solution reduces latency and memory footprint up to 8- and 3-fold . it is validated on MSMARCO and real-world search query logs . |
Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing prompt optimization methods struggle with disjoint cases in complex tasks. |
| Approach: | They propose a tree-of-prompts structure which expands child prompts from parent prompts . they propose to use a nested if-else structure to address varying similarities and complexities . |
| Outcome: | The proposed tree-of-prompts outperforms PromptAgent and MoP on Gorilla, MATH and subset of BBH benchmarks. |
QuBE: Question-based Belief Enhancement for Agentic LLM Reasoning (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to an explosion of interest in their deployment as agents. |
| Approach: | They propose a method that enhances agents’ focus on task-relevant contexts by constructing a belief state via question answering. |
| Outcome: | The proposed method outperforms established baselines and achieves marked improvements on the BeIR zero-shot retrieval benchmark. |