Papers by Jeonghoon Kim
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods rely on linear sequential operations to solve First-Order Logic queries. |
| Approach: | They propose a model-agnostic approach that fully integrates the context of the query graph. |
| Outcome: | The proposed method improves performance on two datasets by 19.5%. |
Enhancing Hallucination Detection via Future Context (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. |
| Approach: | They propose a framework for detection of hallucinations in black-box generators by analyzing future contexts. |
| Outcome: | The proposed framework improves on existing methods and demonstrates that it is feasible to integrate it with other models. |
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing iterative refinement strategies that generate solutions in a single forward pass often hit a performance ceiling on complex algorithmic tasks. |
| Approach: | They propose a reinforcement learning framework that internalizes the structured reasoning trajectory directly into the model’s weights. |
| Outcome: | The proposed framework achieves 94.51% (87.20%) on HumanEval, 81.80% (78.57%) on MBPP, 35.00% on BigCodeBench, 52.21% on LiveCodeBech, and 37.34% on CodeForces in a single-attempt setting. |
Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models (2026.acl-industry)
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| Challenge: | Existing high-quality conversational data is limited for full-duplex models . overlapping and backchanneling are a challenge for most systems . |
| Approach: | They propose a robust and scalable open-source data processing pipeline for full-duplex models. |
| Outcome: | The proposed pipeline can listen and speak simultaneously, supporting more fluid and human-like interaction. |
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models (2022.findings-emnlp)
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Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo, Jin-Hwa Kim, Baeseong Park, Byeongwook Kim, Jung-Woo Ha, Nako Sung, Dongsoo Lee
| Challenge: | Existing approaches to improve inference efficiency by accelerating model fine-tuning have not been thoroughly explored. |
| Approach: | They propose to combine parameter-efficient adaptation and model compression to accelerate model . they propose to freeze binary parameters and scale scaling factors for target tasks . |
| Outcome: | The proposed algorithm achieves >10x compression ratio under 4-bit quantization and >1,000x reduction in trainable parameters. |
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices (2025.naacl-long)
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| Challenge: | Existing methods for quantizing weights and activations of large language models suffer from non-negligible accuracy drops, especially on massive multitask language understanding. |
| Approach: | They propose a weight-activation quantization method that reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices. |
| Outcome: | The proposed method reduces the complexity of the weight-activation quantization techniques while achieving high throughput and reducing inference costs. |