Papers by Jimyeong Kim
Soft Head Selection for Injecting ICL-Derived Task Embeddings (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are commonly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT) or in-context learning (ICL). |
| Approach: | They propose a gradient-based method that derives task-specific embeddings from activations using few-shot prompts and injects them during inference. |
| Outcome: | The proposed method outperforms existing methods on open-ended generation, reasoning, and natural language understanding tasks while using fewer trainable parameters. |
TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models (2026.acl-long)
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| Challenge: | Prior mitigation approaches that optimize entire responses operate at the level of entire responses and can lead to unintended degradation of general model capabilities. |
| Approach: | They propose a fine-tuning framework to mitigate erroneous outputs by localizing and updating the policy at a granular level. |
| Outcome: | The proposed framework outperforms baselines on multiple multilingual LLMs across diverse languages while preserving task accuracy. |