Papers by Je Won Yeom
“Well, Keep Thinking”: Enhancing LLM Reasoning with Adaptive Injection Decoding (2025.findings-acl)
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| Challenge: | Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a decoding strategy that nudges LLMs to continue reasoning, thereby preventing immature reasoning processes. |
| Outcome: | The proposed method significantly improves LLM reasoning capabilities on diverse reasoning benchmarks. |
HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models (2024.lrec-main)
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Guijin Son, Hanwool Lee, Suwan Kim, Huiseo Kim, Jae cheol Lee, Je Won Yeom, Jihyu Jung, Jung woo Kim, Songseong Kim
| Challenge: | Existing evaluation tools rely on translations of English datasets or translation-specific benchmarks such as WMT 21 to assess large language models. |
| Approach: | They propose a dataset curated to challenge models lacking Korean cultural and contextual depth. |
| Outcome: | The HAE-RAE Bench challenges models lacking Korean cultural and contextual depth by highlighting their aptitude for recalling Korean-specific knowledge and cultural contexts. |