Papers by Doyoung Kim
Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that large language models can solve complex reasoning tasks with Chain-of-Thought Prompting. |
| Approach: | They propose a training method where the LLM is tasked to explore the first wrong step within the rationale and use such signals as fine-grained rewards for further improvement. |
| Outcome: | The proposed model improves on the GSM8K and MATH test sets by 11.57% and 2.89% on average compared to supervised fine-tuning (SFT). |
Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt (2023.findings-emnlp)
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| Challenge: | Recent studies show that adding a instruction tuning stage to training large language models can improve zero-shot task generalization. |
| Approach: | They propose a method that retrieves promptspecific source prompt embeddings from training instances . they train soft prompt embeds for each prompt through prompt tuning and store the samples . |
| Outcome: | The proposed method outperforms hard prompts on unseen tasks by 2.39% points and outperformed 10 out of 11 datasets. |
How Well Do Large Language Models Truly Ground? (2024.naacl-long)
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| Challenge: | Existing research defines “grounding” as having the correct answer, which does not ensure the reliability of the entire response. |
| Approach: | They propose a stricter definition of grounding: fully utilizes the necessary knowledge from the provided context and stays within the limits of that knowledge. |
| Outcome: | The proposed model can be ground on external contexts and maintain its correct answer. |
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning (2023.emnlp-main)
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| Challenge: | Language models with less than 100B parameters perform poorly on chain-of-thought reasoning . we aim to equip smaller LMs with the step-by-step reasoning capability . |
| Approach: | They propose to equip smaller LMs with the step-by-step reasoning capability by tuning with CoT rationales. |
| Outcome: | The proposed dataset outperforms large LMs on 4 domain-specific tasks even with demonstrations . |
QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval (2026.acl-long)
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| Challenge: | Existing approaches to grounding large language models rely on static weights and a static retrieval component. |
| Approach: | They propose a dual-perspective adaptive retrieval framework that adapts along two perspectives: retriever type (sparse vs. dense) and query format (original v. expanded). |
| Outcome: | The proposed framework adapts along two perspectives: retriever type (sparse vs. dense) and query format (original v. expanded). |
Semiparametric Token-Sequence Co-Supervision (2024.acl-long)
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| Challenge: | Using semiparametric token-sequence co-supervision, language models are trained using a finite parametric vocabulary space. |
| Approach: | They propose a semiparametric token-sequence co-supervision training method that leverages supervision from two different supervisions. |
| Outcome: | The proposed method outperforms models trained via each supervision independently and shows that it encourages a broader generalization capability across the model. |