Papers by Minbeom Kim
AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence (2025.naacl-long)
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| Challenge: | AdvisorQA aims to improve LLMs’ capability to offer advice for deeply subjective concerns, utilizing the LifeProTips Reddit forum. |
| Approach: | They propose a dataset to train LLMs' ability to offer advice for deeply subjective concerns, utilizing the LifeProTips Reddit forum. |
| Outcome: | The proposed model improves usefulness through automatic metric, GPT-4 and human evaluations, and expands independent evaluation axis to include harmlessness. |
Drift: Decoding-time Personalized Alignments with Implicit User Preferences (2025.findings-emnlp)
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| Challenge: | Drift personalizes large language models at decoding time with implicit user preferences . Unlike traditional Reinforcement Learning from Human Feedback, Drift operates in a training-free manner . |
| Approach: | They propose a framework that personalizes large language models at decoding time with implicit user preferences. |
| Outcome: | The proposed framework personalizes large language models at decoding time with implicit user preferences. |
Critic-Guided Decoding for Controlled Text Generation (2023.findings-acl)
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| Challenge: | Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. |
| Approach: | They propose a method that combines reinforcement learning and weighted decoding to train a critic from reward models. |
| Outcome: | The proposed method generates more coherent and well-controlled texts than previous methods on three controlled generation tasks, topic control, sentiment control, and detoxification. |
A Character-Centric Creative Story Generation via Imagination (2025.findings-acl)
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| Challenge: | Existing narrative generation models lack diversity and character depth, but they are inadequate for human creativity. |
| Approach: | They propose a novel story generation framework called CCI that leverages images to create stories that are diverse and creative in their themes and richer in content. |
| Outcome: | The proposed framework significantly improves various aspects of the stories’ creativity. |
VLind-Bench: Measuring Language Priors in Large Vision-Language Models (2025.findings-naacl)
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| Challenge: | Large Vision-Language Models suffer from a problem known as language prior . such language priors can lead to undesirable biases and hallucinations when dealing with images that are out of distribution. |
| Approach: | They propose a benchmark to measure the language priors of Large Vision-Language Models. |
| Outcome: | The proposed benchmark is the first specifically designed to measure the language priors, or blindness, of LVLMs. |
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding (2025.findings-naacl)
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| Challenge: | Large Vision-Language Models (LVLMs) generate detailed and coherent responses from visual inputs but are prone to generate hallucinations due to an over-reliance on language priors. |
| Approach: | They propose a method that reduces the text context and controls only the image-related POS tokens to maintain text quality by reducing the text contextualization. |
| Outcome: | The proposed method achieves state-of-the-art performance on object hallucination benchmarks and achieves Pareto optimality among the existing methods. |
LifeTox: Unveiling Implicit Toxicity in Life Advice (2024.naacl-short)
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| Challenge: | Existing safety benchmarks and red teaming prompts fail to capture implicit toxicity in complex real-life advice-seeking scenarios. |
| Approach: | They propose a dataset designed for identifying implicit toxicity within advice-seeking scenarios. |
| Outcome: | The proposed dataset matches or surpasses the zero-shot performance of large language models in toxicity classification tasks. |
ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection (2025.emnlp-main)
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| Challenge: | Recent advances in LLMs have significantly enhanced their reasoning capabilities, enabling LLM-based agents to perform complex multi-step decision making beyond static problem solving. |
| Approach: | They propose a novel reasoning backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent’s state relative to its goal. |
| Outcome: | The proposed model outperforms ReAct by 27.7% on average, achieving a 93.3% success rate in ALFWorld. |