Papers by Hongsen Huang

2 papers
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling (2026.acl-long)

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Challenge: Existing multimodal reward models are interpretable but slow, while discriminative ones are opaque "black boxes."
Approach: They propose a framework that dynamically decomposes evaluation into granular, interpretable dimensions.
Outcome: The proposed framework outperforms open-source reward models on benchmarks like VL-RewardBench.
Icon2: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation (2025.emnlp-main)

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Challenge: Large Language Models (LLMs) require high quality preference datasets to align with human preferences.
Approach: They propose a framework that leverages inherent regulation of LLMs’ representation space for efficient and tailored preference dataset construction, named Icon2.
Outcome: The proposed framework improves performance on benchmarks like AlpacaEval 2.0 and Arena-Hard while reducing computational costs by up to 48.1%.

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