Papers by Hsiu-Yuan Huang
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing (2025.findings-emnlp)
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| Challenge: | Multimodal Large Language Models (MLLMs) often rely on spurious correlations, undermining their robustness and generalization. |
| Approach: | They propose a causal mediation-based debiasing framework to address correlation bias in MLLMs . they distinguish core semantics from spurious textual and visual contexts using counterfactual examples . |
| Outcome: | The proposed framework surpasses existing state-of-the-art models on sarcasm detection and sentiment analysis tasks. |
Think Outside the Policy: In-Context Steered Policy Optimization (2026.findings-acl)
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| Challenge: | Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods exhibit limited exploration due to reliance on on-policy rollouts which are limited to the current policy’s distribution, resulting in narrow trajectory diversity. |
| Approach: | They propose a framework that leverages the in-context learning capability of Large Reasoning Models to provide expert guidance using existing datasets. |
| Outcome: | The proposed framework improves RLVR performance and training stability on mathematical reasoning benchmarks. |
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024.lrec-main)
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| Challenge: | Multimodal semantic understanding is crucial for developing machines capable of interpreting complex interplay of text and visual information. |
| Approach: | They propose a multi-modal soft prompt framework that integrates three experts of soft prompts . they propose sarcasm detection and sentiment analysis tasks that are critical for few-shot learning . |
| Outcome: | The proposed model outperforms the 8.2B model InstructBLIP with 2% parameters . it significantly outperformed other prompt methods on VLMs or task-specific methods . |
Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error (2026.acl-long)
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| Challenge: | Existing approaches to RLVR train LMs based on their own on-policy responses and are constrained by the initial capability of LM. |
| Approach: | They propose an approach that hints LMs with their self-made mistakes without external guidance. |
| Outcome: | The proposed approach outperforms the normal group relative policy optimization and requires no external guidance. |
FPT: Feature Prompt Tuning for Few-shot Readability Assessment (2024.naacl-long)
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| Challenge: | Prompt-based methods lack crucial linguistic knowledge for readability assessment tasks such as word length, sentence length, and usage of different difficulty-level words. |
| Approach: | They propose a new prompt-based tuning framework that incorporates linguistic knowledge and a loss function to calibrate the similarity ranking order between categories. |
| Outcome: | The proposed framework outperforms the large language model gpt-3.5-turbo-16k in most cases. |
CTR-Guided Generative Query Suggestion in Conversational Search (2025.emnlp-industry)
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Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin
| Challenge: | Generating effective query suggestions requires aligning model outputs with user click preferences. |
| Approach: | They propose a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement. |
| Outcome: | The proposed framework outperforms strong baselines in CTR, relevance, diversity and diversity. |
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations (2025.emnlp-main)
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| Challenge: | Existing methods for In-Context Learning (ICL) are sensitive to ICL-specific factors and rely on heuristic-based injection positions. |
| Approach: | They propose a method that extracts task-relevant representations from large language models and reinjects them during inference. |
| Outcome: | The proposed method outperforms few-shot In-Context Learning (ICL) and LoRA methods without repeated demonstration processing. |