1 citations · 2 across the 14 of their papers we have counts for
15 papers
Learning Latent Reasoning Traces for Scalar Reward Models End-to-End
Sanwoo Lee, Clive Bai, Hsiu-Yuan Huang +3
Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs enable efficient and probabilis…
RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data
Hsiu-Yuan Huang, Weijie Liu, Chenming Tang +5
The proliferation of Reinforcement Learning from Verifiable Rewards (RLVR) datasets has exacerbated provenance collapse due to unclear lineage among existing datasets. To bridge th…
Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model
Chenming Tang, Hsiu-Yuan Huang, Weijie Liu +3
Reinforcement learning has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments. Existing approaches either rely o…
CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
Junzhao Zhang, Hsiu-Yuan Huang, Chenming Tang +2
Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limited cultural coverage, which h…
Think Outside the Policy: In-Context Steered Policy Optimization
Hsiu-Yuan Huang, Chenming Tang, Weijie Liu +3
Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods, such as Group Relative Policy Optimization (GRPO), have achieved remarkable progress in improving the reason…
Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error
Chenming Tang, Hsiu-Yuan Huang, Weijie Liu +3
Reinforcement learning with verifiable rewards (RLVR) has significantly boosted the reasoning capability of language models (LMs). However, existing RLVR approaches train LMs based…
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing
Zichen Wu, Hsiu-Yuan Huang, Yunfang Wu
Multimodal Large Language Models (MLLMs) have shown substantial capabilities in integrating visual and textual information, yet frequently rely on spurious correlations, underminin…
CTR-Guided Generative Query Suggestion in Conversational Search
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. W…
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations
Wang Cai, Hsiu-Yuan Huang, Zhixiang Wang +1
In-Context derived Vector (ICV) methods extract task-relevant representations from large language models (LLMs) and reinject them during inference, achieving comparable performance…
From Prompting to Alignment: A Generative Framework for Query Recommendation
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
In modern search systems, search engines often suggest relevant queries to users through various panels or components, helping refine their information needs. Traditionally, these…
A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice
Hsiu-Yuan Huang, Yutong Yang, Zhaoxi Zhang +2
As large language models (LLMs) continue to evolve, understanding and quantifying the uncertainty in their predictions is critical for enhancing application credibility. However, t…
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection
Hsiu-Yuan Huang, Zichen Wu, Yutong Yang +2
Large Language Models (LLMs) have shown remarkable performance across a wide range of downstream tasks. However, it is challenging for users to discern whether the responses of LLM…
Assessing the Performance of Chinese Open Source Large Language Models in Information Extraction Tasks
Yida Cai, Hao Sun, Hsiu-Yuan Huang +1
Information Extraction (IE) plays a crucial role in Natural Language Processing (NLP) by extracting structured information from unstructured text, thereby facilitating seamless int…
FPT: Feature Prompt Tuning for Few-shot Readability Assessment
Ziyang Wang, Sanwoo Lee, Hsiu-Yuan Huang +1
Prompt-based methods have achieved promising results in most few-shot text classification tasks. However, for readability assessment tasks, traditional prompt methods lackcrucial l…
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding
Zichen Wu, Hsiu-Yuan Huang, Fanyi Qu +1
Deep multimodal semantic understanding that goes beyond the mere superficial content relation mining has received increasing attention in the realm of artificial intelligence. The…