2 citations · 4 across the 12 of their papers we have counts for
13 papers
A Unified Framework to Elicit Structured Feedback for Interpretable Multi-Trait Essay Scoring
Shihang Yang, Sanwoo Lee, Ningning Zhao +1
Multi-trait Automated Essay Scoring (AES) requires rubric-grounded reasoning across interdependent traits, rather than isolated score prediction. Existing feedback-enhanced methods…
Trait-Aware Policy Optimization for Autoregressive Multi-Trait Essay Scoring
Zhengyang Wang, Sanwoo Lee, Jiaxin Wang +3
Multi-trait essay scoring aims to provide fine-grained evaluation of writing quality across multiple dimensions. However, how to effectively post-train autoregressive scoring model…
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…
Composable Cross-prompt Essay Scoring by Merging Models
Sanwoo Lee, Kun Liang, Yunfang Wu
Recent advances in cross-prompt automated essay scoring (AES) typically train models jointly on all source prompts, often requiring additional access to unlabeled target prompt ess…
Dynamic Fisher-weighted Model Merging via Bayesian Optimization
Sanwoo Lee, Jiahao Liu, Qifan Wang +3
The fine-tuning of pre-trained language models has resulted in the widespread availability of task-specific models. Model merging offers an efficient way to create multi-task model…
Rank-Then-Score: Enhancing Large Language Models for Automated Essay Scoring
Yida Cai, Kun Liang, Sanwoo Lee +2
In recent years, large language models (LLMs) achieve remarkable success across a variety of tasks. However, their potential in the domain of Automated Essay Scoring (AES) remains…
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…
Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring
Sanwoo Lee, Yida Cai, Desong Meng +2
Advances in automated essay scoring (AES) have traditionally relied on labeled essays, requiring tremendous cost and expertise for their acquisition. Recently, large language model…