5 papers
CHART: A Harness-Rotation Curriculum for Harness-Robust Search Agents
Xinlu Zhang, Ying-Chun Lin, Zhihan Zhang +2
Search agents are usually trained under a single harness. But once an agent is deployed in a real application, its harness is frequently updated (e.g., a rewritten system prompt) t…
Rethinking Node Representation Interpretation through Relation Coherence
Ying-Chun Lin, Jennifer Neville, Cassiano Becker +3
Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on expla…
Improving Node Representation by Boosting Target-Aware Contrastive Loss
Ying-Chun Lin, Jennifer Neville
Graphs model complex relationships between entities, with nodes and edges capturing intricate connections. Node representation learning involves transforming nodes into low-dimensi…
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
Taiwei Shi, Zhuoer Wang, Longqi Yang +12
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on hum…
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Ying-Chun Lin, Jennifer Neville, Jack W. Stokes +14
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their sati…