Papers by Yiwei Lv

2 papers
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)

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Challenge: Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios.
Approach: They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance.
Outcome: The proposed framework improves model capabilities across all domains and scales.
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)

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Challenge: Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency.
Approach: They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities .
Outcome: The proposed framework outperforms the sequence tagging baseline on three benchmark datasets.

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