controlled decoding 1efficiency 1large language models 1preference optimization 1sequence labeling 1
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cs.CL2026
DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models
Yilei Wang, Jiaxin Gan, Kexuan Zhang +3
The paper introduces DIRECT, a framework that improves large language model‑based sequence labeling by applying Direct Preference Optimization for better task alignment and a contr…
cs.CL2025
Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations
Peichao Lai, Jiaxin Gan, Feiyang Ye +2
Sequence labeling remains a significant challenge in low-resource, domain-specific scenarios, particularly for character-dense languages like Chinese. Existing methods primarily fo…
cs.CL2025
SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models
Peichao Lai, Kexuan Zhang, Yi Lin +8
Subjective Answer Grading (SAG) plays a crucial role in education, standardized testing, and automated assessment systems, particularly for evaluating short-form responses in Short…