Papers by Chengjun Liu

2 papers
OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)

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Challenge: coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored .
Approach: They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding.
Outcome: The proposed benchmark aims to accelerate the development of more scaffold-aware agents.
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)

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Challenge: Semantic role labeling (SRL) is a crucial task of natural language processing (NLP).
Approach: They propose to equip LLMs with retrieval-augmented generation and self-correction mechanisms to enable SRL to perform better in Chinese and English.
Outcome: The proposed method achieves state-of-the-art in Chinese and English on three widely-used benchmarks.

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