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Youfeng Liu

2 accepted papers

2025

AGENTIF: Benchmarking Large Language Models Instruction Following Ability in Agentic Scenarios

NeurIPS 2025spotlight

Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications. Growing research efforts aim to develop LLM-based agents to address practical demands, introducing a new challenge: agentic scenarios often involve lengthy instructions with complex constraints,…

Cited by 0SourcecodeScholar
2025

LegalAgentBench: Evaluating LLM Agents in Legal Domain

ACL 2025long

With the increasing intelligence and autonomy of LLM Agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain benchmarks are unable to fully capture the complexity and subtle nuances inherent in real-world judicial cognition and de…