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cs.CL2026
CodeDelegator: Mitigating Context Pollution via Role Separation in Code-as-Action Agents
Tianxiang Fei, Cheng Chen, Yue Pan +2
Recent advances in large language models (LLMs) allow agents to represent actions as executable code, offering greater expressivity than traditional tool-calling. However, real-wor…
cs.CL2024
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
Mingyang Song, Mao Zheng, Xuan Luo +1
Utilizing Large Language Models (LLMs) as evaluators to assess the performance of LLMs has garnered attention. However, this kind of evaluation approach is affected by potential bi…