Trajectory Planning On Toolbench
평가 지표
Win rate
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Win rate | Paper Title | Repository |
---|---|---|---|
Attention Bucket | 71.5 | Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use | |
GPT4-TOPGUN | 86.54 | SwissNYF: Tool Grounded LLM Agents for Black Box Setting | |
GPT4- DFSDT | 70.4 | ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs |
0 of 3 row(s) selected.