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cs.SE2024
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction
Hongru Wang, Rui Wang, Boyang Xue +5
Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Prev…
cs.CL2024
SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration
Heming Xia, Yongqi Li, Jun Zhang +2
Speculative decoding (SD) has emerged as a widely used paradigm to accelerate LLM inference without compromising quality. It works by first employing a compact model to draft multi…
cs.CL2024
Enhancing Tool Retrieval with Iterative Feedback from Large Language Models
Qiancheng Xu, Yongqi Li, Heming Xia +1
Tool learning aims to enhance and expand large language models' (LLMs) capabilities with external tools, which has gained significant attention recently. Current methods have shown…