mlsanityData to 7 Oct 2026

GeckOpt: LLM System Efficiency via Intent-Based Tool Selection

arXiv 2404.15804 ↗First posted 24 Apr 2024cs.LG cs.AIcited by 12

Not on the map. Why?

Abstract (arXiv)

In this preliminary study, we investigate a GPT-driven intent-based reasoning approach to streamline tool selection for large language models (LLMs) aimed at system efficiency. By identifying the intent behind user prompts at runtime, we narrow down the API toolset required for task execution, reducing token consumption by up to 24.6%. Early results on a real-world, massively parallel Copilot platform with over 100 GPT-4-Turbo nodes show cost reductions and potential towards improving LLM-based system efficiency.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

6 of 7 references.

References · 6

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  3. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  4. 2024GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilotscited by 27
  5. 2024Evaluating Tool-Augmented Agents in Remote Sensing Platformscited by 17
  6. 2023Stable Diffusion For Aerial Object Detectioncited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 6; 2026: 1.

12 papers.