Summary of Agentquest: a Modular Benchmark Framework to Measure Progress and Improve Llm Agents, by Luca Gioacchini et al.
AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents
by Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito, Kiril Gashteovski, David Friede, Roberto Bifulco, Carolin Lawrence
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, AgentQuest, aims to address the limitations of existing benchmarks for Large Language Models (LLMs) by introducing modular and extensible evaluation metrics. The framework offers two new metrics that track LLM agent progress while solving tasks, providing a reliable measure of performance. The authors demonstrate the effectiveness of these metrics on two use cases, identifying common failure points and refining the agent architecture to achieve significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are getting better at solving complex problems! Researchers want to make sure they’re making progress by using special tools called benchmarks. But existing benchmarks only look at how well the LLM does overall, without telling us what’s going wrong or how to make it better. To fix this, scientists created a new framework called AgentQuest that has two main parts: easy-to-use APIs for adding new benchmarks and metrics, and two new ways to measure how well an LLM is doing. The authors tested these new tools on two examples and found that they helped the LLM perform much better. |
