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elvis· @omarsar0 · X·· 3 小时前AI 评分43
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基于 LLM 的智能体很喜欢结构化数据。 我见过这种方法效果很好,但主要是在复杂代码库中。不过我认为上下文图谱有更广泛的应用,正如这份 Harvey 关于法律知识任务的报告所示。 我们正在 @dair_ai 做研究相关任务方面的工作。很快会分享更多。

正文

LLM-based agents love structured data.

I've seen this work really well, but mostly in complex codebases. But I think context graphs have broader applications, as shown in this Harvey report on legal knowledge tasks.

We are working on this at @dair_ai for research-related tasks. More to share soon.

引用Harvey@harvey
We built context graphs to help agents work through long-horizon legal tasks. Starting with Legal Agent Bench tasks in 11 categories like M&A diligence and contract negotiation, we had agents build context graphs of the underlying documents (without access to the eval tasks or grading rubrics). Context graphs dramatically improved agent performance on LAB tasks: - Firm Knowledge tasks: 65% → 80% - Precedent lists tasks: 67% → 80% - Matter counting tasks: 53% → 83% They also boost agent efficiency: we found a 48% cost reduction for agents given access to a context graph to assist their work. Full video below - more to come soon.
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来源:elvis · x.com