Summary of Llm Augmentations to Support Analytical Reasoning Over Multiple Documents, by Raquib Bin Yousuf et al.
LLM Augmentations to support Analytical Reasoning over Multiple Documents
by Raquib Bin Yousuf, Nicholas Defelice, Mandar Sharma, Shengzhe Xu, Naren Ramakrishnan
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates the application of large language models (LLMs) to enhance in-depth analytical reasoning within the context of intelligence analysis. The authors explore how LLMs can be used to augment the capabilities of an LLM with a memory module called dynamic evidence trees (DETs), which is designed to develop and track multiple investigation threads. The paper highlights that while LLMs are capable of performing various tasks, they are still inadequate for supporting intelligence analysts in complex reasoning applications. To improve the performance of LLMs for such applications, the authors offer recommendations based on extensive experiments conducted on multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models can help people who analyze information to figure out what’s going on. These analysts have a lot of data and need to find connections between things that seem unrelated. The researchers tested if these models could be helpful for this task and developed a way to make them better by adding something called dynamic evidence trees. They found that the models aren’t good enough yet, but they gave suggestions on how to make them better. |
