Summary of Rage Against the Machine: Retrieval-augmented Llm Explanations, by Joel Rorseth et al.
RAGE Against the Machine: Retrieval-Augmented LLM Explanations
by Joel Rorseth, Parke Godfrey, Lukasz Golab, Divesh Srivastava, Jaroslaw Szlichta
First submitted to arxiv on: 11 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This paper presents an innovative tool called RAGE for interpreting Large Language Models (LLMs) with retrieval capabilities. Specifically, it enhances LLMs’ ability to access and incorporate relevant information from external sources into their input context. The authors focus on providing counterfactual explanations that pinpoint specific parts of the input context necessary for a particular answer, allowing users to explore the reasoning behind an LLM’s output. RAGE also features pruning methods to efficiently navigate the vast space of possible explanations, enabling users to inspect the provenance of generated answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new tool called RAGE that helps people understand how Large Language Models work better. It lets these models look up extra information from outside sources and use it to answer questions. The tool shows why certain parts of the question matter by pointing out what changes if you remove them. This makes it easier for people to see how the model came up with its answers. |
Keywords
» Artificial intelligence » Pruning
