Summary of Retrieve to Explain: Evidence-driven Predictions with Language Models, by Ravi Patel et al.
Retrieve to Explain: Evidence-driven Predictions with Language Models
by Ravi Patel, Angus Brayne, Rogier Hintzen, Daniel Jaroslawicz, Georgiana Neculae, Dane Corneil
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces Retrieve to Explain (R2E), a retrieval-based language model that scores and ranks answers to research questions based on evidence retrieved from a document corpus. Unlike existing models, R2E quantitatively compares answer plausibility in terms of supporting evidence, enabling transparent attribution of answer scores back to their underlying evidence. The architecture allows for incorporation of new evidence without retraining, including non-textual data modalities templated into natural language. R2E is assessed on the challenging task of drug target identification from scientific literature, outperforming a genetics-based approach used in the pharmaceutical industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to understand why scientists think something is true. Right now, computers can summarize lots of research papers quickly, but they don’t know which answers are best supported by evidence. The new language model, called R2E, fixes this problem. It takes a research question and finds all the possible answers that people might give. Then it looks at the evidence behind each answer and ranks them based on how good that evidence is. This makes it easier for scientists to decide which answer is most likely to be correct. |
Keywords
* Artificial intelligence * Language model
