Summary of Can’t Remember Details in Long Documents? You Need Some R&r, by Devanshu Agrawal et al.
Can’t Remember Details in Long Documents? You Need Some R&R
by Devanshu Agrawal, Shang Gao, Martin Gajek
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 a novel approach called R&R (reprompting and in-context retrieval) to improve long-context question-answering (QA) over documents. The method combines two prompt-based techniques: reprompting, which repeats the original prompt instructions throughout the context document; and ICR (in-context retrieval), which retrieves relevant passage numbers for a given question. The authors test R&R on GPT-4 Turbo and Claude-2.1 models on documents up to 80k tokens long, observing an average 16-point boost in QA accuracy. Analysis suggests that R&R reduces the distance between relevant context and instructions, improving performance. Compared to chunkwise methods for short-context QA, R&R enables larger chunks with fewer LLM calls while maintaining accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make computers better at answering questions when given lots of text to read. Right now, these computers (called large language models) are good at finding answers in shorter texts, but struggle with longer ones. The new method, called R&R, helps the computers by giving them reminders about what they’re looking for and pointing out important parts of the text. This makes the computers much better at answering questions from long texts. The researchers tested this method on two different computer models and found that it worked really well. |
Keywords
* Artificial intelligence * Claude * Gpt * Prompt * Question answering
