Summary of In Search Of Needles in a 11m Haystack: Recurrent Memory Finds What Llms Miss, by Yuri Kuratov et al.
In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss
by Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Dmitry Sorokin, Artyom Sorokin, Mikhail Burtsev
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 tackles the issue of processing lengthy documents using generative transformer models like GPT-4 and RAG. To assess their capabilities, researchers introduce BABILong, a new benchmark that evaluates how well these models can extract and process scattered facts within extensive texts. The evaluation reveals that common methods only work effectively for sequences up to 10^4 elements. In contrast, fine-tuning GPT-2 with recurrent memory augmentations enables it to handle tasks involving up to 11*10^6 elements, marking a significant leap forward. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding long pieces of text, like books or documents. They’re trying to figure out which techniques work best for doing this. To test these techniques, they created a new tool called BABILong. This tool shows how well different computer models can find important information in big texts. The results are surprising! Most methods only work well for short pieces of text, but one special way of fine-tuning GPT-2 lets it understand really long texts. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Rag * Transformer
