Summary of Babilong: Testing the Limits Of Llms with Long Context Reasoning-in-a-haystack, by Yuri Kuratov et al.
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
by Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Ivan Rodkin, Dmitry Sorokin, Artyom Sorokin, Mikhail Burtsev
First submitted to arxiv on: 14 Jun 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 introduces the BABILong benchmark to evaluate large language models’ (LLMs) ability to reason across facts distributed in extremely long documents. The benchmark consists of 20 reasoning tasks, including fact chaining, induction, deduction, and handling lists/sets. Evaluations show that popular LLMs effectively utilize only a small portion of the context and their performance declines with increased reasoning complexity. Retrieval-Augmented Generation methods achieve modest accuracy on single-fact question answering, independent of context length. Recurrent memory transformers demonstrate high performance after fine-tuning, enabling processing up to 50 million tokens. The benchmark is extendable to any length to support evaluation of new models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test how well large language models can understand and reason about long pieces of text. This is important because many models struggle with very long texts. To solve this problem, the authors created a special set of 20 tasks that ask the model to do things like find specific facts or count items in a list. They found that popular models only use a small part of the text and get worse at understanding as the task gets harder. Some newer methods are better at handling very long texts. The authors hope this new way of testing will help us understand how well different language models can handle long texts. |
Keywords
» Artificial intelligence » Context length » Fine tuning » Question answering » Retrieval augmented generation
