Summary of Multilingual Needle in a Haystack: Investigating Long-context Behavior Of Multilingual Large Language Models, by Amey Hengle et al.
Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models
by Amey Hengle, Prasoon Bajpai, Soham Dan, Tanmoy Chakraborty
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed research introduces a novel evaluation framework, MLNeedle, designed to assess the long-context capabilities of large language models (LLMs) in multilingual settings for information retrieval. The authors evaluate four state-of-the-art LLMs on this task, revealing significant variations in model performance depending on language and needle position. Notably, models struggle with cross-lingual retrieval as context length increases. This study provides essential insights into the long-context behavior of LLMs in multilingual settings, guiding future evaluation protocols. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to test how well large language models can find information from lots of different languages. They tested four top models and found that they all do worse when trying to find something in a language other than English or if it’s in the middle of a long piece of text. This helps us understand what these models are good at and what they need to get better. |
Keywords
* Artificial intelligence * Context length
