Summary of Make Your Llm Fully Utilize the Context, by Shengnan An et al.
Make Your LLM Fully Utilize the Context
by Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou
First submitted to arxiv on: 25 Apr 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 This study tackles the “lost-in-the-middle challenge” in large language models (LLMs), where they struggle to utilize information within lengthy input. The researchers propose an innovative solution, information-intensive (IN2) training, which leverages a synthesized long-context question-answer dataset to overcome this limitation. Specifically, IN2 training emphasizes fine-grained information awareness and integration across short segments within the long context. Applying this approach to Mistral-7B, they develop FILM-7B, which excels in utilizing long contexts. Probing tasks demonstrate FILM-7B’s robustness in retrieving information from different positions in its 32K context window. Furthermore, FILM-7B significantly improves performance on real-world long-context tasks (e.g., NarrativeQA) while maintaining comparable performance on short-context tasks (e.g., MMLU). The study showcases the potential of IN2 training for enhancing LLMs’ ability to handle lengthy input. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a really long piece of writing, but most computer models struggle to find important information in the middle. Researchers wanted to fix this “lost-in-the-middle challenge” by teaching computers to look at short parts of the text and then put them together to understand the whole thing. They created a special training method that helps computers learn from longer pieces of text. The new way of training helped the computer model, FILM-7B, do better on tasks that involve understanding long texts. This could be useful for things like summarizing really long articles or helping computers have conversations with humans. |
Keywords
» Artificial intelligence » Context window
