Summary of Fastmem: Fast Memorization Of Prompt Improves Context Awareness Of Large Language Models, by Junyi Zhu et al.
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models
by Junyi Zhu, Shuochen Liu, Yu Yu, Bo Tang, Yibo Yan, Zhiyu Li, Feiyu Xiong, Tong Xu, Matthew B. Blaschko
First submitted to arxiv on: 23 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 FastMem, a novel method for instruction fine-tuned large language models (LLMs), enhances context awareness by fast memorization of prompts. This targeted approach updates only the last Feed-Forward Network (FFN) module, maximizing prompt likelihood before inference and preventing overfitting. FastMem significantly improves comprehension and accuracy in reading comprehension, text summarization, and output structure adherence tasks. Experimental results demonstrate substantial gains on datasets like NQ-SWAP and Qwen 1.5-4B-Chat, with improved accuracy (e.g., from 59.1% to 71.6%) and reduced failure rates (e.g., from 34.9% to 25.5%). FastMem offers a robust solution for enhancing LLM reliability and accuracy in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make large language models better at following rules and staying accurate. The method, called FastMem, helps the model remember prompts and stay focused on what it’s supposed to do. This makes it more reliable and accurate for tasks like reading comprehension and text summarization. In tests, FastMem improved accuracy by 12 percentage points and reduced mistakes by 9 percentage points. This new approach could help make language models even more useful in many areas. |
Keywords
» Artificial intelligence » Inference » Likelihood » Overfitting » Prompt » Summarization
