Summary of Beyond Memorization: the Challenge Of Random Memory Access in Language Models, by Tongyao Zhu et al.
Beyond Memorization: The Challenge of Random Memory Access in Language Models
by Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang, Min-Yen Kan, Min Lin
First submitted to arxiv on: 12 Mar 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 investigates the mechanisms underlying knowledge storage and memory access within language models, specifically GPT-2. It designs synthetic tasks to test sequential and random memory access, revealing that LMs can sequentially access their memory but struggle with random access. Techniques like recitation and permutation improve random access capability, leading to improved question answering performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how language models (LMs) store and retrieve information from their “memory”. It uses special tasks to test if LMs can remember things in order or just grab whatever they want. The results show that LMs are good at remembering things in the right order, but not so great at picking random stuff out of their memory. To make them better at this, the paper suggests using techniques like repeating words or shuffling sentences. This helps LMs do a better job answering questions. |
Keywords
» Artificial intelligence » Gpt » Question answering
