Summary of Extending Token Computation For Llm Reasoning, by Bingli Liao et al.
Extending Token Computation for LLM Reasoning
by Bingli Liao, Danilo Vasconcellos Vargas
First submitted to arxiv on: 22 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 limitations of Large Language Models (LLMs) in complex reasoning tasks due to inefficient attention distributions. By analyzing attention patterns across layers, researchers identify inefficiencies caused by non-semantic tokens with high attention scores. To address this, they propose an algorithm that emulates early layer attention patterns across downstream layers, re-balancing skewed attention distributions and enhancing knowledge abstraction. The study demonstrates the effectiveness of their approach in improving LLMs’ reasoning capabilities, particularly in non-STEM domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand language, but they often struggle to solve complex problems. Researchers found out why this is happening and created a new way to make these models better at solving problems by adjusting how they pay attention to information. This makes the models even more powerful and able to help with lots of different tasks in real life. |
Keywords
* Artificial intelligence * Attention
