Summary of Prism: Efficient Long-range Reasoning with Short-context Llms, by Dulhan Jayalath et al.
PRISM: Efficient Long-Range Reasoning With Short-Context LLMs
by Dulhan Jayalath, James Bradley Wendt, Nicholas Monath, Sandeep Tata, Beliz Gunel
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Long-range tasks require processing long inputs, a challenge addressed by current solutions which rely on large compute budgets, training data, model weight access, or complex task-specific designs. In contrast, we introduce PRISM, a novel approach that processes information as a stream of chunks while maintaining a structured in-context memory specified with a typed hierarchical schema. This schema-guided processing enables PRISM to outperform baselines on diverse tasks using at least 4x shorter contexts than long-context models. Additionally, PRISM is token-efficient, producing concise outputs and leveraging key-value (KV) caches to reduce costs by up to 54% compared to alternative short-context methods. Notably, PRISM scales down to tiny chunks (<500 tokens) without increasing encoding costs or sacrificing quality, and generalizes to new tasks with minimal effort by automatically generating schemas from task descriptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to make computers better at doing long tasks that require lots of information. Right now, most solutions need a lot of computer power, special training data, or complex designs to work well. The new approach, called PRISM, breaks down the information into smaller chunks and uses a special memory structure to help it understand what’s important. This makes PRISM faster and more efficient than other methods, while still getting good results. Plus, PRISM can even shrink the amount of information it needs to do its job without losing quality or using too many resources. |
Keywords
» Artificial intelligence » Token
