Long-Range Tasks Using Short-Context LLMs: Incremental Reasoning With Structured Memories

• URL: http://arxiv.org/abs/2412.18914v1
• Date: Wed, 25 Dec 2024 14:14:31 GMT
• Title: Long-Range Tasks Using Short-Context LLMs: Incremental Reasoning With Structured Memories
• Authors: Dulhan Jayalath, James Bradley Wendt, Nicholas Monath, Sandeep Tata, Beliz Gunel,
• Abstract summary: We present PRISM, which alleviates concerns by processing information as a stream of chunks, maintaining a structured in-context memory.<n>This approach demonstrates superior performance to baselines on diverse tasks while using at least 4x smaller contexts.<n>It achieves 54% cost reduction when compared to alternative short-context approaches.
• Score: 12.133230897181594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-range tasks require reasoning over long inputs. Existing solutions either need large compute budgets, training data, access to model weights, or use complex, task-specific approaches. We present PRISM, which alleviates these concerns by processing information as a stream of chunks, maintaining a structured in-context memory specified by a typed hierarchy schema. This approach demonstrates superior performance to baselines on diverse tasks while using at least 4x smaller contexts than long-context models. Moreover, PRISM is token-efficient. By producing short outputs and efficiently leveraging key-value (KV) caches, it achieves up to 54% cost reduction when compared to alternative short-context approaches. The method also scales down to tiny information chunks (e.g., 500 tokens) without increasing the number of tokens encoded or sacrificing quality. Furthermore, we show that it is possible to generate schemas to generalize our approach to new tasks with minimal effort.

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