PRISM: Efficient Long-Range Reasoning With Short-Context LLMs (2412.18914v2)

Abstract: Long-range tasks demand reasoning over long inputs. Current solutions require large compute budgets, training data, model weight access, or complex task-specific designs. We introduce PRISM, which processes information as a stream of chunks while maintaining a structured in-context memory specified with a typed hierarchical schema. PRISM outperforms baselines on diverse tasks while using at least 4x shorter contexts than long-context models. This approach is token-efficient, producing concise outputs and efficiently leveraging key-value (KV) caches to reduce costs by up to 54% compared to alternative short-context methods. 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.

• The paper presents PRISM, an approach that incrementally processes inputs with a structured memory hierarchy to tackle long-range tasks using short-context LLMs.
• It achieves up to 54% token cost reduction while nearly matching long-context model performance on benchmark tasks such as BooookScore and RepoQA.
• PRISM dynamically generates task-specific schemas, showcasing its versatility and scalability across varied NLP applications.

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

This paper introduces PRISM, an approach that addresses the challenges inherent in solving long-range tasks using short-context LLMs. PRISM utilizes a stream-processing methodology by dividing inputs into manageable chunks while maintaining an organized in-context memory with a typed hierarchy schema. This technique promises enhanced performance across diverse tasks compared to baseline models, such as models with expansive contexts or those employing task-specific methods. Unique in its token efficiency, PRISM is capable of delivering higher quality outputs using smaller contexts — up to 50x smaller than those required by long-context models — while also being cost-effective, achieving significant reductions in processing costs.

Key Contributions

The authors illuminate several seminal contributions across methodology, performance, and applicability:

1. Processing Incrementally with Structured Memory (PRISM): Unlike standard models that necessitate either vast training data or substantial compute allocations, PRISM operates independently of access to model weights, training efforts, or elaborate task-specific strategies. This independence stems from its handling of data as incremental information chunks, with a structured memory guiding the task resolution process. The structured memory is characterized by a user-defined schema within a typed hierarchy, ensuring that the memory remains minimally verbose and precise in its task guidance.
2. Cost and Token Efficiency: A significant feature of PRISM is its token efficacy, as it surmounts the constraints of token-heavy outputs and tasks through structured key-value (KV) caching. By reusing previously computed activations for unchanged parts of memory across incremental updates, PRISM optimizes computational costs — achieving up to 54% reduction in comparison to alternative approaches.
3. Generalizability and Schema Generation: PRISM excels in its adaptability to new tasks. By generating task-specific schemas through LLMs, the approach automates the process of defining memory without expert intervention, aligning the memory structure dynamically with the task at hand.

Experimental Results

The experiments leverage state-of-the-art datasets: BooookScore, RepoQA, and LOFT-Spider, covering the spectrum from narrative summarization to code understanding and structured database retrieval. PRISM demonstrates notable improvements in task performance metrics over traditional short-context baselines like incremental and hierarchical merging methods. Specifically:

• On BooookScore, PRISM approximates long-context model performance, achieving 97% of its score using substantially fewer tokens.
• For tasks like RepoQA, PRISM remains competitive, capturing essential code retrieval relationships with intermediary performance yet significantly reduced context requirements.

Importantly, these enhancements indicate PRISM's capability to mitigate the challenges presented by context limitations, directly impacting the feasibility of using smaller models for extensive reasoning tasks.

Practical and Theoretical Implications

From a practical perspective, PRISM democratizes access to high-performing NLP solutions by reducing the dependence on computationally demanding resources, providing a pathway toward more environmentally sustainable applications of AI. Theoretically, the paradigm shift toward structured in-context memories advocates for the redefinition of how context is leveraged in LLMs, prompting future research into optimization strategies, schema adaptability, and more granular memory revision methodologies.

Speculation and Future Directions

Given the paper’s insights, future exploration could focus on further refining schema generation to enhance precision and relevance across varied domains. Investigating how PRISM adapts to increasingly complex or nuanced long-context tasks might yield deeper understandings of its limits and potential expansions. Additionally, aligning PRISM with real-world applications could explore its efficacy in dynamic environments where live data feeds necessitate instantaneous processing and response adaptations.

In conclusion, PRISM redefines the strategy for handling long-range tasks within constrained contexts, presenting an adaptable, scalable, and markedly efficient architecture that broadens the applicability and affordability of advanced LLM capabilities.