Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data

2024-10-15

arXiv.org

AI Summary

Existing benchmarks lack comprehensive evaluation of holistic cross-document reasoning over long texts. Method: We introduce HoloBench—the first long-text benchmark grounded in database operation paradigms—mapping structured operations (e.g., aggregation, grouping, extremum queries) to multi-document textual reasoning tasks. Our approach employs controlled-variable experimentation (varying context length, information density, distribution patterns, and query types), formal task modeling, and synthetic data generation. Contribution/Results: We find that model performance depends primarily on total information volume and query type—not merely context length; extremum queries exhibit high robustness, whereas multi-source aggregation accuracy degrades significantly with increasing text length; grouping information improves performance, but optimal grouping position varies across model architectures. This work pioneers the integration of structured database operations into textual reasoning evaluation, establishing a novel paradigm for assessing holistic reasoning capabilities of long-context language models (LCLMs).

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid increase in textual information means we need more efficient methods to sift through, organize, and understand it all. While retrieval-augmented generation (RAG) models excel in accessing information from large document collections, they struggle with complex tasks that require aggregation and reasoning over information spanning across multiple documents--what we call holistic reasoning. Long-context language models (LCLMs) have great potential for managing large-scale documents, but their holistic reasoning capabilities remain unclear. In this work, we introduce HoloBench, a novel framework that brings database reasoning operations into text-based contexts, making it easier to systematically evaluate how LCLMs handle holistic reasoning across large documents. Our approach adjusts key factors such as context length, information density, distribution of information, and query complexity to evaluate LCLMs comprehensively. Our experiments show that the amount of information in the context has a bigger influence on LCLM performance than the actual context length. Furthermore, the complexity of queries affects performance more than the amount of information, particularly for different types of queries. Interestingly, queries that involve finding maximum or minimum values are easier for LCLMs and are less affected by context length, even though they pose challenges for RAG systems. However, tasks requiring the aggregation of multiple pieces of information show a noticeable drop in accuracy as context length increases. Additionally, we find that while grouping relevant information generally improves performance, the optimal positioning varies across models. Our findings surface both the advancements and the ongoing challenges in achieving a holistic understanding of long contexts.

Problem

• Enhancing holistic reasoning in long-context language models
• Evaluating LCLMs on database operations with massive text
• Assessing impact of context length and query complexity

Innovation

• Introduces HoloBench for LCLM evaluation.
• Adjusts context factors for comprehensive testing.
• Highlights information density impact on LCLMs.