Systematic Evaluation of Optimization Techniques for Long-Context Language Models

2025-08-01

AI Summary

To address memory overhead and limited context window constraints in deploying large language models (LLMs) for long-context scenarios, this work conducts a systematic end-to-end evaluation of pruning, quantization, and token dropping across 7B–70B models. We employ a joint evaluation framework spanning system-level metrics (memory footprint, latency, throughput) and task-level metrics (precision-recall trade-offs, generation quality), covering mainstream architectures. Our findings reveal: (1) optimization efficacy exhibits strong model-scale dependence; (2) naïve composition of techniques degrades performance in larger models due to error accumulation; and (3) single-metric evaluations (e.g., F1) obscure critical accuracy-efficiency trade-offs. Collectively, these results expose the fundamental tension among efficiency, accuracy, and scalability in long-context LLMs, establishing an empirical benchmark and principled design guidelines for practical optimization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Large language models (LLMs) excel across diverse natural language processing tasks but face resource demands and limited context windows. Although techniques like pruning, quantization, and token dropping can mitigate these issues, their efficacy in long-context scenarios and system evaluation remains underexplored. This paper systematically benchmarks these optimizations, characterizing memory usage, latency, and throughput, and studies how these methods impact the quality of text generation. We first analyze individual optimization methods for two LLM architectures supporting long context and then systematically evaluate combinations of these techniques to assess how this deeper analysis impacts performance metrics. We subsequently study the scalability of individual optimization methods on a larger variant with 70 billion-parameter model. Our novel insights reveal that naive combination inference optimization algorithms can adversely affect larger models due to compounded approximation errors, as compared to their smaller counterparts. Experiments show that relying solely on F1 obscures these effects by hiding precision-recall trade-offs in question answering tasks. By integrating system-level profiling with task-specific insights, this study helps LLM practitioners and researchers explore and balance efficiency, accuracy, and scalability across tasks and hardware configurations.

Problem

• Evaluate optimization techniques for long-context LLMs
• Assess impact on memory, latency, and generation quality
• Study scalability and error effects in large models

Innovation

• Benchmarks pruning, quantization, and token dropping
• Analyzes optimization combinations for performance impact
• Studies scalability on 70B-parameter model