UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization and Distillation
arXiv:2602.09130v5 Announce Type: replace Abstract: Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning and quantization evaluated primarily on knowledge-centric benchmarks. Thus, we introduce UniComp, a unified evaluation framework for comparing pruning, quantization, and knowledge distillation. UniComp evaluates compressed models along three dimensions: performance, reliability, and efficiency, using a diverse set of capability- and safety-oriented benchmarks together with a hardware-aware efficienc
The proliferation of increasingly large language models necessitates robust compression techniques for practical deployment, and this paper addresses a gap in comprehensive evaluation frameworks.
A unified evaluation of LLM compression methods directly impacts the efficiency and accessibility of advanced AI, lowering computational barriers and enabling wider adoption.
The unified evaluation framework, UniComp, shifts the LLM compression landscape by providing a standardized method for comparing pruning, quantization, and distillation, considering performance, reliability, and efficiency.
- · AI developers
- · Edge AI computing
- · Cloud providers
- · Niche hardware manufacturers
- · Inefficient LLM architectures
- · Undifferentiated compression techniques
More efficient and cost-effective deployment of large language models across diverse applications.
Increased competition among hardware and software providers offering optimized solutions for compressed LLMs, leading to further innovation.
Democratization of advanced AI capabilities as computational demands are lowered, enabling new applications in resource-constrained environments.
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Read at arXiv cs.LG