- The paper introduces a novel method that evaluates LLMs using lossless compression metrics to align model training with information theory.
- It integrates LLMs with adaptive arithmetic coding, demonstrating that minimizing cross-entropy is equivalent to optimizing compression lengths.
- Experimental results across NLP tasks show a positive correlation between compression ratios and model accuracy, offering an efficient benchmarking approach.
Ranking LLMs by Compression
Overview
The paper presents a novel approach to ranking LLMs based on their performance in lossless data compression tasks. The proposed method leverages the concept of information compression to understand and evaluate LLMs, suggesting that the compression ratio can serve as a general metric for model performance. It outlines the equivalence of LLM pre-training goals with compression length under arithmetic coding, thereby providing a framework that obviates the need for typical compression computations.
Methodology
LLMs and Arithmetic Coding for Compression
The authors integrate LLMs with adaptive arithmetic coding to compress text data. By using LLMs as entropy models, they encode text into bit streams, optimizing the coding length based on the probability distributions predicted by the models. Each token's probability guides the allocation of bits, optimizing for high-frequency token representation and consequently maximizing compression efficiency.
Equivalence of Model Training and Compression
The paper demonstrates that the training objectives for LLMs—minimizing cross-entropy between predicted and true data distributions—are equivalent to minimizing the expected length of encoded messages during data compression. This equivalence is rooted in the mathematical formalization of Kullback-Leibler Divergence and Shannon's theorem of coding, providing a theoretical underpinning for using compression metrics as evaluation tools for LLMs.
Experiments and Results
The empirical analysis involves evaluating five LLMs as priors for compression tasks, using the Text8 dataset to calculate compression ratios. Additionally, the paper assesses model performance across several NLP tasks: sentence completion, question answering, and coreference resolution. The results consistently show a positive correlation between compression ratios and model accuracy across tasks, reinforcing the utility of compression as a proxy for evaluating LLM capabilities.
Key Findings
- Sentence Completion: Mistral 7B demonstrates superior accuracy compared to other models, aligning with its higher compression ratio.
- Question Answering: LLaMA 2 7B outperforms OPT-IML 1.3B on the BoolQ dataset, consistent with its superior data compression result.
- Coreference Resolution: GPT-2-XL shows better performance than GPT-2 when evaluated on the Winograd Schema Challenge, correlating with differences in compression ratios.
Implications and Future Work
The implications of using compression ratios as a unified metric extend beyond model evaluation, potentially influencing LLM optimization and development strategies. This approach facilitates an efficient, standardized evaluation framework that mitigates task-specific metric challenges and data contamination issues prevalent in traditional benchmarking.
Future research might explore the scalability of this method with more advanced LLMs, addressing computational constraints noted during experimentation. Additionally, developing a comprehensive evaluation system that not only ranks but also diagnoses underlying model capabilities and limitations remains an open avenue for further investigation.
Conclusion
By demonstrating the theoretical and empirical viability of compression ratios as evaluation metrics for LLM generalization abilities, this paper contributes a compelling alternative to traditional task-based benchmarks. The approach elucidates the intimate connection between compression and model understanding, offering a streamlined, objective measure for comparing LLMs in diverse applications.
