Paloma: A Benchmark for Evaluating Language Model Fit (2312.10523v2)

Abstract: Evaluations of LLMs (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains--varying distributions of language. We introduce Perplexity Analysis for LLM Assessment (Paloma), a benchmark to measure LM fit to 546 English and code domains, instead of assuming perplexity on one distribution extrapolates to others. We include two new datasets of the top 100 subreddits (e.g., r/depression on Reddit) and programming languages (e.g., Java on GitHub), both sources common in contemporary LMs. With our benchmark, we release 6 baseline 1B LMs carefully controlled to provide fair comparisons about which pretraining corpus is best and code for others to apply those controls to their own experiments. Our case studies demonstrate how the fine-grained results from Paloma surface findings such as that models pretrained without data beyond Common Crawl exhibit anomalous gaps in LM fit to many domains or that loss is dominated by the most frequently occurring strings in the vocabulary.

• The paper presents domain-specific perplexity evaluation across 585 diverse domains, advancing beyond one-size-fits-all benchmarks.
• It employs rigorous decontamination and standardized evaluation methods to prevent data leakage and ensure consistent experimental comparisons.
• The research reveals that scaling model parameters or data volume unevenly benefits performance, highlighting the need for nuanced evaluation metrics.

An Analytical Exploration of Evaluating LLM Fit: The Paloma Benchmark

The paper describes "Perplexity Analysis for LLM Assessment" (Paloma), a novel benchmark for analyzing LLM (LM) performance across various text domains. This benchmark introduces a new dimension to evaluating LMs by analyzing their fit across an expansive collection of 585 text domains from diverse sources, ranging from mainstream media to specialized online forums. Unlike conventional approaches that employ perplexity on a singular, monolithic dataset, Paloma aims to provide a more comprehensive view of model performance across varied linguistic distributions.

Technical Contributions and Methodological Rigor

Key technical innovations of Paloma include:

• Domain-Specific Perplexity Evaluation: The benchmark delineates perplexity analysis across numerous domains, advancing beyond the usual single corpus applicability. This approach acknowledges the inherent diversity within language data and counters the limitations of prior, generalized perplexity measures.
• Decontamination and Standardization: The paper emphasizes rigorous decontamination of training data from evaluation sets, addressing data leakage concerns which can artificially deflate perplexity values. Additionally, Paloma prescribes standardized evaluation formats and fixed tokenization to ensure consistency across experimental comparisons.
• Stratified Subsampling: To mitigate subsampling biases and enhance reliability in perplexity assessments, the benchmark employs stratified sampling, ensuring more stable evaluations without overwhelming computational resource requirements.
• Robustness and Efficiency: Paloma allows researchers to record model parameters and training token counts, enabling analysis of performance with respect to computational cost—a key metric for assessing Pareto efficiency.

Empirical Results and Interpretations

The authors present several case studies demonstrating the applicability of Paloma:

1. Dataset Heterogeneity and Model Performance: Through controlled experiments, it is evident that model performance varies significantly across domains, highlighting the inadequacy of relying on a single data distribution for comprehensive performance evaluation.
2. Impact of Pretraining Corpora: The benchmark data illustrates that models pretrained on solely Common Crawl-derived data exhibit inconsistent domain performance, while those integrating diverse data sources show improved stability and domain generalization.
3. Scaling Dynamics: The research discerns that scaling either the number of parameters or pretraining data volume generally enhances performance but with uneven improvement across domains. This insight challenges the assumption that scaling benefits all language facets equally.
4. Type-Level Analysis: Beyond domains, the paper explores model performance at the vocabulary level, uncovering types that exhibit inverse scaling phenomena. This highlights potential inefficiencies in token-level modeling, inviting further scrutiny.

Implications and Future Directions

Paloma stands as a pivotal contribution to the landscape of LM evaluation by setting a precedent for fine-grained assessment across diverse linguistic contexts. It provides an invaluable framework for practitioners and researchers to critically appraise and enhance model architectures, pretraining corpora, and scalability strategies. It also underscores the necessity for continual reevaluation of evaluation benchmarks to align with evolving linguistic data landscapes.

The paper suggests future research avenues may include extending the Paloma framework to incorporate multilingual data, exploring deeper causal links between perplexity and downstream task performance, and refining metrics that capture alignment with human-relevant linguistic features. By adopting these directions, the field can progress towards more holistic and nuanced assessments of LLM capabilities. The work invites the research community to contemplate broader, more inclusive assessment paradigms reflective of the global tapestry of human language.