Bring Your Own Data! Self-Supervised Evaluation for Large Language Models (2306.13651v2)

Abstract: With the rise of LLMs and their ubiquitous deployment in diverse domains, measuring LLM behavior on realistic data is imperative. For example, a company deploying a client-facing chatbot must ensure that the model will not respond to client requests with profanity. Current evaluations approach this problem using small, domain-specific datasets with human-curated labels. These evaluation sets are often sampled from a narrow and simplified distribution, and data sources can unknowingly be leaked into the training set which can lead to misleading evaluations. To bypass these drawbacks, we propose a framework for self-supervised evaluation of LLMs by analyzing their sensitivity or invariance to transformations on the input text. Self-supervised evaluation can directly monitor LLM behavior on datasets collected in the wild or streamed during live model deployment. We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence, in addition to sensitivity to grammatical structure and tokenization errors. When comparisons to similar human-labeled benchmarks are available, we find strong correlations between self-supervised and human-supervised evaluations. The self-supervised paradigm complements current evaluation strategies that rely on labeled data.

• The paper introduces a self-supervised evaluation framework that leverages sensitivity scores for metrics such as perplexity, toxicity, and long-range dependencies.
• The methodology uses text transformation perturbations to benchmark model behaviors, demonstrating strong correlation with established benchmarks like TriviaQA and LAMBADA.
• The approach decouples evaluations from static datasets, enabling dynamic, real-world assessments of LLM robustness and response to diverse input variations.

Self-Supervised Evaluation of LLMs: A Critical Examination

In their research paper, Jain et al. introduce an innovative paradigm for evaluating LLMs through self-supervised strategies. As LLMs continue to proliferate across various domains, understanding their nuanced behaviors under realistic data conditions becomes imperative. Traditional evaluations leveraging small, curated datasets and human labels face challenges such as dataset obsolescence and inadvertent training set contamination. The proposed self-supervised evaluation framework circumvents these limitations by focusing on the sensitivity or invariance of LLMs to specific text transformations, thus enabling assessment directly on dynamically collected datasets.

Key Contributions and Methodology

The paper outlines several case studies demonstrating the efficacy of self-supervised evaluation metrics. The authors develop sensitivity scores for closed-book knowledge, toxicity, long-range context dependency, grammatical structure sensitivity, and tokenization robustness. For instance, to gauge knowledge probing via negations, the framework assesses the change in perplexity scores when factual sentences are negated. This approach is compared to established human-supervised benchmarks, showing a strong correlation with TriviaQA accuracy.

Another significant contribution is the framework's capability to measure the robustness of LLMs against toxic text. By analyzing the model's responses to profane prompt variations, the authors develop a reproducible metric aligned with the Perspective API's outputs while excluding reliance on external classifiers. Furthermore, the analysis of long-range dependencies employs Jensen–Shannon divergence to quantify context sensitivity by substituting portion texts, showcasing comparability with LAMBADA benchmarks.

The research also provides insights into word order sensitivity and tokenization robustness. The word order metric assesses how permutation of words affects LLM predictions, while tokenization sensitivity explores the effects of non-standard tokenization without altering the core text. Both metrics are pivotal in understanding LLM resilience against input perturbations that may occur in deployment scenarios.

Implications and Theoretical Insights

One of the fundamental implications of this work is its potential to decouple evaluation from static and rigid benchmarks, offering a flexible, scalable approach that aligns with the ongoing developments in LLM deployments. These self-supervised ontologies present unprecedented opportunities to assess LLMs in real-time production environments, adapting to diverse application contexts without the prohibitive cost of data curation.

The theoretical advancements in sensitivity and invariance analysis presented in this paper may further illuminate the interplay between model size and behavior stability. Indeed, the authors document that larger models generally exhibit higher sensitivity scores across nearly all proposed metrics, reflecting their improved ability to discern nuanced input transformations.

However, the research also underscores the profound impact of model instruction finetuning, particularly when addressing robustness to syntactic variation and toxicity. Surprisingly, instruction-tuned models show diverse behaviors, particularly in the normalization of sensitivities, suggesting further exploration into the fine-grained influence of finetuning datasets and interventions.

Future Directions and Challenges

The paper identifies unexplored trajectories, such as the influence of model entropy and memorization on sensitivity scores. Future explorations might explore how entropy affects self-supervised evaluation metrics, or how memorization of training samples influences model responses to self-supervised interventions. Another promising area might be extending self-supervised frameworks to explore more granular aspects of model reasoning and decision-making, especially in complex, multi-modal environments.

The introduced self-supervised methodologies pave a transformative path for evaluating LLMs. By trading reliance on external labeled data for text manipulation strategies, the research posits a future where LLM assessment is more dynamic, reflective, and intrinsically connected to the models' functional contexts. This framework is poised to significantly enhance the understanding and design of robust, generalizable LLMs, even as AI technologies continue to evolve at a rapid pace.