Cloze Prompt Evaluation

Cloze Prompt Evaluation

The cloze_prompt step evaluates models on cloze-style (fill-in-the-blank) tasks, focusing on the model’s ability to complete partial information based on context.

Overview

Cloze tasks test a model’s understanding of context and its ability to predict missing information. This evaluation approach transforms multiple choice problems into a format where the model needs to determine the right completion rather than selecting from provided choices.

Technical Implementation

This step employs a token probability-based approach to evaluation, operating at a more fundamental level than generative methods. When presented with a task, the model receives a context that includes a placeholder (such as “[BLANK]” or “[MASK]”). Unlike the generative approach, this method doesn’t rely on text generation but instead directly accesses the model’s internal probability distributions. For each possible answer choice, the evaluator computes the total log probability of the tokens that would complete the context. The system then selects the choice with the highest probability as the model’s answer. This approach can optionally incorporate length normalization to account for varying answer lengths.

Key Features

The cloze prompt evaluation focuses on assessing a language model’s core conditional probability estimation capabilities. By directly accessing raw model probabilities instead of relying on generated text, it provides a more direct measurement of the model’s internal confidence. The method supports length normalization to account for answer length differences and offers precise confidence measures through detailed probability scores. This approach can reveal nuanced aspects of model behavior that might be obscured in text-generation evaluations.

Model Compatibility

This evaluation method has specific technical requirements that limit its applicability. It only works with local Hugging Face models where token-level logits and probabilities are accessible to the evaluation system. This means it’s not compatible with most API-based models that only provide text outputs, nor is it suitable for black-box models where internal probability distributions are inaccessible. This limitation restricts its use to scenarios where you have full access to model internals.

When to Use

This approach is best used when you want to evaluate a model’s inherent prediction abilities without the additional complexity of parsing generated text. It provides more precise measurements of model confidence and avoids potential issues with response formatting that can affect generative approaches. The method is particularly valuable when comparing language models at the probability level or when you need fine-grained insights into model uncertainty across different answers.

Implementation Details

The implementation process starts by converting multiple choice problems into a cloze format with appropriate placeholders. For each possible answer choice, it prepares contexts where the placeholder would be filled with that option. The system then directly computes log probabilities for each potential answer, bypassing the text generation process entirely. After calculating these probabilities, it selects the answer with the highest score. The implementation can incorporate various probability adjustments such as length normalization to ensure fair comparisons between answers of different lengths.

Technical Considerations

The cloze prompt evaluation takes a fundamentally different approach compared to generative methods by bypassing the text generation layer of the model and working directly with its probability distributions. This direct access makes the method more robust to formatting issues that might confuse a text-based approach, but also significantly restricts its use to models where these internal probabilities are accessible. This tradeoff between depth of insight and breadth of applicability is an important consideration when choosing evaluation methods.

Further Reading

For a detailed comparison between probability-based methods (like this step) and generative approaches (like the simple_multiple_choice step), refer to:

Robinson, J., & Wingate, D. (2023). Leveraging Large Language Models for Multiple Choice Question Answering. In International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=yKbprarjc5B

This paper presents a comprehensive analysis of different approaches to multiple choice evaluation, including how probability-based methods can offer more reliable measurement in certain scenarios.