Multiple Choice Evaluation

Multiple Choice Evaluation

The simple_multiple_choice step is the most basic form of evaluation in FreeEval. It tests a model’s ability to select the correct option from a set of choices.

Overview

Multiple choice evaluation is straightforward and interpretable, making it one of the most widely used methods for LLM evaluation. The model is presented with a question and a set of possible answers, and must choose the correct one.

Technical Implementation

This step implements a generative approach to evaluation. When evaluating a model, it receives a formatted prompt containing both the question and answer choices (typically labeled A, B, C, D). The model then generates a free-form text response to this prompt. The evaluation system analyzes this response to determine which option the model selected, using pattern matching to identify letter identifiers (A, B, C, D) and key phrases from the answer options. For greater reliability, results can be aggregated across multiple runs of the same question.

Key Features

The multiple choice evaluation step offers direct accuracy measurement, automatically calculating scores based on correct responses. It supports various multiple choice datasets including ARC, MMLU, and others, with flexible formatting options. The step includes different answer aggregation methods such as mean scoring, voting, and options to ignore augmented data. For additional robustness, it can create permutations of questions to test consistency across different orderings of the same options.

Model Compatibility

One of the primary advantages of this approach is its versatility across different model types. It works with all model varieties in FreeEval, including local Hugging Face models and remote API models. Because it doesn’t require access to token probabilities or other internal model features, it’s compatible with both open and closed-source models, making it ideal for evaluating black-box systems.

When to Use

This step is best used when you need to benchmark your model against standard knowledge and reasoning tests with well-defined correct answers. It provides clear, comparable metrics on structured tasks and allows for quick assessment across various domains. It’s particularly valuable when evaluating black-box models where you don’t have access to internal probability distributions.

Implementation Details

The implementation process begins by loading a multiple choice dataset and processing each question into a standardized format. It then sends these formatted questions to the model for prediction. Rather than simply accepting the generated text at face value, the system uses regular expressions and text matching to identify which answer the model is selecting. After processing all responses, it aggregates the results and calculates overall accuracy metrics, providing a clear picture of model performance.

Common Datasets

This evaluation approach works effectively with many built-in datasets, including ARC (AI2 Reasoning Challenge), MMLU (Massive Multitask Language Understanding), TruthfulQA, HellaSwag, CEval, MedMCQA, and Reclor. Each of these datasets tests different aspects of model knowledge and reasoning ability.

Further Reading

For a detailed comparison between generative approaches (like this step) and probability-based methods (like the cloze_prompt 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 their strengths, weaknesses, and when each method is most appropriate.