Language Model Loss Evaluation

The compute_lm_loss step evaluates models by measuring their language modeling loss - how well they predict the next token in a sequence.

Overview

Language model loss is a fundamental metric for assessing the quality of language models. This approach measures a model’s ability to assign high probabilities to correct tokens in context, providing a direct measure of how well the model has learned language patterns.

Key Features

Foundation Model Assessment: Evaluates the core language modeling ability of LLMs
Domain Adaptation Measurement: Can assess how well a model performs on specific domains
Perplexity Calculation: Computes perplexity, a standard metric in language modeling
Fine-grained Analysis: Can identify specific contexts where a model struggles

When to Use

Use this step when you want to:

Assess the fundamental predictive capabilities of language models
Compare base model quality without relying on format-specific outputs
Measure domain-specific adaptation of language models
Identify specific weaknesses in a model’s prediction abilities

Implementation Details

Internally, this step:

Prepares text sequences for evaluation
Computes token-by-token loss for each sequence
Aggregates losses across sequences into metrics like average loss or perplexity
Can be applied to both complete sequences or specific target tokens

Technical Considerations

This evaluation method requires access to token probabilities from the model, making it primarily suitable for local language models where these outputs are accessible.