Unveiling Inspect: A Framework for Evaluating Large Language Models

The UK AI Safety Institute has developed an open source framework called Inspect to evaluate large language models in a flexible and extensible manner. Inspect offers a range of features for designing and conducting evaluations that assess various aspects of an LLM’s performance, including its capabilities, limitations, robustness, and safety.

One notable feature of Inspect is its functionality for prompt engineering, which allows researchers to systematically vary inputs to the models and examine how these variations affect performance. This can help uncover sensitivities and inconsistencies in how models respond to different prompts, instructions, and examples.

Inspect also supports granting models access to Python functions, called “tools,” which can provide external information or enable the model to take actions. Researchers can define custom tools and control when models have access to them during an evaluation. This feature facilitates testing a model’s ability to use tools effectively to augment its knowledge and capabilities.

Another key aspect of Inspect is its support for multi-turn dialogue, which is crucial for evaluating models in conversational scenarios that mirror real-world applications. Researchers can simulate chat interfaces, interactive tools, and other multi-turn interactions to assess a model’s performance in open-ended dialogue.

Inspect enables the creation of model-graded evaluations, where one model assesses the output of another by scoring, critiquing, or comparing it to an expected answer. This is achieved through customisable prompts and templates, allowing for novel evaluation setups.

The framework offers flexibility in dataset integration, supporting various data sources such as local files, Hugging Face datasets, and programmatic generation of evaluation samples via custom Python functions. This adaptability allows researchers to tailor evaluations to their specific needs and use cases.

Under the hood, Inspect utilises an asynchronous architecture to efficiently run evaluations in parallel across multiple samples and models. It can automatically batch requests to optimise performance while respecting rate limits, enabling large-scale evaluations.

However, it is important to recognise the scope and limitations of Inspect. The framework is primarily designed for evaluating models, not training or fine-tuning them. While it can benchmark open-source models through the Hugging Face integration, Inspect mainly evaluates models via HTTP APIs rather than running the large models directly.

Inspect is a Python framework intended for use through scripts, notebooks, and IDEs. Although it includes a log viewer for analysing results, it does not provide a graphical user interface for creating evaluations, which may limit its accessibility for some users. Although if this library proves popular I’d expect UIs to pop up to cater for this.

Inspect is a powerful tool for researchers and practitioners seeking to thoroughly evaluate large language models. Its flexible and extensible design allows for the creation of diverse and targeted evaluations, helping to uncover insights into model performance, identify potential issues, and promote transparency in the capabilities and behaviours of LLMs.

As LLMs continue to advance and find application in various domains, frameworks like Inspect will play a significant role in facilitating responsible development and deployment. It is crucial to consider Inspect’s strengths and limitations, as well as the technical expertise required to effectively leverage its capabilities. While Inspect offers a robust set of features, it may not be the optimal choice for every evaluation scenario or user.

As the field of AI continues to evolve, it will be interesting to observe how Inspect and similar frameworks adapt to the changing landscape of LLMs and their evaluation needs. The UK AI Safety Institute has made a valuable contribution with Inspect, but there remains a lot of room for further development and refinement to address the diverse challenges posed by the advancements of AI.