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Specific examples of NLP tasks suitable for evaluating long-context models include summarizing single or multiple long documents, answering questions based on lengthy texts, and completing code within large codebases.

Examples of Real-World NLP Tasks for Long-Context Evaluation

A software company is testing a new AI assistant designed to help developers work with massive codebases. To evaluate the model's ability to understand the context of an entire software project (consisting of hundreds of interconnected files), which of the following tasks would be the most effective measure of its long-context capabilities?

Based on the following scenario, propose a specific task that the legal firm could use to evaluate the AI assistant's ability to process and reason over the entire document set. Justify why your proposed task is an effective measure for this purpose.

AI Evaluation for a Legal Firm

Match each real-world scenario with the specific Natural Language Processing (NLP) task that would be most appropriate for evaluating a model's ability to handle the long-context information presented.

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