A rapid evidence review of evaluation techniques for large language models in legal use cases: trends, gaps, and recommendations for future research.

The legal profession faces mounting pressures, including case backlogs and limited access to legal services. Large language models (LLMs), such as OpenAI's GPT series, have been touted as potential solutions, promising to streamline tasks such as legal drafting, summarisation, analysis, and advice. Proponents argue these models can enhance efficiency, accuracy, and access to justice. However, significant risks remain. LLMs are prone to bias, factual hallucinations, and opaque reasoning processes, which can have severe consequences in high-stakes legal contexts. For responsible use in law, legal use cases must be accurately operationalised into LLM tasks that are sensitive to legal settings, as do the evaluation metrics used to evaluate LLMs performing those tasks. This paper presents a rapid literature review of LLM research in legal contexts since ChatGPT-4's release in March 2023. We examine how legal tasks are operationalised for LLMs and what evaluation metrics are used, with a focus on how these align-or fail to align-with real-world legal practice. We argue that existing studies often overlook the institutional, organisational, and professional contexts in which these tools would be deployed. This oversight limits the practical relevance of current evaluations and proposes directions for more contextually grounded research and responsible deployment strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s00146-025-02741-9.