In this work, we introduce the first benchmark for evaluating the capabilities of large language models (LLMs) in understanding and generating responses in Tunisian Arabic. To achieve this, we construct a dataset of Tunisian Arabic instructions and prompt ten widely-used LLMs that claim to support Arabic. We then assess the LLM responses through both human and LLM-based evaluations across four criteria: quality, correctness, relevance, and dialectal adherence. We analyze the agreement and correlation between these judgments and identify GPT-4o as our automated judge model based on its high correlation with human ratings, and generate a final leaderboard using this model. Our error analysis reveals that most LLMs struggle with recognizing and properly responding in Tunisian Arabic. To facilitate further research, we release our dataset, along with gold-standard human-written responses for all 744 instructions, and our evaluation framework, allowing others to benchmark their own models.

Writing tests for machine learning (ML) code is a crucial step towards ensuring the correctness and reliability of ML software. At the same time, Large Language Models (LLMs) have been adopted at a rapid pace for various code generation tasks, making it a natural choice for many developers who need to write ML tests. However, the implications of using these models, and how the LLM-generated tests differ from human-written ones, are relatively unexplored. In this work, we examine the use of LLMs to extract representations of ML source code and tests in order to understand the semantic relationships between human-written test functions and LLM-generated ones, and annotate a set of LLM-generated tests for several important qualities including usefulness, documentation, and correctness. We find that programmers prefer LLM-generated tests to those selected using retrieval-based methods, and in some cases, to those written by other humans.