Large language models (LLMs) frequently produce source code that seems correct and well-formed, yet includes hallucinated elements that cause downstream test failures. In this study, we benchmark state-of-the-art uncertainty quantification methods and existing baselines for the task of hallucination detection in source code and introduce a diff-based pipeline to construct a code dataset annotated with line-level hallucinations. Building on this, we train a lightweight Transformer-based detector that uses LLM internal representations to identify hallucinations, substantially outperforming existing methods across several code generation domains. The detector also shows particular promise for enabling self-correction in LLM-based coding agents. We release the first publicly available dataset of line-level code hallucinations, along with the corresponding source code and trained hallucination detectors https://github.com/datapaf/CodeHallucinationDetection

Large language models (LLMs), which are primarily trained on high-resource programming languages (HRPLs), tend to perform sub-optimally for low-resource programming languages (LRPLs). This study investigates the impact of tokenizer adaptation methods on improving code generation for LRPLs. StarCoder 2 and DeepSeek-Coder models adapted to Elixir and Racket using methods such as Fast Vocabulary Transfer (FVT), FOCUS, and Zero-shot Tokenizer Transfer (ZeTT) are evaluated and compared with the original and fine-tuned models. Our experiments reveal that ZeTT outperforms other methods, achieving significant improvements in handling syntax, program logic, and data types for LRPLs. However, we also highlight performance declines in non-target languages like Python after tokenizer adaptation. The study approves the positive impact of tokenizer adaptation in enhancing LRPL code generation and suggests directions for future research, including token embeddings improvement.