arXiv:2608. 08266v1 Announce Type: cross Abstract: Code generated by modern language models often reads naturally.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?
arXiv:2606. 28574v1 Announce Type: cross Abstract: When a large language model (LLM) codes a construct in text as a human annotator would, that agreement makes the LLM a reliable coder.
arXiv:2608. 04439v1 Announce Type: cross Abstract: Large language models (LLMs) have made notable progress in code generation, but they still struggle on challenging tasks that require sophisticated algorithms or complex implementations.
The thesis explores Introspective Uncertainty Estimation (IUE) for large language models (LLMs) in code generation, aiming to determine whether hidden-state representations can indicate functional correctness at both response and line levels. Using LiveCodeBench and BigCodeBench, the study finds that hidden states provide a strong signal for overall correctness, with static single-token probes performing best, while dynamic strategies offer no consistent advantage. Although line-level fault localization is more challenging, a conditional Top‑K ranking approach remains effective, suggesting a two‑stage workflow that first screens responses for risk and then prioritizes line‑level checks.
arXiv:2505. 13553v3 Announce Type: replace-cross Abstract: The hallucination of code generation models hinders their applicability to systems requiring higher safety standards.
arXiv:2606. 09577v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as code generators, where silently wrong programs pose real safety and reliability risks.