A hazard analysis framework for code synthesis large language models

Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies

arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.

Evaluating large language models trained on code

TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models

arXiv:2607. 01235v1 Announce Type: cross Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners.

Minionese: Comprehensive Benchmark and Mechanistic Study of Multilingual LLM Safety

arXiv:2607. 10112v1 Announce Type: cross Abstract: Safety alignment in large language models remains brittle across languages: prompts reliably refused in English can elicit harmful compliance in non-English and low-resource settings.

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion

arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.

Lessons learned on language model safety and misuse

We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.

Measuring and Mitigating Bias in Code Generated by Large Language Models

arXiv:2606. 00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation tasks.

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

arXiv:2607. 18260v1 Announce Type: new Abstract: We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis.

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback

arXiv:2507. 22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation.

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.

A research agenda for assessing the economic impacts of code generation models

Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System

arXiv:2512. 02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or language translation.