Showing 2024Show all
3 papers · 1 filter
cs.SE2024★ 3 cited
Can LLMs Replace Manual Annotation of Software Engineering Artifacts?
Toufique Ahmed, Premkumar Devanbu, Christoph Treude +1
Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain gr…
cs.SE2024
Calibration and Correctness of Language Models for Code
Claudio Spiess, David Gros, Kunal Suresh Pai +6
Machine learning models are widely used, but can also often be wrong. Users would benefit from a reliable indication of whether a given output from a given model should be trusted,…
cs.SE2024
De-Hallucinator: Mitigating LLM Hallucinations in Code Generation Tasks via Iterative Grounding
Aryaz Eghbali, Michael Pradel
Large language models (LLMs) trained on datasets of publicly available source code have established a new state of the art in code generation tasks. However, these models are mostl…