API specifications serve as critical documentation for modern software development, directly impacting integration efficiency and client code generation. While recent advancements have leveraged Large Language Models (LLMs) to generate initial specifications from source code, existing approaches treat this process as a...
Yong-Jian Tang, C. Moraru, Doruk Tuncel et al.· 0 citations
Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering to materials informatics. However, their logical reasoning often falls short, producing factual inaccuracies unacceptable in these settings. Reliable evaluation remains...
Nishtha N. Vaidya, S. Grimm, T. Hubauer et al.· 0 citations
This study systematically evaluates self-evaluation-based uncertainty quantification across different language models of varying sizes on question-answering tasks spanning general to specialized knowledge domains. Using various self-evaluation methods where models judge their own predictions, we examine how model scale...
Idil Kapikiran, Thomas Decker, Thomas A. Runkler· 0 citations
Overall, AutoCoC contributes a reproducible and scalable alternative to expert-authored concept hierarchies by automatically deriving domain-specific prompt structures from documentation, making concept-based prompting more practical for structured knowledge generation.
Hurile Borjigin, Nishtha N. Vaidya, Thomas A. Runkler et al.· SKILLED-LLMs@FLoC· 0 citations
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