AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 4 of 64 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Model performance for basic medication tasks was consistently poor, highlighting the need for domain-specific training through clinician-annotated datasets and a comprehensive evaluation framework for benchmarking performance.
Substantial variability exists in both DDI identification performance and uncertainty calibration across large language models, and prompt design alone was insufficient to consistently improve knowledge-aware uncertainty expression.
It is found that sensitivity to demographic cues is distributed across internal units and varies substantially across models, suggesting that effective mitigation requires understanding distributed, task-specific mechanisms rather than manipulating a small set of identified neurons alone.
A systematic study spanning five KG task formulations, three training paradigms, two KGs, and three base LLMs finds that at the task level, all paradigms improve over the non-finetuned baseline, but methods with comparable in-domain accuracy show substantially different knowledge transfer behavior.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.