AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Cross-Domain TTS is proposed, a novel framework that enables task-tailored scaling in broader domains and achieves an improvement of up to 17% in pass@1 accuracy while reducing inference latency and saving up to 30% in token consumption.
Abstract Background Test-time scaling has emerged as a promising method to enhance the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) during inference without additional training. While foundational studies established scaling paradigms in general domains, their applicability t...
The first compute-normalised comparison of five TTS families across five open-ended generation benchmarks spanning medicine, law, finance, general chat, and creative writing is conducted - grounded in a unified framework that decomposes the effectiveness of each method's token budget into exploration and exploitation.
It is found that prompting models to reason in English while keeping questions in low-resource languages substantially improves UE performance, suggesting that comprehension of low-resource languages is largely intact, and that the reliability bottleneck lies in generation rather than understanding.
Two novel contributions are introduced: CodeEval and CodeQual, an open-source execution framework that provides researchers with a ready-to-use evaluation pipeline for evaluating and improving LLMs in software engineering contexts, encompassing both functional correctness assessment and subjective code quality evaluati...
This work conducts a fine-grained analysis of hallucinations arising in LLM reasoning and finds that the reasoning traces are particularly prone to Context-Sensitive Factual Hallucinations: cases where the model actually has the relevant knowledge, yet makes factual errors due to contextual interference during reasonin...
This paper proposes symbolic preference distillation (SyPD), a reasoning optimization strategy that leverages specialized domain knowledge to perform SLMs' error analysis and generate symbolic knowledge via a teacher, and proposes a preference distillation method that guides an SLM to align with high-quality and clinic...
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