AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Fine-tuning adapts aligned large language models (LLMs) to downstream tasks, but a few dozen harmful examples can remove their refusal of harmful requests. Prior work localizes safety-related behavior to specific layers, directions, and tokens, suggesting targets for protection. We test whether successful localization...
Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may recall a relation in one direction while failing to generate the answer in the reverse direction. Distillation from its generated answers can therefore propagate this dire...
CultureConverse is introduced, a scalable, multilingual simulation and evaluation harness for culturally grounded assistant dialogue that covers 10 East and Southeast Asian regions, 58 subgroup identities, and 7 domains and performance gains from fine-tuning on 27,860 high-quality CultureConverse-DS samples improve in-...
A byte crossover is derived, the context length at which the two savings are equal, together with ideal speedup bounds for each branch and for their composition, from model dimensions and keep ratios alone and is derived at https://github.com/js-lee-AI/ByteCross.
This paper proposes Cross-lingual Ranking Preference Optimization~ (CRPO), a novel framework that leverages robust preference knowledge from English to facilitate preference alignment in the target language, thereby enhancing language adaptation and output quality.
This work releases LAMAR, a language aware multilingual cross encoder trained to account for both semantic relevance and language coherence, which achieves the best performance overall and across all languages examined individually on general multilingual reranking benchmarks.
This work shows that training a strong instruction-tuned reasoning model on its own answer-conditioned chains sharply lowers its verifiable-reasoning accuracy, and generates answer-blind data, because no correctness filter can see this damage in the data.
CultureConverse is introduced, a scalable, multilingual simulation and evaluation harness for culturally grounded assistant dialogue that covers 10 East and Southeast Asian regions, 58 subgroup identities, and 7 domains and performance gains from fine-tuning on 27,860 high-quality CultureConverse-DS samples improve in-...
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