AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
This work studies two questions: when can one model improve two objectives simultaneously, and how can many trade-offs be covered without training a separate model for each?
Text-output scores alone do not show whether quantization preserves performance on speech tasks whose target labels cannot be recovered from the transcript. We evaluate fixed mixed 4/8-bit Qwen2-Audio-7B-Instruct allocations averaging 6 and 7 bits per parameter on 508 English-to-German FLEURS utterances and on 512 RAVD...
A sequential low-rank adaptation protocol for cross-scale transfer that attains the best point-estimate MAE, RMSE, and macro-$F_1$ on both 0.6B and 1.7B backbones, outperforming target-only training and non-LLM baselines.
The study concludes that the diglossic use of SA and CA contributes to the growing body of research on digital discourse, unveiling that the digital age reshapes but does not erode diglossic boundaries, giving rise instead to a reconfigured digital diglossia.
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It is shown that, for frozen off-the-shelf encoders and language models, identity is computed when both sentences share one forward pass, and is not a property of the embedding geometry those systems ship.
This work introduces a step-by-step token extraction procedure to extract causal shortcuts from data, and applies parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts.
At test time, large language models (LLMs) can encode historical information in activation memory (i.e., KV caches) and parametric memory (i.e., updated parameters). While activation memory is generally considered effective for factual recall and parametric memory for learning new tasks, their interplay remains unclear...
Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored. Prior work has focused largely on modern language varieties and poetry, while classical prose traditions such as ma...
Closed-loop revision is increasingly used in large language model (LLM) applications, but failures may reflect incomplete feedback or ineffective responses to correct feedback. We introduce a fixed-budget revision protocol with deterministic verifiers that report all remaining violations across exact-length, lexical, a...
A gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps enables a large-scale causal analysis of attention heads, making it suitable for LLMs.
A 96-item paired benchmark in which every flawed backtest has a clean control that holds strategy, dates, code style, labels, and reporting scaffold fixed while changing one methodology detail is built.
A reference-based framework for examining coherence and diversity through three perspectives is presented: aligning their evolution with human trajectories, comparing their summaries with a human continuation of the same prompt, and estimating their likelihood under a human reference distribution.
Natural Language Processing
Our most capable vision-language-action model enhances robot dexterity, reliability, and safety
A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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