AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work introduces TLXML (Task-Level eXplanation of Meta-Learning), a novel framework that extends influence functions to meta-learning settings, enabling task-level explanations of adaptation and inference, and proposes a Gauss-Newton-based approximation.
Pretrained large language models offer a practical foundation for learning useful behavior from few task-specific examples. We argue that current prompt and context optimization methods underuse the extensive knowledge and reasoning capabilities of trillion-parameter models. These capabilities can make adaptation more...
A lightweight LLM-based framework designed for joint performance prediction and feedback generation and a Low-Rank Adaptation-based parameter-efficient fine-tuning mechanism is proposed, indicating that parameter fusion and parameter-efficient fine-tuning provide effective solutions for integrating prediction and feedb...
TAILS resolves cross-task ambiguity at the representation level, while leaving the original PTM, method-specific modules, and classifier unchanged, and can improve classification and task-inference performance with modest parameter overhead and negligible inference cost.
A dependency-graph framework is proposed to formalize compositional reasoning, yielding three levels of compositionality with increasing complexity, and preliminary evidence that the decomposed-to-composed asymmetry can extend to practical settings is presented.
As large language models are increasingly deployed across diverse downstream tasks, efficient task adaptation has emerged as a central challenge. In response, a wide range of task adaptation methods have been proposed, spanning parameter-efficient fine-tuning, in-context learning, and embedding-injection approaches. Ho...
Experiments across synthetic benchmarks and a real-world robot arm control problem, together with evaluations under different acquisition functions and evolutionary multitasking, demonstrate the effectiveness and generality of ICG-MTO for few-shot multitask optimization.
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