AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work finds that likelihood-trained TPMs can result in failed generations due to overly large corrections to the LM’s logits, and trains TPMs with LM-aligned objectives that better align with the LM token-probability space.
A novel alignment framework, Reinforcement Learning for Selection Reward (RLSR), is proposed, which targets the area under the risk-coverage curve (AURC) -- a popular SP performance metric -- as its alignment objective and achieves substantially better risk-coverage trade-off compared to multiple alignment baselines on...
The experimental results show that even 130M-parameter models benefit from including the MTP task in the pre-training objective, and hold even under severe data constraints, as demonstrated on both zero-shot benchmarks and downstream tasks.
It is found that, in this benchmark, supervised fine-tuning (SFT) provides a strong baseline, substantially improving argument language consistency and end-to-end function call accuracy and, under consistent model selection, SFT achieves performance comparable to, and sometimes exceeding more complex reinforcement lear...
Pretrained language models (PLMs) have established state-of-the-art performance across diverse natural language understanding (NLU) tasks. This study reveals that seman-tic-rich explanations of lexical units can effectively guide PLM learning processes. We propose a novel language understanding enhancement method with...
This work proposes a distillation approach based on ranking supervision that consistently outperforms supervised fine-tuning as well as FKL and RKL baselines in Python code generation, multilingual generation, and data-science scenarios and offers guidance for future research in model compression.
Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be partly attributed to a limited understanding of which tokenizer properties affect which aspects of downstream performance. We introduce TokEval, a framewor...
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