Browsing: ML News
Google AI recently released Patchscopes to address the challenge of understanding and interpreting the inner workings of Large Language Models (LLMs), such as those based on…
MIT researchers have proposed a method that combines first-principles calculations and machine learning to address the challenge of computationally expensive and intractable calculations required to understand…
The abundance of web-scale textual data available has been a major factor in the development of generative language models, such as those pretrained as multi-purpose foundation…
Mathematical reasoning is vital for problem-solving and decision-making, particularly in large language models (LLMs). Evaluating LLMs’ mathematical reasoning usually focuses on the final result rather than…
Language models often need more exposure to fruitful mistakes during training, hindering their ability to anticipate consequences beyond the next token. LMs must improve their capacity…
Fine-tuning large language models (LLMs) enhances task performance and ensures adherence to instructions while modifying behaviors. However, this process incurs significant costs due to high GPU…
The evolution of artificial intelligence through the development of Large Language Models (LLMs) has marked a significant milestone in the quest to mirror human-like abilities in…
Coding-related jobs have led to the rapid advancement of Large Language Models (LLMs), with a focus on code editing. LLMs created specifically for coding jobs are…
The evaluation of jailbreaking attacks on LLMs presents challenges like lacking standard evaluation practices, incomparable cost and success rate calculations, and numerous works that are not…
Global feature effects methods, such as Partial Dependence Plots (PDP) and SHAP Dependence Plots, have been commonly used to explain black-box models by showing the average…