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Enhancing User Agency in Generative Language Models: Algorithmic Recourse for Toxicity Filtering
Generative Language Models (GLMs) are being increasingly integrated into various sectors, including customer service and content creation, which necessitates maintaining a balance between moderation and freedom of expression. Hence, the need for a sophisticated...
This AI Paper Introduces SafeEdit: A New Benchmark to Investigate Detoxifying LLMs via Knowledge Editing
As Large Language Models (LLMs) like ChatGPT, LLaMA, and Mistral continue to advance, concerns about their susceptibility to harmful queries have intensified, prompting the need for robust safeguards. Approaches such as supervised fine-tuning (SFT), reinforcement...
Researchers from Imperial College and GSK AI Introduce RAmBLA: A Machine Learning Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
As advanced models, large Language Models (LLMs) are tasked with interpreting complex medical texts, offering concise summaries, and providing accurate, evidence-based responses. The high stakes associated with medical decision-making underscore the paramount...
MathVerse: An All-Around Visual Math Benchmark Designed for an Equitable and In-Depth Evaluation of Multi-modal Large Language Models (MLLMs)
The performance of multimodal large Language Models (MLLMs) in visual situations has been exceptional, gaining unmatched attention. However, their ability to solve visual math problems must still be fully assessed and comprehended. For this reason, mathematics often...
Engineering household robots to have a little common sense
Engineers aim to give robots a bit of common sense when faced with situations that push them off their trained path, so they can self-correct after missteps and carry on with their chores. The team's method connects robot motion data with the common sense knowledge of...
Engineering household robots to have a little common sense
Engineers aim to give robots a bit of common sense when faced with situations that push them off their trained path, so they can self-correct after missteps and carry on with their chores. The team's method connects robot motion data with the common sense knowledge of...
