Alignment, bias, regulation, and responsible AI
submitted by /u/esporx [link] [comments]
RLHF trains models on human feedback. Humans rate responses they like. And it turns out humans consistently rate confident, fluent, agree...
Rep. Josh Gottheimer, who is generally tough on China, just sent a letter to Anthropic questioning their decision to reduce certain safet...
![[2411.02317] Defining and Evaluating Physical Safety for Large Language Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper explores the physical safety of Large Language Models (LLMs) in controlling robotic systems, identifying risks and proposing a...
This paper presents an adaptive differentially private federated learning framework that addresses challenges in model efficiency and sta...
This article introduces the Structured Cognitive Loop (SCL) architecture for large language model (LLM) agents, addressing key architectu...
The paper discusses how embodied AI enables drones to make adaptive landing decisions in real-time, enhancing their resilience and safety...
This paper explores causal explanations in image classification, demonstrating their formal properties and computability, while introduci...
This paper presents a scalable framework for evaluating health language models, introducing Adaptive Precise Boolean rubrics to enhance e...
This paper explores AI-assisted decision-making, focusing on how algorithms can enhance human learning through feature selection, balanci...
This paper presents M-Attack-V2, an advanced method for executing black-box attacks on Large Vision-Language Models (LVLMs) by improving ...
The paper presents MARS, a novel margin-aware reward modeling framework that enhances training efficiency by focusing on ambiguous prefer...
The paper discusses the balance between weak and strong verification methods in reasoning with large language models (LLMs), emphasizing ...
The paper presents a novel framework for statistical watermarking in machine-generated content, addressing limitations of existing method...
This paper presents a novel framework for geospatial discovery that integrates active learning and online meta-learning, focusing on rele...
This paper presents Deep-Flow, an innovative framework for anomaly detection in autonomous driving, utilizing Optimal Transport Condition...
This article evaluates the interpretability of single-cell foundation models, revealing that attention mechanisms capture co-expression r...
This paper critiques current benchmarking practices in 12-lead ECG representation learning, advocating for broader evaluation criteria to...
This article presents a novel method for training neural networks on Boolean data using Boolean threshold functions (BTF), demonstrating ...
Jolt Atlas introduces a zero-knowledge machine learning framework that enhances inference verification through lookup arguments, optimizi...
This article presents a human-centered audit of how large language models (LLMs) associate personal data with individual names, highlight...
This study presents a taxonomy for fine-grained uncertainty quantification in long-form language model outputs, highlighting effective me...
This paper explores the convergence of two-layer neural networks trained with Gaussian masked inputs, demonstrating linear convergence th...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime