Alignment, bias, regulation, and responsible AI
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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...
Sam Altman, CEO of OpenAI, defends AI's resource usage, dismissing water consumption concerns as unfounded and comparing AI energy use to...
The article explores an experiment where an LLM was tasked with designing a Zero-Hallucination architecture, focusing on internal problem...
The article discusses the implications of AI's self-assessment capabilities and the potential risks associated with its development, part...
Sam Altman addresses concerns over AI's resource consumption, arguing it is comparable to human energy use, while dismissing Musk's space...
![[2510.00463] On the Adversarial Robustness of Learning-based Conformal Novelty Detection](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper investigates the adversarial robustness of learning-based conformal novelty detection methods, revealing significant vulnerabi...
This article evaluates the effectiveness of textual data sanitization methods, revealing that current techniques may provide a false sens...
This article explores how different missing data mechanisms and handling techniques affect the fairness of machine learning algorithms, r...
This paper presents a method for optimizing the allocation of observations between explainable and black box models, aiming to maximize e...
The paper explores how AI discourse influences the alignment of large language models (LLMs), revealing that negative narratives can lead...
The paper presents DeRaDiff, a novel method for denoising time realignment in diffusion models, enabling efficient adjustment of regulari...
The paper introduces GRAPE (Group Representational Position Encoding), a framework for positional encoding that integrates multiplicative...
This paper presents a novel framework for incomplete multi-view clustering using Hierarchical Semantic Alignment and Cooperative Completi...
This paper introduces a probabilistic framework for certifying defenses against jailbreaking attacks on LLMs, addressing limitations of t...
This paper introduces Flexible Evidential Deep Learning (F-EDL), enhancing uncertainty quantification in machine learning by extending th...
ViGText introduces a novel approach to deepfake detection by integrating Vision-Language Model explanations with Graph Neural Networks, e...
The paper presents a holistic framework for Continual Model Merging (CMM) that addresses scalability and performance issues in continual ...
The paper discusses the concept of anthropomimetic uncertainty in language models, emphasizing the need for these models to express confi...
This article evaluates how Text-to-Image diffusion models represent historical contexts, introducing a benchmark to assess their accuracy...
The paper presents ConformalNL2LTL, a novel method for translating natural language instructions into Linear Temporal Logic (LTL) formula...
The paper presents M3OOD, a meta-learning framework designed for the automatic selection of out-of-distribution (OOD) detectors in multim...
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