As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus.

'Sam has the sad-psycho eyes of the lost woman's boyfriend who the police have asked to front the missing person's appeal' 'Sam has the sad-psycho eyes of the lost woman's boyfriend who the police have asked to front the missing person's appeal' If he's the future, can we go backwards? His AI video generator Sora 2 has been reviled for pinching the work of others. I mean, actually do it. Go to Google images, where you can find countless photos of the OpenAI boss smiling in a kind of wan genius way, the humble lost puppy of Silicon Valley . But I urge you to simply cover the bottom half of his face in any of these pictures, and you will immediately clock that Sam has the sad-psycho eyes of the lost woman's boyfriend who the police have asked to front the missing person's appeal.

Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has rarely been studied. While a few research explored the conflicts between the inherent knowledge of LLMs and the retrieved contextual knowledge, a comprehensive assessment of knowledge conflict in LLMs is still missing.

Disentangled representation learning (DRL) aims to identify and decompose underlying factors behind observations, thus facilitating data perception and generation.

In this paper, we adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity.

This significantly improves upon prior work, which has been restricted to downsam-pled versions of MNIST and CIFAR-10. Taking CRONOS as a primitive, we then develop a new algorithm called CRONOS-AM, which combines CRONOS with alternating minimization, to obtain an algorithm capable of training multi-layer networks with arbitrary architectures.

The surge in applications of large language models (LLMs) has prompted concerns about the generation of misleading or fabricated information, known as hallucinations. Therefore, detecting hallucinations has become critical to maintaining trust in LLM-generated content. A primary challenge in learning a truthfulness classifier is the lack of a large amount of labeled truthful and hallucinated data. To address the challenge, we introduce HaloScope, a novel learning framework that leverages the unlabeled LLM generations in the wild for hallucination detection. Such unlabeled data arises freely upon deploying LLMs in the open world, and consists of both truthful and hallucinated information. To harness the unlabeled data, we present an automated membership estimation score for distinguishing between truthful and untruthful generations within unlabeled mixture data, thereby enabling the training of a binary truthfulness classifier on top. Importantly, our framework does not require extra data collection and human annotations, offering strong flexibility and practicality for real-world applications. Extensive experiments show that HaloScope can achieve superior hallucination detection performance, outperforming the competitive rivals by a significant margin.