Given a piece of subject text, many existing detection methods work by measuring the difficulty of LLM predicting the next token in the text from their prefix.

The claim and evidence conflict pairs can be found at https://huggingface. The scope of our dataset is purely for scientific research. Conflict V erification: Ensuring that the default and conflict evidence are contradictory. The human evaluation results showed a high level of accuracy in our data generation process. We select models with 2B and 7B parameters for our analysis. MA2 [ Touvron et al., 2023 ] is a popular open-source foundation model, trained on 2T Models with 7B and 70B parameters are selected for our analysis. To facilitate parallel training, we employ DeepSpeed Zero-Stage 3 [ Ren et al., The prompt for generating semantic conflict descriptions is shown in Figure 1 . The prompt for generating default evidence is shown in Table 6 . The prompt for generating misinformation conflict evidence is shown in Table 7 . The prompt for generating temporal conflict evidence is shown in Table 8 . The prompt for generating semantic conflict evidence is shown in Table 9 .

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.

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.

When the audio caption is provided as a prompt, V A TT achieves even more refined performance (with lowest KLD score of 1.41).

Personalization has emerged as a critical research area in modern intelligent systems, focusing on mining users' behavioral history and adapting to their preferences

The endangered baby Komodo dragons were recently born at ZooTampa. Breakthroughs, discoveries, and DIY tips sent every weekday. Though Komodo Dragons () live only on a few Indonesian islands --including the aptly named island of Komodo--they're pretty famous reptiles . That's because they are the largest and heaviest lizards in the world, growing up to eight to ten feet long and weighing between 100 and 150 pounds on average. With a forked tongue, long claws, and a strong venomous bite, they certainly live up to their mythological name.

Most existing works follow the scheme of selecting from a pre-generated pool of candidate prompts.

Large Language Models (LLMs) are being used for a wide variety of tasks.