Fossil evidence shows a case of mistaken big cat identity. Breakthroughs, discoveries, and DIY tips sent six days a week. Present-day Japan may see its fair share of bears, but the islands' big cat populations are long gone. Between 129,000 and 11,700 years ago, temporary land bridges allowed the ancient predators to migrate between mainland Asia and the islands. Paleobiologists have long believed tigers were the primary cats to make this trek, but recently analyzed evidence published in the suggests a different timeline.

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 .

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.

Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited.

Environment Climate Change Antarctica has a'gravity hole' The geological oddity has existed since dinosaurs roamed the Earth. Breakthroughs, discoveries, and DIY tips sent six days a week. A "gravity hole" beneath Antarctica sounds like the plot to a bad sci-fi movie, but it's a very real situation deep beneath the Earth's surface stretching back tens of millions of years. The phenomenon thankfully isn't as apocalyptic as it sounds, either. In fact, researchers say these complex interactions between rock densities, gravitational pull, and sea levels are actually helping them understand how the southernmost continent's ice sheets evolved, and what their influences mean for the planet's climate.