Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim.

Exploiting symmetry inherent in data can significantly improve the sample efficiency of a learning procedure and the generalization of learned models. When data clearly reveals underlying symmetry, leveraging this symmetry can naturally inform the design of model architectures or learning strategies. Yet, in numerous real-world scenarios, identifying the specific symmetry within a given data distribution often proves ambiguous. To tackle this, some existing works learn symmetry in a data-driven manner, parameterizing and learning expected symmetry through data. However, these methods often rely on explicit knowledge, such as pre-defined Lie groups, which are typically restricted to linear or affine transformations.

Existing benchmarks for evaluating LLMs' cultural sensitivities are limited to a single language or collected from online sources such as Wikipedia, which do not reflect the mundane everyday lifestyles of diverse regions. That is, information about the food people eat for their birthday celebrations, spices they typically use, musical instruments youngsters play, or the sports they practice in school is common cultural knowledge but uncommon in easily collected online sources, especially for underrepresented cultures.

As societal awareness of climate change grows, corporate climate policy engagements are attracting attention. We propose a dataset to estimate corporate climate policy engagement from various PDF-formatted documents. Our dataset comes from LobbyMap (a platform operated by global think tank InfluenceMap) that provides engagement categories and stances on the documents. To convert the LobbyMap data into the structured dataset, we developed a pipeline using text extraction and OCR. Our contributions are: (i) Building an NLP dataset including 10K documents on corporate climate policy engagement.

A.1 Multimodal Large Language Models Building on the foundational capabilities of groundbreaking Large Language Models (LLMs) such as GPT [3], PALM [6], Mistral [49], and LLama [108], which excel in language understanding and reasoning, recent innovations have integrated these models with other modalities (especially vision), leading to the development of Multimodal Large Language Models (MLLMs). These advanced MLLMs combine and process visual and textual data, demonstrating enhanced versatility in addressing both traditional vision tasks [21, 40, 42, 133] and complex multimodal challenges [34, 70, 136]. Among all MLLMs, proprietary models consistently perform well. OpenAI's GPT-4-Vision [82] pioneered this space by adeptly handling both text and image content. Anthropic's Claude 3 series [7] integrates advanced vision capabilities and multilingual support, enhancing its application across diverse cognitive and real-time tasks.

Figure 9: Example showing how a single line of HTML code is rendered by a browser's renderer. In this example, we can see that the tags

delimit different blocks which are therefore spaced by line breaks while other tags, such as , are rendered on the same line of text that precedes and follows them.

Unless otherwise specified all algorithms were timed on single core versions even though, for instance, the proposed method is in places trivially parallelizable (i.e. during forest build). An exception was the grid search across meta-parameters to find the best (optimal) reference model where parallelization was used when required as this stage does not form part of the time comparisons. Hosted on Google Colaboratory they enable the use of hosted or local runtime environments. When tested hosted runtimes were running Python 3.6.9 Please note that while a hosted runtime can be used for ease of replication, all timings reported in the paper were based on using a local runtime environment as previously indicated NOT a hosted environment. The notebooks, when run in the hosted environment will automatically install the required packages developed as part of this work.

President Donald Trump evoked Elon Musk during his Oval Office meeting with South Africa's president on Wednesday, during talks about the ongoing attacks white farmers in the country are facing. Trump went back and forth with President Cyril Ramaphosa over whether what is occurring in South Africa is indeed a "genocide" against white farmers. At one point, during the conversation, a reporter asked Trump how the United States and South Africa might be able to improve their relations. The president said that relations with South Africa are an important matter to him, noting he has several personal friends who are from there, including professional golfers Ernie Els and Retief Goosen, who were present at Tuesday's meeting, and Elon Musk. President Donald Trump and Elon Musk attend a UFC 309 at Madison Square Garden last November. Unprompted, Trump added that while Musk may be a South African native, he doesn't want to "get [him] involved" in the ongoing foreign diplomacy matters that played out during Tuesday's meeting.

It sure looks like Google is prepping its controversial AI mode for primetime. This week, some Google users noticed an AI Mode button showing up instead of Google's iconic "I'm feeling lucky" button on the homepage. And today, a Mashable reporter spotted "AI Mode" appearing as an option on search results pages, alongside stalwart Google tools like News, Shopping, Images, and Videos. Notably, this reporter did not proactively sign up to participate in AI Mode through Google Labs. That suggests Google is testing the feature for select users.

Yesterday, we reported on a bizarre glitch from xAI's chatbot Grok, which began adding commentary about "white genocide" in South Africa into random conversations about baseball and HBO Max. And last night, xAI -- the artificial intelligence arm of Elon Musk's X -- finally admitted it had a problem. In a post on X, the company promised to conduct a full investigation into the glitch, blaming it on "an unauthorized modification" that directed Grok "to provide a specific response on a political topic." Coincidentally, Musk, the leader of xAI and a Grok power user, has a known interest in the subject. In fact, he spent yesterday tweeting about white genocide in South Africa, his home country.