Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical questions. Recent efforts to address this issue have primarily focused on leveraging mathematical datasets through supervised fine-tuning or self-improvement techniques. However, these methods often depend on high-quality datasets that are difficult to prepare, or they require substantial computational resources for fine-tuning. Inspired by findings that LLMs know how to produce the right answer but struggle to select the correct reasoning path, we propose a purely inference-based searching method -- MindStar (M*). This method formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. We evaluate the M* framework on both the GSM8K and MATH datasets, comparing its performance with existing open and closed-source LLMs. Our results demonstrate that M* significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1, but with substantially reduced model size and computational costs.

Characters are integral to long-form narratives, but are poorly understood by existing story analysis and generation systems. While prior work has simplified characters via graph-based methods and brief character descriptions, we aim to better tackle the problem of representing complex characters by taking inspiration from advice given to professional writers. We propose CHIRON, a new `character sheet' based representation that organizes and filters textual information about characters. We construct CHIRON sheets in two steps: a Generation Module that prompts an LLM for character information via question-answering and a Validation Module that uses automated reasoning and a domain-specific entailment model to eliminate false facts about a character. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. We also show that metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.

The rise of artificial intelligence in our media and entertainment industries has raised a lot of concerns about programs like Open Al's text-to-video maker Sora replacing the artistic endeavors and aspirations of humans. If those AI made movies are anything like a new brand film about the Toys'R' Us toy store chain's origin story, the only thing we'll have to fear is watching them. Toys'R' Us's current owner WHP Global worked with the Emmy nominated creative agency Native Foreign to create a short brand film called The Origin of Toys'R' Us using OpenAI's text-to-video creator Sora. The film premiered at the 2024 Cannes Lions International Festival of Creativity and can currently be viewed on the toy retailer's website. The Origin of Toys'R' Us is only a little over a minute long but it's a mix of confusing and eerie.

Reddit has a warning for AI companies and other scrapers: play by our rules or get blocked. The company said in an update that it plans to update its Robots Exclusion Protocol (robots.txt The company said it will also continue to block and rate-limit crawlers and other bots that don't have a prior agreement with the company. The changes, it said, shouldn't affect "good faith actors," like the Internet Archive and researchers. Reddit's notice comes shortly after multiple reports that Perplexity and other AI companies regularly bypass websites' robots.txt Perplexity's CEO, in a recent interview with Fast Company, said that the protocol is "not a legal framework."

OpenAI plans to block people from using ChatGPT in China, a country where its services aren't officially available, but where users and developers access it via the company's API anyway. Securities Times, a Chinese state-owned newspaper reported on Tuesday that OpenAI had started sending emails to users in China outlining its plans to block access starting July 9, according to Reuters. "We are taking additional taps to block API traffic from regions where we do not support access to OpenAI's services," an OpenAI spokesperson told the publication. The move could impact several Chinese startups which have built applications using OpenAI's large language models. Although OpenAI's services are available in more than 160 countries, China isn't one of them.

The original Razr was always sort of a style icon. It was super thin (almost to a fault) and looked more like a prop from a sci-fi movie than all its brick-shaped rivals at the time. And with the launch of a new generation of its signature flip phone later this summer, it feels like Moto is trying to recapture some of that magic with the 2024 Razr family. Just like last year, the Razr line is split between two devices: the flagship Razr, which starts at 1,000, and a more affordable non-plus model that starts at 700. That said, Motorola has made some significant changes to help close the gap between the two phones.

The world's biggest record labels are suing two artificial intelligence startups, taking an aggressive stance to protect their intellectual property against technology that makes it easy for people to generate music based on existing songs. The Recording Industry Association of America said it filed twin lawsuits Monday against Suno AI and Uncharted Labs Inc., the developer of Udio AI, on behalf of Universal Music Group NV, Warner Music Group Corp. and Sony Music Entertainment. The RIAA, a trade group for record labels, is seeking damages of as much as 150,000 "per work infringed." That could amount to potentially billions of dollars. "The music community has embraced AI, and we are already partnering and collaborating with responsible developers to build sustainable AI tools centered on human creativity that put artists and songwriters in charge," Mitch Glazier, chief executive officer of the RIAA, said in a statement.

Learning musical structures and composition patterns is necessary for both music generation and understanding, but current methods do not make uniform use of learned features to generate and comprehend music simultaneously. In this paper, we propose PianoBART, a pre-trained model that uses BART for both symbolic piano music generation and understanding. We devise a multi-level object selection strategy for different pre-training tasks of PianoBART, which can prevent information leakage or loss and enhance learning ability. The musical semantics captured in pre-training are fine-tuned for music generation and understanding tasks. Experiments demonstrate that PianoBART efficiently learns musical patterns and achieves outstanding performance in generating high-quality coherent pieces and comprehending music. Our code and supplementary material are available at https://github.com/RS2002/PianoBart.

Multilingual automatic lyrics transcription (ALT) is a challenging task due to the limited availability of labelled data and the challenges introduced by singing, compared to multilingual automatic speech recognition. Although some multilingual singing datasets have been released recently, English continues to dominate these collections. Multilingual ALT remains underexplored due to the scale of data and annotation quality. In this paper, we aim to create a multilingual ALT system with available datasets. Inspired by architectures that have been proven effective for English ALT, we adapt these techniques to the multilingual scenario by expanding the target vocabulary set. We then evaluate the performance of the multilingual model in comparison to its monolingual counterparts. Additionally, we explore various conditioning methods to incorporate language information into the model. We apply analysis by language and combine it with the language classification performance. Our findings reveal that the multilingual model performs consistently better than the monolingual models trained on the language subsets. Furthermore, we demonstrate that incorporating language information significantly enhances performance.

We are exposed to much information trying to influence us, such as teaser messages, debates, politically framed news, and propaganda - all of which use persuasive language. With the recent interest in Large Language Models (LLMs), we study the ability of LLMs to produce persuasive text. As opposed to prior work which focuses on particular domains or types of persuasion, we conduct a general study across various domains to measure and benchmark to what degree LLMs produce persuasive text - both when explicitly instructed to rewrite text to be more or less persuasive and when only instructed to paraphrase. To this end, we construct a new dataset, Persuasive-Pairs, of pairs each consisting of a short text and of a text rewritten by an LLM to amplify or diminish persuasive language. We multi-annotate the pairs on a relative scale for persuasive language. This data is not only a valuable resource in itself, but we also show that it can be used to train a regression model to predict a score of persuasive language between text pairs. This model can score and benchmark new LLMs across domains, thereby facilitating the comparison of different LLMs. Finally, we discuss effects observed for different system prompts. Notably, we find that different 'personas' in the system prompt of LLaMA3 change the persuasive language in the text substantially, even when only instructed to paraphrase. These findings underscore the importance of investigating persuasive language in LLM generated text.