The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them.In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.Self-Retrieval unifies all essential IR functions within a single LLM, leveraging the inherent capabilities of LLMs throughout the IR process.Specifically, Self-Retrieval internalizes the retrieval corpus through self-supervised learning, transforms the retrieval process into sequential passage generation, and performs relevance assessment for reranking.Experimental results demonstrate that Self-Retrieval not only outperforms existing retrieval approaches by a significant margin, but also substantially enhances the performance of LLM-driven downstream applications like retrieval-augmented generation.

Musk testifies at OpenAI trial it's not OK to'loot a charity' Elon Musk has taken the stand at a high-stakes trial over the future of OpenAI, casting his lawsuit against the ChatGPT maker as a defence of charitable giving. The world's richest person is suing OpenAI, its cofounder and chief executive officer, Sam Altman, and its president, Greg Brockman, and said on the stand on Tuesday that they betrayed him and the public by abandoning OpenAI's mission to be a benevolent steward of AI for humanity and transforming the nonprofit into a profit-seeking juggernaut. Musk, who founded carmaker Tesla and rocket company SpaceX, also said he is committed to serving the public by working 80-to 100-hour weeks and generally not taking vacations. "I like working and solving problems that make people's lives better," he said. Before Musk began testifying, Bill Savitt, a lawyer for OpenAI and Altman, told jurors during his opening statement it was Musk who saw dollar signs as he helped finance OpenAI's early growth and pushed it to become a for-profit business, one he might eventually lead as CEO.

Similar to natural language models, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (<0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on tokenizers or fixed k-mers to aggregate meaningful DNA units, losing single nucleotide resolution (i.e. DNA "characters") where subtle genetic variations can completely alter protein function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large language model based on implicit convolutions was shown to match attention in quality while allowing longer context lengths and lower time complexity.

Elon Musk Testifies That He Started OpenAI to Prevent a'Terminator Outcome' The judge also warned Musk and Sam Altman to curb their "propensity to use social media to make things worse outside the courtroom" after both sides traded attacks online. Elon Musk and Sam Altman appeared in a federal courtroom together for the first time on Tuesday as they fight over OpenAI's decade-long evolution and what it means for the company's future. The trial in Musk's lawsuit against Altman could result in financial damages and, more significantly, governance changes at OpenAI that may complicate its plans for an initial public offering as soon as this year. As the first witness on the stand, Musk immediately sought to frame his case as more than just about OpenAI. Siding with Altman "will give license to looting every charity in America" and shake the "entire foundation of charitable giving," Musk told a panel of nine jurors advising US District Judge Yvonne Gonzalez Rogers on how to rule.

Large language models (LLMs) are being applied to time series forecasting. But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance---in most cases, the results even improve! We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and find that patching and attention structures perform similarly to LLM-based forecasters.

A trial pitting two founders of OpenAI - Sam Altman and Elon Musk - against each other has opened in California, with the sides presenting duelling narratives about the company's history and obligations to consumers. Musk, wearing a dark suit and tie, was asked by one of his lawyers what the lawsuit was about when he took the stand. It's actually very simple, he said. It's not okay to steal a charity... If it's okay to loot a charity, the entire foundation of charitable giving will be destroyed.

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Formal verification (FV) has witnessed growing significance with current emerging program synthesis by the evolving large language models (LLMs). However, current formal verification mainly resorts to symbolic verifiers or hand-craft rules, resulting in limitations for extensive and flexible verification. On the other hand, formal languages for automated theorem proving, such as Isabelle, as another line of rigorous verification, are maintained with comprehensive rules and theorems. In this paper, we propose FVEL, an interactive Formal Verification Environment with LLMs. Specifically, FVEL transforms a given code to be verified into Isabelle, and then conducts verification via neural automated theorem proving with an LLM. The joined paradigm leverages the rigorous yet abundant formulated and organized rules in Isabelle and is also convenient for introducing and adjusting cutting-edge LLMs. To achieve this goal, we extract a large-scale FVELER. The FVELER dataset includes code dependencies and verification processes that are formulated in Isabelle, containing 758 theories, 29,304 lemmas, and 201,498 proof steps in total with in-depth dependencies.

When you purchase through links in our articles, we may earn a small commission. I asked AI to book dinner. ChatGPT, Claude, and Gemini may be aces at coding, but they're less than magical when it comes to booking a table for three. I can clearly see the day when we'll be able to summon ChatGPT, Claude, or Gemini on our phones, say something like "Hey ChatGPT, book a table for two at Outback Steakhouse tonight at 8," and ChatGPT will simply take care of it. All of the big AI providers are busy unveiling integrations for everyday services ranging from Spotify and DoorDash to AllTrails and the dinner reservation app Resy, with varying degrees of success.