The spread of misinformation online, most commonly known as fake news, is an important issue that has become more pronounced in the last two decades due to the prevalence of social media. Platforms like Twitter, Reddit, and Facebook, have been commonly identified as the main channels for propagating misinformation and have been criticized for not acting on addressing the conditions that permit the circulation and amplification of false information [32]. Such misinformation includes false claims and non fact-checked news items, that originate from sources of questionable credibility [113]. The problem of misinformation becomes critical when it pertains to healthcare and health issues, since it puts lives and the public health at risk. One of the first cases of widely spread misinformation in the medical domain is the falsehood that the MMR vaccine (Measles, Mumps, Rubella) causes autism [109]. The falsehood originated from a fraudulent article titled "Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children" published in the prestigious Lancet journal in 1998 [171, 197]. This study turned tens of thousands of parents against the vaccine, and as a result, in 2020, many countries, including the United Kingdom, Greece, Venezuela, and Brazil, lost their measles elimination status. In 2020, twenty-two years after publishing this study Lancet retracted the paper [203].

Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks crucial context necessary for accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge or constraints, which can be efficiently communicated through natural language. However, the ability of existing forecasting models to effectively integrate this textual information remains an open question. To address this, we introduce "Context is Key" (CiK), a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters, and propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings. By presenting this benchmark, we aim to advance multimodal forecasting, promoting models that are both accurate and accessible to decision-makers with varied technical expertise. The benchmark can be visualized at https://servicenow.github.io/context-is-key-forecasting/v0/ .

Large language models (LLMs), although having revolutionized many fields, still suffer from the challenging extrapolation problem, where the inference ability of LLMs sharply declines beyond their max training lengths. In this work, we conduct a theoretical analysis to better understand why No Position Encoding (NoPE) fails outside its effective range, as well as examining the power of Position Encoding (PE) in this context. Our findings reveal that with meticulous weave position, PE can indeed be extended beyond effective range. Our theorems establish that LLMs equipped with weave PE can achieve improved extrapolation performance without additional cost. Furthermore, we introduce a novel weave PE method, Mesa-Extrapolation, which utilizes a chunk-based triangular attention matrix and applies Stair PE to manage the final chunk. This method not only retains competitive performance but also offers substantial benefits such as significantly reduced memory demand and faster inference speed.

Low-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face several challenges, including the need for rank adaptivity, robustness, and computational efficiency during the fine-tuning process. We introduce GeoLoRA, a novel approach that addresses these limitations by leveraging dynamical low-rank approximation theory. GeoLoRA requires only a single backpropagation pass over the small-rank adapters, significantly reducing computational cost as compared to similar dynamical low-rank training methods and making it faster than popular baselines such as AdaLoRA. This allows GeoLoRA to efficiently adapt the allocated parameter budget across the model, achieving smaller low-rank adapters compared to heuristic methods like AdaLoRA and LoRA, while maintaining critical convergence, descent, and error-bound theoretical guarantees. The resulting method is not only more efficient but also more robust to varying hyperparameter settings. We demonstrate the effectiveness of GeoLoRA on several state-of-the-art benchmarks, showing that it outperforms existing methods in both accuracy and computational efficiency.

Open-ended text generation has become a prominent task in natural language processing due to the rise of powerful (large) language models. However, evaluating the quality of these models and the employed decoding strategies remains challenging because of trade-offs among widely used metrics such as coherence, diversity, and perplexity. Decoding methods often excel in some metrics while underperforming in others, complicating the establishment of a clear ranking. In this paper, we present novel ranking strategies within this multicriteria framework. Specifically, we employ benchmarking approaches based on partial orderings and present a new summary metric designed to balance existing automatic indicators, providing a more holistic evaluation of text generation quality. Furthermore, we discuss the alignment of these approaches with human judgments. Our experiments demonstrate that the proposed methods offer a robust way to compare decoding strategies, exhibit similarities with human preferences, and serve as valuable tools in guiding model selection for open-ended text generation tasks. Finally, we suggest future directions for improving evaluation methodologies in text generation. Our codebase, datasets, and models are publicly available.

Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopolistic industries with a history of collusion, calls for closer examination by competition authorities. In this paper, we extend the study of tacit collusion in learning algorithms from basic pricing games to more complex markets characterized by perishable goods with fixed supply and sell-by dates, such as airline tickets, perishables, and hotel rooms. We formalize collusion within this framework and introduce a metric based on price levels under both the competitive (Nash) equilibrium and collusive (monopolistic) optimum. Since no analytical expressions for these price levels exist, we propose an efficient computational approach to derive them. Through experiments, we demonstrate that deep reinforcement learning agents can learn to collude in this more complex domain. Additionally, we analyze the underlying mechanisms and structures of the collusive strategies these agents adopt.

Tesla shares saw an 8% jump after reporting its third quarter earnings on Wednesday. The electric car manufacturer was able to bounce back from a tough second quarter, beating Wall Street expectations for earnings per share. The company reported an earnings-per-share of 0.72, surpassing investors' projection of 0.60. At the end of the second quarter, Tesla's chief executive, Elon Musk, said the nearly 50% drop in profits was temporary and due to difficulty competing with cheaper or price-slashed electric vehicles by rival companies such as BYD. "We don't see this as a long-term issue," Musk said in July, "but really fairly short term."

EXCLUSIVE: Sen. Lindsey Graham is demanding answers on reporting that British International Criminal Court (ICC) prosecutor Karim Khan was accused of sexual misconduct at the same time he was pursuing criminal charges against Israeli officials. "Public reports indicate that allegations of harassment surfaced in early May--just a few days before Prosecutor Khan applied for arrest warrants against the Prime Minister and Minister of Defense of Israel for alleged violations of law during the defensive Israeli-Hamas War," Graham wrote in a letter obtained by Fox News Digital. "The timing of the allegations is troubling, and only compounds the other strong legal, jurisdictional, and prudential objections I have expressed regarding the Prosecutor's decision to seek arrest warrants." On May 20, Khan requested arrest warrants for Israeli President Benjamin Netanyahu and Defence Minister Yoav Gallant, as well as Hamas leaders Yahya Sinwar, Ismail Haniyeh and Mohammed Deif. All three Hamas leaders have been killed in the past year.

The advanced machine is about enhancing the quality of life for those who need assistance the most. Ariana Grande at the Fourth Annual Academy Museum Gala held at Academy Museum of Motion Pictures on Oct. 19, 2024 in Los Angeles, California. SOMETHING'WICKED': "Wicked" star Ariana Grande is uncertain about artificial intelligence after her co-star Cynthia Erivo felt insulted by fan edits of the poster for the upcoming musical. TECH INTERFERENCE: TikTok parent company ByteDance has confirmed it terminated an intern over the summer for allegedly sabotaging the training of an artificial intelligence model. A woman walks to cast her ballot after filling it in a privacy booth while voting in the gubernatorial election in Newark, New Jersey, on Nov. 2, 2021.

Arkansas is sitting on a 150 billion'hidden treasure' trove of lithium that could meet the global demand for EV batteries by 2030. The US Geological Survey (USGS) found between five and 19 million tons of lithium in the Smackover Formation, which is nine times the amount needed to meet the ongoing electric vehicle demand in the US by the end of the decade. The metal is a necessary component for batteries used in EVs and can be extracted from the brine wastewater from the same mines that produce oil and gas. 'Lithium is a critical mineral for the energy transition, and the potential for increased U.S. production to replace imports has implications for employment, manufacturing and supply-chain resilience,' USGS Director David Applegate said. 'This study illustrates the value of science in addressing economically important issues.'