Supervised and preference-based fine-tuning techniques have become popular for aligning large language models (LLMs) with user intent and correctness criteria. However, real-world training data often exhibits spurious correlations -- arising from biases, dataset artifacts, or other "shortcut" features -- that can compromise a model's performance or generalization. In this paper, we systematically evaluate three post-training algorithms -- Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and KTO (Kahneman-Tversky Optimization) -- across a diverse set of synthetic tasks and spuriousness conditions. Our tasks span mathematical reasoning, constrained instruction-following, and document-grounded question answering. We vary the degree of spurious correlation (10% vs. 90%) and investigate two forms of artifacts: "Feature Ambiguity" and "Distributional Narrowness." Our results show that the models often but not always degrade under higher spuriousness. The preference-based methods (DPO/KTO) can demonstrate relative robustness in mathematical reasoning tasks. By contrast, SFT maintains stronger performance in complex, context-intensive tasks. These findings highlight that no single post-training strategy universally outperforms in all scenarios; the best choice depends on the type of target task and the nature of spurious correlations.

The emergence of Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in the generation of large language models (LLMs), yet it still reveals numerous limitations. When a general-purpose LLM serves as the RAG generator, it often suffers from inadequate response informativeness, response robustness, and citation quality. Past approaches to tackle these limitations, either by incorporating additional steps beyond generating responses or optimizing the generator through supervised fine-tuning (SFT), still failed to align with the RAG requirement thoroughly. Consequently, optimizing the RAG generator from multiple preference perspectives while maintaining its end-to-end LLM form remains a challenge. To bridge this gap, we propose Multiple Perspective Preference Alignment for Retrieval-Augmented Generation (PA-RAG), a method for optimizing the generator of RAG systems to align with RAG requirements comprehensively. Specifically, we construct high-quality instruction fine-tuning data and multi-perspective preference data by sampling varied quality responses from the generator across different prompt documents quality scenarios. Subsequently, we optimize the generator using SFT and Direct Preference Optimization (DPO). Extensive experiments conducted on four question-answer datasets across three LLMs demonstrate that PA-RAG can significantly enhance the performance of RAG generators. Our code and datasets are available at https://github.com/wujwyi/PA-RAG.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Formula 1 and Amazon Web Services (AWS) have been partners for more than six years. But, that longstanding partnership is now set to reach new heights as the popular sports league and the leading tech company will leverage AWS tools to develop a generative artificial intelligence-designed trophy for the upcoming Canadian Grand Prix. The first-of-its-kind approach to the trophy for the highly-anticipated event is expected to help increase creativity.

'The greatest track on Earth' finally finishes up at the Interlagos Circuit of the São Paulo Grand Prix. It features the Senna'S', an S-shaped part of the track named after the legendary Brazilian racing driver Ayrton Senna. Look closely and you'll see a statue of Senna, who was tragically killed at the 1994 San Marino Grand Prix when his car crashed into a concrete barrier. Bringing the AI track to an end in Brazil, the last section runs from Turn 14, known as Junção, and into Interlagos' final sector. Sky Sports, which has exclusive broadcast rights to live F1 races, is trying to entice fans to subscriptions before the Grand Prix season starts next month. The 2024 calendar comprises a record 24 Grands Prix, starting with the Bahrain Grand Prix on March 2. The Senna'S', named after the legendary Ayrton Senna, is renowned as one of Formula 1's most iconic overtaking spots Bringing the race to an end in Brazil, the thirteenth section of'The Greatest Track On Earth' runs from Turn 14, known as Junção, and into Interlagos' final sector Not content with winning trophies in real life, McLaren is now competing in the virtual world for F1 glory. The legendary British automobile company entered the world of eSports in 2017 and won its first tournament in December last year. With two Brits on the team, McLaren saw off fierce competitors including Mercedes-Benz, Aston Martin, Red Bull Racing and Haas. MailOnline has taken a trip to the global headwaters of McLaren in Woking, Surrey, to see what it takes to become a professional eSports driver.

The Fédération Internationale de l'Automobile (FIA), F1's governing body, says it will employ Computer Vision tech at the season-closing Abu Dhabi Grand Prix this weekend. Drivers know the exact lines to take at corners for optimal lap times, but sometimes racers go out of bounds as they try to gain an advantage, and officials need to check cars stay within track limits. Four people had to review around 1,200 potential violations in July's Austrian Grand Prix, and some track limit violations went unpunished in October's US Grand Prix. The FIA hopes to reduce the number of possible infringements officials manually review to around 50 per race. You can get these reports delivered daily direct to your inbox.

The margin of success in Formula 1 often comes down to tiny measurements of time and distance. Drivers know the exact lines to take at corners for optimal lap times. Sometimes, though, racers will go out of bounds as they try to gain an advantage. To help officials check whether a car's wheels entirely cross the white boundary line, F1 will test an AI system. The Fédération Internationale de l'Automobile (FIA), the motorsport's governing body, says it will employ Computer Vision tech at the season-closing Abu Dhabi Grand Prix this weekend.

The 2023 Ford Mustang Mach-E is equipped with the latest semi-autonomous BlueCruise highway driving system that can drive the car under certain circumstances better than the original version. The family of Formula One star Michael Schumacher is planning to take legal action against a German weekly magazine after it published an "interview" with the racer that was generated using artificial intelligence. A spokesperson for the Schumacher family pointed to published reports of legal action, according to Reuters. Family spokesperson Sabine Kehm confirmed to The Associated Press by email on Thursday that legal action is being planned over a "fake artificial intelligence interview by German outlet Die Aktuelle." Schumacher has not been seen in public since suffering a near-fatal brain injury in a skiing accident on a French Alps vacation in December 2013.

This document focuses on modeling a complex situations to achieve an advantage within a competitive context. Our goal is to devise the characteristics of games to teach and exercise non-easily quantifiable tasks crucial to the math-modeling process. A computerized game to exercise the math-modeling process and optimization problem formulation is introduced. The game is named The Formula 1 Championship, and models of the game were developed in the computerized simulation platform MoNet. It resembles some situations in which team managers must make crucial decisions to enhance their racing cars up to the feasible, most advantageous conditions. This paper describes the game's rules, limitations, and five Formula 1 circuit simulators used for the championship development. We present several formulations of this situation in the form of optimization problems. Administering the budget to reach the best car adjustment to a set of circuits to win the respective races can be an approach. Focusing on the best distribution of each Grand Prix's budget and then deciding how to use the assigned money to improve the car is also the right approach. In general, there may be a degree of conflict among these approaches because they are different aspects of the same multi-scale optimization problem. Therefore, we evaluate the impact of assigning the highest priority to an element, or another, when formulating the optimization problem. Studying the effectiveness of solving such optimization problems turns out to be an exciting way of evaluating the advantages of focusing on one scale or another. Another thread of this research directs to the meaning of the game in the teaching-learning process. We believe applying the Formula 1 Game is an effective way to discover opportunities in a complex-system situation and formulate them to finally extract and concrete the related benefit to the context described.

Avatars: In this year's inaugural Grands Prix, the locations for which are yet to be revealed, 'telerobotic avatars' named'The Aviators' will sit in the'octocopter' race car The craft sports eight rotor blades surrounding a central carbon-fibre cockpit and is capable of going from 0-62 miles per hour in 2.8 seconds. Lewis Hamilton in a Mercedes F1 car would be able to do the same in around 2.6 seconds

The roar inside a packed stadium is felt more than heard, a kind of whole-body buzz. As the announcer on the PA brings the crowd to a crescendo, techno music pumping and lights strafing our heads, distant figures file onto the stage, sit in front of keyboards and PC screens, and fit helicopter-grade headphones over their ears to shut out the sound of 10,000 people chanting their names. Two years ago I traveled to Katowice, Poland, to make a short video documentary about e-sports. IEM 2018 was the biggest yet, with a million-dollar prize pot and around 100,000 fans turning up to cheer on their favorite teams. This year, those teams played in silence.