When the Netflix anthology series "Beef" premièred, in 2023, it was a revelation in more ways than one. The show, which traced the depths into which two Angelenos descend after a road-rage incident, reintroduced Ali Wong as a dramatic lead, gave Steven Yeun a chance to go darkly comic, and shined a rare light on the issue of Asian American mental health. It also remade the career of its creator, Lee Sung Jin, a seeming overnight success who actually had nearly two decades of TV-comedy writing under his belt. Lee first pitched the show after he stalked another driver for a half hour following a parking-lot dispute; he similarly drew from life for Season 2, which stars Oscar Isaac as Josh, a country-club manager, and Carey Mulligan as his interior-designer wife, Lindsay. The couple are caught on video having a nasty fight by two members of his staff, Austin (Charles Melton) and Ashley (Cailee Spaeny). The Gen Z employees, about to embark on their own marriage, see the footage as blackmail material--and thus an opportunity to start their next chapter on secure financial footing. As in the first season, the story quickly broadens beyond the central conflict, roping in the club's new billionaire owner, Chairwoman Park (Youn Yuh-jung), her unreliable plastic-surgeon husband, and the seething resentments of both the haves and the have-nots. I met Lee earlier this month, at his new office in Hollywood. The space was sparsely decorated, but he'd already mounted posters for "Beef"; "It's Always Sunny in Philadelphia," the show that gave him his start in the industry; and "Thunderbolts," a 2025 Marvel movie directed by his creative partner, Jake Schreier. Lee, who has gone by Sonny since childhood and was credited as Sonny Lee for the first half of his career, opened up about the long road to "Beef"--a journey toward more intentional storytelling, as well as feeling "O.K. in my own skin." Perhaps surprisingly, the "Beef" character he seemed to relate to most was Josh, a congenial go-getter who mires himself in workaholism to avoid addressing his grief, as Lee did when one of his dogs died suddenly during production. We talked about his method of tailoring dialogue to his actors, the differences between Korean and American billionaires, and why class and capitalism are such inescapable themes on TV today.

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations.

RWDS Big Questions: how do we balance innovation and regulation in the world of AI? AI development is accelerating, while regulation moves more deliberately. That tension creates a core challenge: how do we maintain momentum without breaking the things that matter? The aim isn't to slow innovation unnecessarily, but to ensure progress happens at a pace that protects individuals and society. Responsible actors should not be disadvantaged -- yet safeguards are essential to maintain trust. For the latest video in our RWDS Big Questions series, our panel explores this delicate balance.

Traditionally, each component of task-oriented dialogue systems is trained independently with different supervision.

In addition, users may become overly dependent on the model's outputs For the feedback, we ask the person "Please consider the quality of the Given a score (1-5). 1 means its quality is bad, and 5 means its quality is very good". The interface of the user study is shown in Fig. A1. We report the average scores in Tab. We have a total of 1.1M training data in FIRE. In Fig. A2, we present the curves of A T, A TR, A TR, and RR using different Results show that more data leads to better performance.

T5) to generate an auxiliary directional stimulus prompt for each input instance.

Large language models (LLMs) are considered a crucial technology for advancing intelligent education since they exhibit the potential for an in-depth understanding of teaching scenarios and providing students with personalized guidance. Nonetheless, current LLM-based application in personalized teaching predominantly follows a "Question-Answering" paradigm, where students are passively provided with answers and explanations. In this paper, we propose SocraticLM, which achieves a Socratic "Thought-Provoking" teaching paradigm that fulfills the role of a real classroom teacher in actively engaging students in the thought