In the search for happiness, having a good day every day is surely crucial. But when there are so many pursuits competing for our attention, sometimes it's difficult to know how much time to allocate for each one. Now, scientists in Canada claim to cracked the code for the perfect day – and surprisingly, it includes a short shift at work. According to the experts, the formula for the perfect day is six hours of family time, two hours spent with friends, 1.5 hour socialising, two hours exercising and one hour eating and drinking. Additionally, the perfect day should involve no more than six hours of work and less than 15 minutes commuting.

Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data. One recent solution is to train a filter module to find relevant content but only achieve suboptimal noise compression. In this paper, we propose to introduce the information bottleneck theory into retrieval-augmented generation. Our approach involves the filtration of noise by simultaneously maximizing the mutual information between compression and ground output, while minimizing the mutual information between compression and retrieved passage. In addition, we derive the formula of information bottleneck to facilitate its application in novel comprehensive evaluations, the selection of supervised fine-tuning data, and the construction of reinforcement learning rewards. Experimental results demonstrate that our approach achieves significant improvements across various question answering datasets, not only in terms of the correctness of answer generation but also in the conciseness with $2.5\%$ compression rate.

A perfect day in Los Angeles starts with a stroll along the Venice Beach boardwalk. After that, Beverly Hills, then Hollywood to see the Walk of Fame, then Griffith Park for a hike, then Chinatown for dim sum, then downtown, perhaps to catch an evening show at the Walt Disney Concert Hall. Or at least, that's what a chatbot thinks a "perfect day" is. This agenda was custom-made for me by Microsoft Copilot after I told it I had one day in town to explore the sights and asked it to plan accordingly. Here's a jam-packed 24-hour itinerary," Copilot responded, before rattling off an eight-part answer. What I didn't tell Copilot is that I already live here--and know that such an itinerary is perfect only if your idea of bliss is spending most of the day traversing one of the country's most sprawling, traffic-clogged cities, frantically popping from landmark to landmark. I asked Copilot to make me a travel itinerary because Microsoft has trotted it out as an example of how people can use the ChatGPT-like assistant. It can supposedly help you pick a destination, compare flight prices, and settle on attractions that are "popular with tourists--or just a little more off the beaten path." Of all the things you might ask a chatbot, AI companies love to suggest you ask for help planning upcoming travel. Open up ChatGPT and you might see this hypothetical prompt: "Plan a trip to see the best of New York in 3 days." Google's Gemini chatbot offers similar ones. Meta's line of chatbot assistants on Instagram and Facebook includes "Lorena," your own personal travel expert. And Rabbit, the company behind a new AI gadget, pulled out the travel example for a keynote video last month. If one were to play AI-marketing bingo, "trip itinerary" would get crossed off basically every time. More than a year into the generative-AI revolution, companies so frequently suggest that people use their tools in this way that you'd think chatbots would excel at it. In theory, chatbots that can instantaneously create travel plans are a marketer's dream. The use case is easy to understand: Planning a vacation can be a real challenge for people. First, it involves toggling among flight listings, hotel availability, and ticketing websites for major attractions. Then, it requires more nuanced research, to figure out which local restaurants are actually good and which are overpriced tourist scams, or what time to set off for a big hike that won't leave you in the woods after sunset. Most of this travel information already lives on the internet or in books, meaning that it has likely already been incorporated into a chatbot's training data. "There are probably thousands of places on webpages that describe a trip to Boston," Kathleen Creel, a professor of philosophy and computer science at Northeastern University, told me. There's people on Reddit talking about living in Boston and what they like."