Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks. To address this, Retrieval-Augmented Generation (RAG) incorporates external document chunks to expand LLM knowledge. Furthermore, compressing information from document chunks through extraction or summarization can improve LLM performance. Nonetheless, LLMs still struggle to notice and utilize scattered key information, a problem known as the "lost-in-the-middle" syndrome. Therefore, we typically need to restructure the content for LLM to recognize the key information. We propose $\textit{Refiner}$, an end-to-end extract-and-restructure paradigm that operates in the post-retrieval process of RAG. $\textit{Refiner}$ leverages a single decoder-only LLM to adaptively extract query-relevant contents verbatim along with the necessary context, and section them based on their interconnectedness, thereby highlights information distinction, and aligns downstream LLMs with the original context effectively. Experiments show that a trained $\textit{Refiner}$ (with 7B parameters) exhibits significant gain to downstream LLM in improving answer accuracy, and outperforms other state-of-the-art advanced RAG and concurrent compressing approaches in various single-hop and multi-hop QA tasks. Notably, $\textit{Refiner}$ achieves a 80.5% tokens reduction and a 1.6-7.0% improvement margin in multi-hop tasks compared to the next best solution. $\textit{Refiner}$ is a plug-and-play solution that can be seamlessly integrated with RAG systems, facilitating its application across diverse open-source frameworks.

Major League Baseball's record books will look a lot different later this week. The league is reportedly set to officially recognize statistics from the Negro Leagues and incorporate them into its own data. MLB elevated Negro League stats as "major league" in 2020, a move they said was "a longtime oversight in the game's history." Since then, MLB has been working with the Elias Sports Bureau in order to figure out a way to incorporate them into MLB's history. One player in particular, one you may have never heard of, will now be considered one of MLB's all-time greats.

The advance of Artificial Intelligence has plenty of people mapping out doomsday scenarios where the machines become self-aware and take over the world. This exercise should assuage some fears. As part of a fantasy baseball preview for our draft kit, we included ChatGPT -- an AI language model/chatbot developed by OpenAI and launched in November 2022 -- alongside our four living and breathing experts in an advice roundtable. All the participants were given the same questions, and the answers would help fantasy players make informed decisions on who to draft, who to avoid -- the usual stuff. To be clear, this was meant as a fun, dumb experiment: Could you spot the bot among five answers to the same question? I've used ChatGPT for several things (episode summaries off podcast transcripts, for example) and I have friends who built shows around Q&A sessions with the open AI.

When studying unconstrained behaviour and allowing mice to leave their cage to navigate a complex labyrinth, the mice exhibit foraging behaviour in the labyrinth searching for rewards, returning to their home cage now and then, e.g. to drink. Surprisingly, when executing such a ``home run'', the mice do not follow the exact reverse path, in fact, the entry path and home path have very little overlap. Recent work proposed a hierarchical active inference model for navigation, where the low level model makes inferences about hidden states and poses that explain sensory inputs, whereas the high level model makes inferences about moving between locations, effectively building a map of the environment. However, using this ``map'' for planning, only allows the agent to find trajectories that it previously explored, far from the observed mice's behaviour. In this paper, we explore ways of incorporating before-unvisited paths in the planning algorithm, by using the low level generative model to imagine potential, yet undiscovered paths. We demonstrate a proof of concept in a grid-world environment, showing how an agent can accurately predict a new, shorter path in the map leading to its starting point, using a generative model learnt from pixel-based observations.

Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models' ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users' needs.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Jose Siri and Chas McCormick hit back-to-back home runs in the eighth inning, rallying the AL West-leading Houston Astros over the Arizona Diamondbacks 7-6 on Sunday. Carlos Correa also homered as the Astros held their comfortable division lead over Oakland. Houston won for the fourth time in five games and cut Tampa Bay's lead for the best record in the AL to 3 ½ games.

At any given moment, many thousands of new videos are being posted to sites like YouTube, TikTok, and Instagram. An increasing number of those videos are being recorded and streamed live. But tech and media companies still struggle to understand what's going in all that content. Now MIT alumnus-founded Netra is using artificial intelligence to improve video analysis at scale. The company's system can identify activities, objects, emotions, locations, and more to organize and provide context to videos in new ways.

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Old school, new school: The Tampa Bay Rays have two pitchers who have started 20 games this year. One is their ace, Blake Snell, whose 2.03 earned-run average ranks second in the American League. The other is Ryne Stanek, a reliever turned “opener” — in his case, a right-hander who works the first inning or so, followed by a left-hander. In a year in which the Rays lost starters Anthony Banda, Jose DeLeon and Brent Honeywell to Tommy John surgery and traded starters Chris Archer and Nathan Eovaldi, the team leads the AL in ERA since May 19, when Sergio Romo debuted as Tampa Bay’s first “opener.” There is no pitching statistic more derided in sabermetrics than wins for a pitcher.

How do baseball scouts use machine learning and AI to predict player performance? Ari Kaplan, Principal at Aginity, and David Kearns, Offering Manager, IBM Analytics Ecosystem, join us to talk about the recent merge of H20.ai and IBM. They also discuss how baseball decisions are made using analytics, AI, machine learning, Hadoop, and much more. Discover how the world's best players come together to create winning teams, and how these principles apply off the field in the world of business.