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 {"tstamp": 1724250341.7275, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1724250339.5505, "finish": 1724250341.7275, "ip": "", "conv_id": "9cf51e22aaab451f88d1d21b7c928b3d", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "who founded twitter?", "output": [["who founded twitter?", "Twitter, Inc.\n\nTwitter, Inc. was an American social media company based in San Francisco, California, which operated and was named for its flagship social media network prior to its rebrand as X. In addition to Twitter, the company previously operated the Vine short video app and Periscope livestreaming service. In April 2023, Twitter merged with X Holdings and ceased to be an independent company, becoming a part of X Corp.\nTwitter was created by Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams in March 2006 and was launched that July. , more than 100 million users tweeted 340 million tweets a day. The company went public in November 2013. , Twitter had more than 330 million monthly active users.\nOn April 25, 2022, Twitter agreed to a $44 billion buyout by Elon Musk, CEO of SpaceX and Tesla, one of the biggest deals to turn a company private. On July 8, Musk terminated the deal. Twitter's shares fell, leading company officials to sue Musk in Delaware's Court of Chancery on July 12. On October 4, Musk announced his intention to purchase the company as he had agreed, for $44 billion, or $54.20 a share; the agreement closed on October 27.\nFollowing Musk's takeover, Twitter was criticized for an increase in hate speech, as well as for perceived systemic prioritization of right-wing content. His acquisition of the company has been characterized by large-scale policy changes, mass layoffs and resignations, and a dramatic shift in the company's work culture.\nHistory\n2006–2007: Creation and initial reaction"]], "corpus": "wikipedia"}
 {"tstamp": 1724256882.7484, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1724256880.5502, "finish": 1724256882.7484, "ip": "", "conv_id": "4bd29ba0dbca4091bfb91490a904b237", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "return the top paper from CVPR 2023", "output": [["return the top paper from CVPR 2023", "Title: Multiview Compressive Coding for 3D Reconstruction\n\nAbstract: A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and general-purpose representations. Comparatively, 3D poses new challenges stemming from occlusions not depicted in the image. Prior works try to overcome these by inferring from multiple views or rely on scarce CAD models and category-specific priors which hinder scaling to novel settings. In this work, we explore single-view 3D reconstruction by learning generalizable representations inspired by advances in self-supervised learning. We introduce a simple framework that operates on 3D points of single objects or whole scenes coupled with category-agnostic large-scale training from diverse RGB-D videos. Our model, Multiview Compressive Coding (MCC), learns to compress the input appearance and geometry to predict the 3D structure by querying a 3D-aware decoder. MCC's generality and efficiency allow it to learn from large-scale and diverse data sources with strong generalization to novel objects imagined by DALL$\\cdot$E 2 or captured in-the-wild with an iPhone."]], "corpus": "arxiv"}
 {"tstamp": 1724256882.7484, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1724256880.5502, "finish": 1724256882.7484, "ip": "", "conv_id": "55aede8cdd1d41be8df54a5feeb359c5", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "return the top paper from CVPR 2023", "output": [["return the top paper from CVPR 2023", "Title: State-of-the-Art in Smart Contact Lenses for Human Machine Interaction\n\nAbstract: Contact lenses have traditionally been used for vision correction applications. Recent advances in microelectronics and nanofabrication on flexible substrates have now enabled sensors, circuits and other essential components to be integrated on a small contact lens platform. This has opened up the possibility of using contact lenses for a range of human-machine interaction applications including vision assistance, eye tracking, displays and health care. In this article, we systematically review the range of smart contact lens materials, device architectures and components that facilitate this interaction for different applications. In fact, evidence from our systematic review demonstrates that these lenses can be used to display information, detect eye movements, restore vision and detect certain biomarkers in tear fluid. Consequently, whereas previous state-of the-art reviews in contact lenses focused exclusively on biosensing, our systematic review covers a wider range of smart contact lens applications in HMI. Moreover, we present a new method of classifying the literature on smart contact lenses according to their six constituent building blocks, which are the sensing, energy management, driver electronics, communications, substrate and the interfacing modules. Based on recent developments in each of these categories, we speculate the challenges and opportunities of smart contact lenses for human-machine interaction. Moreover, we propose a novel self-powered smart contact lens concept with integrated energy harvesters, sensors and communication modules to enable autonomous operation. Our review is therefore a critical evaluation of current data and is presented with the aim of guiding researchers to new research directions in smart contact lenses."]], "corpus": "arxiv"}
+{"tstamp": 1724259962.7753, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1724259962.5628, "finish": 1724259962.7753, "ip": "", "conv_id": "435e8496b59e4262873847fa94d937a7", "model_name": "GritLM/GritLM-7B", "prompt": "ranking loss", "output": [["ranking loss", "Title: Learning Rank Functionals: An Empirical Study\n\nAbstract: Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query. In this paper, we investigate key issues in designing an effective learning to rank algorithm. These include data representation, the choice of rank functionals, the design of the loss function so that it is correlated with the rank metrics used in evaluation. For the loss function, we study three techniques: approximating the rank metric by a smooth function, decomposition of the loss into a weighted sum of element-wise losses and into a weighted sum of pairwise losses. We then present derivations of piecewise losses using the theory of high-order Markov chains and Markov random fields. In experiments, we evaluate these design aspects on two tasks: answer ranking in a Social Question Answering site, and Web Information Retrieval."]], "corpus": "arxiv"}
+{"tstamp": 1724259962.7753, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1724259962.5628, "finish": 1724259962.7753, "ip": "", "conv_id": "c2acc9107e674e1596ad65f3835eaca7", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "ranking loss", "output": [["ranking loss", "Title: Learning Rank Functionals: An Empirical Study\n\nAbstract: Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query. In this paper, we investigate key issues in designing an effective learning to rank algorithm. These include data representation, the choice of rank functionals, the design of the loss function so that it is correlated with the rank metrics used in evaluation. For the loss function, we study three techniques: approximating the rank metric by a smooth function, decomposition of the loss into a weighted sum of element-wise losses and into a weighted sum of pairwise losses. We then present derivations of piecewise losses using the theory of high-order Markov chains and Markov random fields. In experiments, we evaluate these design aspects on two tasks: answer ranking in a Social Question Answering site, and Web Information Retrieval."]], "corpus": "arxiv"}