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data/retrieval_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

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 {"tstamp": 1722445796.0975, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "483dafda9ed54c9c80bc45216ad796db", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "what type of data is provided in a knowledge base", "0_output": [["what type of data is provided in a knowledge base", "W. Edwards Deming\n\nDeming continued to advise businesses large and small. From 1985 through 1989, Deming served as a consultant to Vernay Laboratories, a rubber manufacturing firm in Yellow Springs, Ohio, with fewer than 1,000 employees. He held several week-long seminars for employees and suppliers of the small company where his famous example \"Workers on the Red Beads\" spurred several major changes in Vernay's manufacturing processes.\nDeming joined the Graduate School of Business at Columbia University in 1988. In 1990, during his last year, he founded the W. Edwards Deming Center for Quality, Productivity, and Competitiveness at Columbia Business School to promote operational excellence in business through the development of research, best practices and strategic planning.\nIn 1990, Marshall Industries (NYSE:MI, 1984–1999) CEO Robert Rodin trained with the then 90-year-old Deming and his colleague Nida Backaitis. Marshall Industries' dramatic transformation and growth from $400 million to $1.8 billion in sales was chronicled in Deming's last book The New Economics, a Harvard Case Study, and Rodin's book, Free, Perfect and Now.\nIn 1993, Deming published his final book, The New Economics for Industry, Government, Education, which included the System of Profound Knowledge and the 14 Points for Management. It also contained educational concepts involving group-based teaching without grades, as well as management without individual merit or performance reviews.\nAcademic contributions\nThe philosophy of W. Edwards Deming has been summarized as follows:\nThe Deming System of Profound Knowledge"]], "0_corpus": "wikipedia", "1_conv_id": "3139be50f5064e0894343f6a22890c54", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "what type of data is provided in a knowledge base", "1_output": [["what type of data is provided in a knowledge base", "Knowledge base\n\nIn computer science, a knowledge base (KB) is a set of sentences, each sentence given in a knowledge representation language, with interfaces to tell new sentences and to ask questions about what is known, where either of these interfaces might use inference. It is a technology used to store complex structured data used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems.\nOriginal usage of the term"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722445834.473, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ed8435cf63824a24a3b2372e6608f29d", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "how to change share name in windows server 2008?", "0_output": [["how to change share name in windows server 2008?", "Superuser Stackexchange\n\nQ: How to change the administrator account name from command prompt in Windows Server 2008 Server Core? I'm trying to change the administrator account name on my virtual machine for lab work purposes.  I'm running Windows Server 2008 Server Core, therefore I'm only using the standard command prompts.\nHow can I do this?\n\nA: If it domain user install Active Directory Administration module for PowerShell. \ncommand line:\npowershell Import-module ActiveDirectory\n\nrename domain user -\nPowershell:\nGet-ADUser -Identity 'Administrator' | Rename-ADObject -NewName 'Partisan'\n\ncommand line:\npowershell Get-ADUser -Identity 'Administrator' ^| Rename-ADObject -NewName 'Partisan'\n\nvar 2:\ndsquery user -name Administrator | dsmove -newname \"Partisan\"\n\nlocal administrator -\nPowershell:\nRename-LocalUser -UserName 'Administrator' -NewUserName 'Partisan'\n\ncommand line:\npowershell Rename-LocalUser -UserName 'Administrator' -NewUserName 'Partisan'\n\nvar2:\nwmic UserAccount where Name=\"Administrator\" call Rename Name=\"Partisan\"\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "939f7aa837ce48d9b9700ee6346622d4", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "how to change share name in windows server 2008?", "1_output": [["how to change share name in windows server 2008?", "Serverfault Stackexchange\n\nQ: How do you configure a \\\\domain\\sharedFolder in windows 2008? Background: \n\n\n*\n\n*I've got several Windows 2008 servers setup by someone else.\n\n*They are all on one domain, lets call this mydomain\n\n*Several boxes are accessing a shared folder by going to \\\\mydomain\\share1\n\n*I found the actual files backing the share on box1\n\n*I need to move the location of this share to a larger drive for space reasons\n\n\nI'm familiar with setting up and managing shares like \\\\box1\\share1, but where do you manage/configure shares that are domain-wide; accessed like \\\\mydomain\\share1 ?\n\nA: It sounds like you have a domain based DFS configured, which is managed by the DFS Management utility.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722446198.8045, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9e2f3e5d4eb04b5db030979a91bfe01e", "0_model_name": "text-embedding-3-large", "0_prompt": "How can a small visual-linguistic model be effectively trained using knowledge distillation?", "0_output": [["How can a small visual-linguistic model be effectively trained using knowledge distillation?", "Title: Compressing Visual-linguistic Model via Knowledge Distillation\n\nAbstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages."]], "0_corpus": "arxiv", "1_conv_id": "99d70563aab749489c924deb1dc45544", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "How can a small visual-linguistic model be effectively trained using knowledge distillation?", "1_output": [["How can a small visual-linguistic model be effectively trained using knowledge distillation?", "Title: Compressing Visual-linguistic Model via Knowledge Distillation\n\nAbstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages."]], "1_corpus": "arxiv"}
+{"tstamp": 1722447804.2737, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a69e4fe18f394932a19bcd5b57c0266f", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "0_output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: Order Preservation in Limit Algebras\n\nAbstract: The matrix units of a digraph algebra, A, induce a relation, known as the diagonal order, on the projections in a masa in the algebra. Normalizing partial isometries in A act on these projections by conjugation; they are said to be order preserving when they respect the diagonal order. Order preserving embeddings, in turn, are those embeddings which carry order preserving normalizers to order preserving normalizers. This paper studies operator algebras which are direct limits of finite dimensional algebras with order preserving embeddings. We give a complete classification of direct limits of full triangular matrix algebras with order preserving embeddings. We also investigate the problem of characterizing algebras with order preserving embeddings."]], "0_corpus": "arxiv", "1_conv_id": "05b4950e4c6247cd99901948d031c52e", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "1_output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: An Explanation of In-context Learning as Implicit Bayesian Inference\n\nAbstract: Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning."]], "1_corpus": "arxiv"}