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{"tstamp": 1722362021.8407, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6c79be6c3a75449590dbdf0de2712f54", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "are fresh or frozen vegetables healthier", "0_output": [["are fresh or frozen vegetables healthier", "Frozen meal\n\nFollowing this, the dinners are either covered with aluminum foil or paper, and the product is tightly packed with a partial vacuum created to ensure no evaporation takes place that can cause the food to dry out. Then the packaged dinners are placed in a refrigerated storage facility, transported by refrigerated truck, and stored in the grocer's freezer. TV dinners prepared with the aforementioned steps—that is, frozen and packaged properly—can remain in near-perfect condition for a long time, so long as they are stored at during shipping and storage.\nHealth concerns\nFrozen meals are often heavily processed with extra salt and fat to make foods last longer. In addition, stabilizing the product for a long period typically means that companies will use partially hydrogenated vegetable oils for some items (typically dessert). Partially hydrogenated vegetable oils are high in trans fats and are shown to adversely affect cardiovascular health. The dinners are almost always significantly less nutritious than fresh food and are formulated to remain edible after long periods of storage, thus often requiring preservatives such as butylated hydroxytoluene. There is, however, some variability between brands.\nIn recent years there has been a push by a number of independent manufacturers and retailers to make meals that are low in salt and fat and free of artificial additives. In the UK, most British supermarkets also produce their own \"healthy eating\" brands. Nearly all chilled or frozen ready meals sold in the UK are now clearly labeled with the salt, sugar and fat content and the recommended daily intake. Concern about obesity and government publicity initiatives such as those by the Food Standards Agency and the National Health Service have encouraged manufacturers to reduce the levels of salt and fat in ready prepared food."]], "0_corpus": "wikipedia", "1_conv_id": "7b8aff77791243c8ade0e1a6091a52d9", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "are fresh or frozen vegetables healthier", "1_output": [["are fresh or frozen vegetables healthier", "Fresh Fruit for Rotting Vegetables\n\nFresh Fruit for Rotting Vegetables is the debut studio album by the American punk rock band Dead Kennedys. It was first released on September 2, 1980, through Cherry Red Records in the United Kingdom, and I.R.S. Records in the United States. It was later issued by Jello Biafra's own Alternative Tentacles label in the United States. It is the only Dead Kennedys studio album to feature drummer Bruce Slesinger and (on one track) guitarist Carlo Cadona.\nRecording and release\nThe photo on the front cover, showing several police cars on fire, was taken during the White Night riots of May 21, 1979, that resulted from the light sentence given to former San Francisco City Supervisor Dan White for the murder of Mayor George Moscone and Supervisor Harvey Milk. When Biafra ran for mayor, one of his policies had been for a statue to be erected to Dan White, and for eggs, tomatoes and stones to be available nearby for pelting it. In addition, the band's version of \"I Fought the Law\" has rewritten lyrics making reference to the incident."]], "1_corpus": "wikipedia"}
{"tstamp": 1722362060.3289, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "4e0b17f4fecb4b1685dcac82f33b7b27", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "\thow does qualtrics fit in with the big data trend?", "0_output": [["\thow does qualtrics fit in with the big data trend?", "Qualtrics\n\nQualtrics is an American experience management company, with co-headquarters in Seattle, Washington, and Provo, Utah, in the United States. The company was founded in 2002 by Scott M. Smith, Ryan Smith, Jared Smith, and Stuart Orgill.\nQualtrics offers a cloud-based subscription software platform for experience management, which it launched in March 2017."]], "0_corpus": "wikipedia", "1_conv_id": "3a42eda27b5d4c9fb598e714d2144882", "1_model_name": "text-embedding-3-large", "1_prompt": "\thow does qualtrics fit in with the big data trend?", "1_output": [["\thow does qualtrics fit in with the big data trend?", "Qualtrics\n\nAwards and ranking In 2020, Qualtrics earned a ‘Leader’ designation in Gartner's Magic Quadrant for Voice of Customer, a ‘leader’ designation in Forrester's Employee Experience for Enterprise wave, and the top ranking in G2's Experience Management category. In 2016, Qualtrics was ranked #12 on the Forbes Cloud 100 list, moving to #6 in 2017. In March 2020, Qualtrics's CoreXM platform was named a 2020 gold winner by the Edison Awards in the Applied Technology category.\nAcquisitions\nIn May 2016, Qualtrics acquired statistical analysis startup Statwing for an undisclosed sum. Statwing was a San Francisco-based company that created point-and-click software for advanced statistical analysis.\nIn April 2018 the firm acquired Delighted for an undisclosed sum. Delighted had more than 1,500 customers at the time of acquisition.\nIn October 2021 the firm acquired Clarabridge in an all-stock deal for $1.125 billion. Clarabridge was a Virginia-based company that created software for omnichannel conversational analytics.\nAcquisition by SAP SE\nIn November 2018, SAP announced its intent to acquire Qualtrics. SAP acquired all outstanding shares of Qualtrics for US$8 billion in an all cash deal. SAP secured €7 billion in financing. At the time it was announced, the Qualtrics acquisition was SAP's second-biggest purchase ever, behind the $8.3 billion acquisition of travel and expense management firm Concur in 2014. The acquisition was formally closed January 23, 2019."]], "1_corpus": "wikipedia"}
{"tstamp": 1722362558.6289, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e8c6f75538814d58ac1610d735ef218d", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "how many numbers can computer count?", "0_output": [["how many numbers can computer count?", "Orders of magnitude (numbers)\n\nComputing – UTF-16/Unicode: There are 1,114,112 (220 + 216) distinct values encodable in UTF-16, and, thus (as Unicode is currently limited to the UTF-16 code space), 1,114,112 valid code points in Unicode (1,112,064 scalar values and 2,048 surrogates).\nLudology – Number of games: Approximately 1,181,019 video games have been created as of 2019.\nBiology – Species: The World Resources Institute claims that approximately 1.4 million species have been named, out of an unknown number of total species (estimates range between 2 and 100 million species). Some scientists give 8.8 million species as an exact figure.\nGenocide: Approximately 800,000–1,500,000 (1.5 million) Armenians were killed in the Armenian genocide.\nLinguistics: The number of possible conjugations for each verb in the Archi language is 1,502,839.\nInfo: The freedb database of CD track listings has around 1,750,000 entries .\nComputing – UTF-8: 2,164,864 (221 + 216 + 211 + 27) possible one- to four-byte UTF-8 sequences, if the restrictions on overlong sequences, surrogate code points, and code points beyond U+10FFFF are not adhered to. (Note that not all of these correspond to unique code points.)\nMathematics – Playing cards: There are 2,598,960 different 5-card poker hands that can be dealt from a standard 52-card deck.\nMathematics: There are 3,149,280 possible positions for the Skewb."]], "0_corpus": "wikipedia", "1_conv_id": "46323c41f9be4245865047b7788ba305", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "how many numbers can computer count?", "1_output": [["how many numbers can computer count?", "Computer number format\n\nThe relation between numbers and bit patterns is chosen for convenience in computer manipulation; eight bytes stored in computer memory may represent a 64-bit real, two 32-bit reals, or four signed or unsigned integers, or some other kind of data that fits into eight bytes. The only difference is how the computer interprets them. If the computer stored four unsigned integers and then read them back from memory as a 64-bit real, it almost always would be a perfectly valid real number, though it would be junk data.\nOnly a finite range of real numbers can be represented with a given number of bits. Arithmetic operations can overflow or underflow, producing a value too large or too small to be represented.\nThe representation has a limited precision. For example, only 15 decimal digits can be represented with a 64-bit real. If a very small floating-point number is added to a large one, the result is just the large one. The small number was too small to even show up in 15 or 16 digits of resolution, and the computer effectively discards it. Analyzing the effect of limited precision is a well-studied problem. Estimates of the magnitude of round-off errors and methods to limit their effect on large calculations are part of any large computation project. The precision limit is different from the range limit, as it affects the significand, not the exponent."]], "1_corpus": "wikipedia"}
{"tstamp": 1722363249.6393, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "19611b79c0b8467cb1ffaee054c1e14e", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Training an adapter for a retrieval model", "0_output": [["Training an adapter for a retrieval model", "Title: Parameter-Efficient Sparse Retrievers and Rerankers using Adapters\n\nAbstract: Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2\\% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR"]], "0_corpus": "arxiv", "1_conv_id": "2306e0a4a4b24733a382d537e1889bd3", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Training an adapter for a retrieval model", "1_output": [["Training an adapter for a retrieval model", "Title: On Discrete Painleve Equations Associated with the Lattice KdV Systems and the Painleve VI Equation\n\nAbstract: A new integrable nonautonomous nonlinear ordinary difference equation is presented which can be considered to be a discrete analogue of the Painleve V equation. Its derivation is based on the similarity reduction on the two-dimensional lattice of integrable partial difference equations of KdV type. The new equation which is referred to as GDP (generalised discrete Painleve equation) contains various ``discrete Painleve equations'' as subcases for special values/limits of the parameters, some of which were already given before in the literature. The general solution of the GDP can be expressed in terms of Painleve VI (PVI) transcendents. In fact, continuous PVI emerges as the equation obeyed by the solutions of the discrete equation in terms of the lattice parameters rather than the lattice variables that label the lattice sites. We show that the bilinear form of PVI is embedded naturally in the lattice systems leading to the GDP. Further results include the establishment of Baecklund and Schlesinger transformations for the GDP, the corresponding isomonodromic deformation problem, and the self-duality of its bilinear scheme."]], "1_corpus": "arxiv"}
{"tstamp": 1722363327.3402, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "55efd4d6039e4ba194eb41c38a3c6ea7", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "prompt optimization using a reward model", "0_output": [["prompt optimization using a reward model", "Title: Invariants of moduli spaces of abelian surfaces\n\nAbstract: Compactifications of moduli spaces of (1,p)-polarized abelian surfaces with level structures of canonical type have been described in great detail by Hulek, Kahn and Weintraub. The aim of this paper is to determine some invariants of smooth models of these moduli spaces. In particular, a geometric description of their canonical divisors is given and their Chern numbers are computed."]], "0_corpus": "arxiv", "1_conv_id": "720dd7663692404dbe4e7978fb23acb5", "1_model_name": "voyage-multilingual-2", "1_prompt": "prompt optimization using a reward model", "1_output": [["prompt optimization using a reward model", "Title: Reward Collapse in Aligning Large Language Models\n\nAbstract: The extraordinary capabilities of large language models (LLMs) such as ChatGPT and GPT-4 are in part unleashed by aligning them with reward models that are trained on human preferences, which are often represented as rankings of responses to prompts. In this paper, we document the phenomenon of \\textit{reward collapse}, an empirical observation where the prevailing ranking-based approach results in an \\textit{identical} reward distribution \\textit{regardless} of the prompts during the terminal phase of training. This outcome is undesirable as open-ended prompts like ``write a short story about your best friend'' should yield a continuous range of rewards for their completions, while specific prompts like ``what is the capital of New Zealand'' should generate either high or low rewards. Our theoretical investigation reveals that reward collapse is primarily due to the insufficiency of the ranking-based objective function to incorporate prompt-related information during optimization. This insight allows us to derive closed-form expressions for the reward distribution associated with a set of utility functions in an asymptotic regime. To overcome reward collapse, we introduce a prompt-aware optimization scheme that provably admits a prompt-dependent reward distribution within the interpolating regime. Our experimental results suggest that our proposed prompt-aware utility functions significantly alleviate reward collapse during the training of reward models."]], "1_corpus": "arxiv"}
{"tstamp": 1722363435.7841, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "4cb5f6a122cb452f867ed2739011cd9b", "0_model_name": "BM25", "0_prompt": "distilling the reranking ability of large language models to cross encoders", "0_output": [["distilling the reranking ability of large language models to cross encoders", "Title: ReFIT: Relevance Feedback from a Reranker during Inference\n\nAbstract: Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), which are then reranked by a more powerful cross-encoder model. While the reranker often yields improved candidate scores compared to the retriever, its scope is confined to only the top K retrieved candidates. As a result, the reranker cannot improve retrieval performance in terms of Recall@K. In this work, we propose to leverage the reranker to improve recall by making it provide relevance feedback to the retriever at inference time. Specifically, given a test instance during inference, we distill the reranker's predictions for that instance into the retriever's query representation using a lightweight update mechanism. The aim of the distillation loss is to align the retriever's candidate scores more closely with those produced by the reranker. The algorithm then proceeds by executing a second retrieval step using the updated query vector. We empirically demonstrate that this method, applicable to various retrieve-and-rerank frameworks, substantially enhances retrieval recall across multiple domains, languages, and modalities."]], "0_corpus": "arxiv", "1_conv_id": "44d3ff6d268a4decb6abdb3d5eb97904", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "distilling the reranking ability of large language models to cross encoders", "1_output": [["distilling the reranking ability of large language models to cross encoders", "Title: A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking\n\nAbstract: Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, the distilled models usually do not reach their teacher LLM's effectiveness. To investigate whether best practices for fine-tuning cross-encoders on manually labeled data (e.g., hard-negative sampling, deep sampling, and listwise loss functions) can help to improve LLM ranker distillation, we construct and release a new distillation dataset: Rank-DistiLLM. In our experiments, cross-encoders trained on Rank-DistiLLM reach the effectiveness of LLMs while being orders of magnitude more efficient. Our code and data is available at https://github.com/webis-de/msmarco-llm-distillation."]], "1_corpus": "arxiv"}
{"tstamp": 1722363538.7216, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "435649f6492e485c93e938c5af958974", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "How can I use an LLM to train an embedding model?", "0_output": [["How can I use an LLM to train an embedding model?", "Title: Language Models are Universal Embedders\n\nAbstract: In the large language model (LLM) revolution, embedding is a key component of various systems. For example, it is used to retrieve knowledge or memories for LLMs, to build content moderation filters, etc. As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario. In this work, we make an initial step towards this goal, demonstrating that multiple languages (both natural and programming) pre-trained transformer decoders can embed universally when finetuned on limited English data. We provide a comprehensive practice with thorough evaluations. On English MTEB, our models achieve competitive performance on different embedding tasks by minimal training data. On other benchmarks, such as multilingual classification and code search, our models (without any supervision) perform comparably to, or even surpass heavily supervised baselines and/or APIs. These results provide evidence of a promising path towards building powerful unified embedders that can be applied across tasks and languages."]], "0_corpus": "arxiv", "1_conv_id": "8eee464cbf244db6b3e8f8a8cc8561d8", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "How can I use an LLM to train an embedding model?", "1_output": [["How can I use an LLM to train an embedding model?", "Title: Embedding-Aligned Language Models\n\nAbstract: We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space. Our method leverages reinforcement learning (RL), treating a pre-trained LLM as an environment. Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. We demonstrate the effectiveness of the EAGLE agent using the MovieLens 25M dataset to surface content gaps that satisfy latent user demand. We also demonstrate the benefit of using an optimal design of a state-dependent action set to improve EAGLE's efficiency. Our work paves the way for controlled and grounded text generation using LLMs, ensuring consistency with domain-specific knowledge and data representations."]], "1_corpus": "arxiv"}
{"tstamp": 1722364270.4879, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6e94a2b6dc134ea99ef8d30eb1530d85", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Like Sparrows on a Clothes Line: The Self-Organization of Random Number Sequences\n\nAbstract: We study sequences of random numbers {Z[1],Z[2],Z[3],...,Z[n]} -- which can be considered random walks with reflecting barriers -- and define their \"types\" according to whether Z[i] > Z[i+1], (a down-movement), or Z[i] < Z[i+1] (up-movement). This paper examines the means, xi, to which the Zi converge, when a large number of sequences of the same type is considered. It is shown that these means organize themselves in such a way that, between two turning points of the sequence, they are equidistant from one another. We also show that m steps in one direction tend to offset one step in the other direction, as m -> infinity. Key words:random number sequence, self-organization, random walk, reflecting barriers."]], "0_corpus": "arxiv", "1_conv_id": "3de8ea25d77340518d8527eed178d6ab", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining\n\nAbstract: Large language models exhibit exceptional generalization capabilities, primarily attributed to the utilization of diversely sourced data. However, conventional practices in integrating this diverse data heavily rely on heuristic schemes, lacking theoretical guidance. This research tackles these limitations by investigating strategies based on low-cost proxies for data mixtures, with the aim of streamlining data curation to enhance training efficiency. Specifically, we propose a unified scaling law, termed BiMix, which accurately models the bivariate scaling behaviors of both data quantity and mixing proportions. We conduct systematic experiments and provide empirical evidence for the predictive power and fundamental principles of BiMix. Notably, our findings reveal that entropy-driven training-free data mixtures can achieve comparable or even better performance than more resource-intensive methods. We hope that our quantitative insights can shed light on further judicious research and development in cost-effective language modeling."]], "1_corpus": "arxiv"}
{"tstamp": 1722364274.046, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "8a308b6e542942c5a5144ec9d52b95f3", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Can we submit to other venues after review at ARR without withdrawing the paper?", "0_output": [["Can we submit to other venues after review at ARR without withdrawing the paper?", "Tex Stackexchange\n\nQ: Online Overleaf Editor: Clicked the Submit Button by mistake By mistake I clicked on the \"Submit\" button provided in the overleaf editor. Now my document shows:\n\"Manuscript Submitted to ACM\"\nI have following questions:\n\n*\n\n*What I have to do with my document, it was an incomplete document?\n\n*Will my document be leaked out now?\n\n*How can I remove the line \"Manuscript Submitted to ACM\" from my document?\n\nSome body please guide me.\nZulfi.\n\nA: (Tom from Overleaf Support here.)\nIf you accidentally submitted a paper via the Overleaf's Submit modal, there are three options:\n\n*\n\n*Accidental submissions to the Overleaf Gallery: Simply contact us via support@overleaf.com and as for the submission to be rejected. We're happy to assist with this.\n\n\n*Accidental submissions to journals with direct submission: In these cases, the files are not with us anymore. You'll need to get in touch with the journal's Editorial Office and ask for the submission to be disregarded. It shouldn't be an issue.\n\n\n*Accidental submissions when a ZIP file was provided to you: Nothing is needed. You were given the ZIP file yourself, it hasn't been sent to any 3rd party.\n"]], "0_corpus": "stackexchange", "1_conv_id": "89bc9091aa614bccae37263f84d98252", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Can we submit to other venues after review at ARR without withdrawing the paper?", "1_output": [["Can we submit to other venues after review at ARR without withdrawing the paper?", "Tex Stackexchange\n\nQ: Online Overleaf Editor: Clicked the Submit Button by mistake By mistake I clicked on the \"Submit\" button provided in the overleaf editor. Now my document shows:\n\"Manuscript Submitted to ACM\"\nI have following questions:\n\n*\n\n*What I have to do with my document, it was an incomplete document?\n\n*Will my document be leaked out now?\n\n*How can I remove the line \"Manuscript Submitted to ACM\" from my document?\n\nSome body please guide me.\nZulfi.\n\nA: (Tom from Overleaf Support here.)\nIf you accidentally submitted a paper via the Overleaf's Submit modal, there are three options:\n\n*\n\n*Accidental submissions to the Overleaf Gallery: Simply contact us via support@overleaf.com and as for the submission to be rejected. We're happy to assist with this.\n\n\n*Accidental submissions to journals with direct submission: In these cases, the files are not with us anymore. You'll need to get in touch with the journal's Editorial Office and ask for the submission to be disregarded. It shouldn't be an issue.\n\n\n*Accidental submissions when a ZIP file was provided to you: Nothing is needed. You were given the ZIP file yourself, it hasn't been sent to any 3rd party.\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722364308.3175, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "863d8484113b41f3ad486bce94296929", "0_model_name": "text-embedding-004", "0_prompt": "do you have to have a gun permit to shoot at a range", "0_output": [["do you have to have a gun permit to shoot at a range", "Shooting range\n\nA shooting range, firing range, gun range or shooting ground is a specialized facility, venue, or field designed specifically for firearm usage qualifications, training, practice, or competitions. Some shooting ranges are operated by military or law enforcement agencies, though the majority of ranges are privately owned by civilians and sporting clubs and cater mostly to recreational shooters. Each facility is typically overseen by one or more supervisory personnel, known as a Range Officer (RO), or sometimes a range master in the United States. Supervisory personnel are responsible for ensuring that all safety rules and relevant laws are followed at all times."]], "0_corpus": "wikipedia", "1_conv_id": "2cadf86189454fcc929249ad0f97a89a", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "do you have to have a gun permit to shoot at a range", "1_output": [["do you have to have a gun permit to shoot at a range", "Overview of gun laws by nation\n\nPoland Gun ownership in Poland is regulated by the Weapons and Munitions Act of 1999 as further amended. A permit is required to own or possess firearms. Permits are granted for specific purposes, such as self-defense, sport, hunting, collecting etc. and are except the ones for self-defense, which are . Institutional permits allow for firearm ownership by private security companies, shooting ranges, sport clubs etc.\nPermits are issued to permanent residents over 21, not addicted to alcohol or other substances, having no criminal record, being in good physical and mental health, who passed an exam before sporting association, hunting association or the Police. Permits specify the purposes, categories and quantities of allowed guns; categories depend on permit purpose while quantities are assigned at the discretion of the Police, based on documentation provided. Quantities can be increased when one uses up all available slots.\nCarrying loaded guns is allowed on all types of permits except permits for collecting and memorial purposes. Hunters can carry loaded only on hunting grounds. Guns shall be carried concealed in a holster close to the body. Carrying in public transport is limited only to self-defense permit holders. It is prohibited to carry while intoxicated, and when attending public gatherings or mass events.\nEveryone, including foreigners, is allowed to use legal guns at registered shooting ranges, regardless of actual gun's ownership, e.g. rented from the range or shared by a legal owner present. Discharging a firearm for training or leisure out of a registered shooting range (even on a large private property) is prohibited."]], "1_corpus": "wikipedia"}
{"tstamp": 1722364851.6531, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8c8e65f41c2245b29e485dbd910fd7b6", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Where is university of waterloo", "0_output": [["Where is university of waterloo", "University of Waterloo\n\nCoat of arms The university's coat of arms was first used by the university in October 1961, but was only officially granted by the Lord Lyon King of Arms in August 1987. It was not registered with the Canadian Heraldic Authority until 15 February 2001. Four variations of the coat of arms existed. The first was used from 1961 to 1996, when the second bright-yellow shield using slightly different shaped lions was introduced. The yellow background was dulled in 2000, and finally, the original lions were reintroduced in 2010 in conjunction with the attempt to replace the use of the coat of arms with a futuristic W logo. The new logo was eventually rejected after student opposition.\nThe red-on-gold lions on the university's arms were adopted from those of Arthur Wellesley, 1st Duke of Wellington, who defeated Napoleon at the Battle of Waterloo. The City of Waterloo was named after an area just south of Brussels, Belgium, where the battle occurred. The chevron on the arms was taken from the arms of Herbert Kitchener, 1st Earl Kitchener, Field Marshal with the British Army during World War I. The black and white pattern used on the chevron was based on the colours of Prussia, as homage to the German heritage of the area. The City of Kitchener was originally known as Berlin, but was renamed after Earl Kitchener in 1916 during World War I."]], "0_corpus": "wikipedia", "1_conv_id": "1947b916cfc64cc2a34fa5c4baf1cb63", "1_model_name": "text-embedding-004", "1_prompt": "Where is university of waterloo", "1_output": [["Where is university of waterloo", "University of Waterloo\n\nThe University of Waterloo (UWaterloo, UW, or Waterloo) is a public research university with a main campus in Waterloo, Ontario, Canada. The main campus is on of land adjacent to uptown Waterloo and Waterloo Park. The university also operates three satellite campuses and four affiliated university colleges. The university offers academic programs administered by six faculties and thirteen faculty-based schools. Waterloo operates the largest post-secondary co-operative education program in the world, with over 20,000 undergraduate students enrolled in the university's co-op program. Waterloo is a member of the U15, a group of research-intensive universities in Canada."]], "1_corpus": "wikipedia"}
{"tstamp": 1722365106.2273, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d6d55602c73c4ae5bdcca137fa7bdfba", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what is the difference between service and services?", "0_output": [["what is the difference between service and services?", "Stackoverflow Stackexchange\n\nQ: Spring RestTemplate POST Request with URL encoded data I'm new to Spring and trying to do a rest request with RestTemplate. The Java code should do the same as below curl command:\ncurl --data \"name=feature&color=#5843AD\" --header \"PRIVATE-TOKEN: xyz\" \"https://someserver.com/api/v3/projects/1/labels\"\n\nBut the server rejects the RestTemplate with a 400 Bad Request\nRestTemplate restTemplate = new RestTemplate();\nHttpHeaders headers = new HttpHeaders();\nheaders.add(\"PRIVATE-TOKEN\", \"xyz\");\nHttpEntity<String> entity = new HttpEntity<String>(\"name=feature&color=#5843AD\", headers);\nResponseEntity<LabelCreationResponse> response = restTemplate.exchange(\"https://someserver.com/api/v3/projects/1/labels\", HttpMethod.POST, entity, LabelCreationResponse.class);\n\nCan somebody tell me what I'm doing wrong?\n\nA: I think the problem is that when you try to send data to server didn't set the content type header which should be one of the two: \"application/json\" or \"application/x-www-form-urlencoded\" . In your case is: \"application/x-www-form-urlencoded\" based on your sample params (name and color). This header means \"what type of data my client sends to server\".\nRestTemplate restTemplate = new RestTemplate();\n\nHttpHeaders headers = new HttpHeaders();\nheaders.setContentType(MediaType.APPLICATION_FORM_URLENCODED);\nheaders.add(\"PRIVATE-TOKEN\", \"xyz\");\n\nMultiValueMap<String, String> map = new LinkedMultiValueMap<>();\nmap.add(\"name\",\"feature\");\nmap.add(\"color\",\"#5843AD\");\n\nHttpEntity<MultiValueMap<String, String>> entity = new HttpEntity<>(map, headers);\n\nResponseEntity<LabelCreationResponse> response =\n restTemplate.exchange(\"https://foo/api/v3/projects/1/labels\",\n HttpMethod.POST,\n entity,\n LabelCreationResponse.class);\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "139ac954b90f43fa8538ac61d8e18970", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between service and services?", "1_output": [["what is the difference between service and services?", "Stackoverflow Stackexchange\n\nQ: What is a service? I keep hearing the term \"service\" a lot, but I have a vague understanding of it. Could someone explain what a service is citing some practical examples? \n\nA: It depends on the context. Very abstractly speaking, a service is some sort of program that offers some function, but the internals of which are completely opaque. I.e., a web service is something running somewhere on the web, having some sort of API which you can contact to get or submit some data. What exactly it does is none of your business, it just offers its service to you. On a system, for example a Windows Service may be a program running persistently in the background, doing something. Inside an application, you may have a service layer, which offers some functionality which you can use in the rest of the application through an API, but how exactly it works is irrelevant.\nThat's in contrast to, e.g., a function or library or class, which you usually import, manipulate, use more directly. A service is more self-contained, offering only its functionality with nothing much in the way of introspecting it.\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722365346.4281, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "c225cc29368447dca969428826fd3660", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "0_corpus": "stackexchange", "1_conv_id": "95f3ec488c314e2b81635422386eedb6", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n import pdb\n pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722365495.8143, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "a4c7486a646e42299791238df7dd3ffd", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "what is dense retrieval", "0_output": [["what is dense retrieval", "Latent semantic analysis\n\nSemantic hashing In semantic hashing documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses. Deep neural network essentially builds a graphical model of the word-count vectors obtained from a large set of documents. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method.\nLatent semantic indexing\nLatent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. LSI is based on the principle that words that are used in the same contexts tend to have similar meanings. A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts.\nLSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents."]], "0_corpus": "wikipedia", "1_conv_id": "960bea43fd02452d9429b486a6719b0e", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "what is dense retrieval", "1_output": [["what is dense retrieval", "Information retrieval\n\nInformation retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.\nAutomated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications."]], "1_corpus": "wikipedia"}
{"tstamp": 1722367648.9929, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "cd65862465c04ec5b52a5544b3e095b7", "0_model_name": "text-embedding-3-large", "0_prompt": "Which milk is best for coffee?", "0_output": [["Which milk is best for coffee?", "Milk coffee\n\nMilk coffee is a category of coffee-based drinks made with milk.\nJohan Nieuhof, the Dutch ambassador to China, is credited as the first person to drink coffee with milk when he experimented with it around 1660.\nVarieties\nBreve\nA breve is an espresso made with a steamed mixture of half milk and half cream (i.e., half and half). Its size can vary. The name relates to the use of cream and milk."]], "0_corpus": "wikipedia", "1_conv_id": "2fbbfb6ab1f24128a2e01dc01a678ac0", "1_model_name": "BM25", "1_prompt": "Which milk is best for coffee?", "1_output": [["Which milk is best for coffee?", "Cha chaan teng\n\nBlack coffee: Hongkongers usually called this jaai fea (lit. \"vegetarian coffee\" / \"just coffee\", emphasising its plain texture) or \"fei sha jaau naai\" (lit. \"get rid of sand and milk\", i.e. coffee without sugar (the \"sand\") and milk)\nHong Kong-style milk tea: A highly popular drink in Hong Kong. Its standard is judged by its aroma, smoothness, and concentration. The tea is soaked in an iron container for several hours to let the flavour come out. If the customer wants condensed milk instead of normal milk, people will often say \"caa zau\" (lit. \"tea go\")\nSweet soy milk (豆漿): Soy milk sweetened with cane sugar and served either hot or cold.\nYuenyeung: A mixture of coffee and tea, originated in Hong Kong. According to traditional Chinese medicine, coffee and tea are \"hot\" and \"cold\" in nature, respectively. A mixing of both thus then yields the best combination for the beverage.\nBlack and White Yuenyeung: A mixture of Ovaltine and Horlicks, originated in Hong Kong.\nHorlicks\nOvaltine\nLemon Tea\nLemonade: Commonly served without sugar or syrup.\nLemon with Ribena\nLemon with Honey: Often, to reduce cost, the honey is replaced with \"Watercress Honey\"\nSalted lemon with 7-Up (鹹檸七): 7-Up, adding lemon marinated in salt, which is good for sore throat\nBlack cow (黑牛): Coca-Cola with vanilla ice cream.\nSnow White (白雪公主): Sprite or 7-Up with vanilla ice cream."]], "1_corpus": "wikipedia"}
{"tstamp": 1722368441.9754, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "99bcf2d0a9da49e1807d4f54cc274227", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Who is gwbush", "0_output": [["Who is gwbush", "Wabush\n\nWabush is a small town in the western tip of Labrador, bordering Quebec, known for transportation and iron ore operations."]], "0_corpus": "wikipedia", "1_conv_id": "99afcc82ac07410d83cc24502ec8af33", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Who is gwbush", "1_output": [["Who is gwbush", "George W. Bush\n\nGeorge Walker Bush (born July 6, 1946) is an American politician and businessman who served as the 43rd president of the United States from 2001 to 2009. A member of the Republican Party, he previously served as the 46th governor of Texas from 1995 to 2000."]], "1_corpus": "wikipedia"}
{"tstamp": 1722368449.6892, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "85b44bccdaf743a79e45c12d7e753a33", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "What is the name of the third president of france", "0_output": [["What is the name of the third president of france", "President of France\n\nEighteen years later, during the opening phases of the Second Republic, the title was created for a popularly elected head of state, the first of whom was Louis-Napoléon Bonaparte, nephew of Emperor Napoleon. Bonaparte served as president until he staged an auto coup against the republic, proclaiming himself Napoleon III, Emperor of the French.\nUnder the Third Republic the president was at first quite powerful, mainly because the royalist party was strong when the constitutional laws of 1875 were established, and it was hoped that a member of one of the two branches of the royal family would be able to serve as president and turn France into a constitutional monarchy. However, the next legislature was dominated by Republicans, and after President Patrice de MacMahon had unsuccessfully tried to obtain a new royalist majority by dissolving the Chambre des Députés, his successor Jules Grévy promised in 1879 that he would not use his presidential power of dissolution, and therefore lost his control over the legislature, effectively creating a parliamentary system that would be maintained for 80 years until the accession of Charles de Gaulle as president in 1959.\nIndeed, when the Fourth Republic was created, after the Second World War, it was a parliamentary system, in which the office of President of the Republic was a largely ceremonial one."]], "0_corpus": "wikipedia", "1_conv_id": "ce8ad6bb8422480e98c77eb6a6b88b97", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "What is the name of the third president of france", "1_output": [["What is the name of the third president of france", "Napoleon\n\nOn 1 August 1798, the British fleet under Sir Horatio Nelson captured or destroyed all but two vessels of the French fleet in the Battle of the Nile, preventing Bonaparte from strengthening the French position in the Mediterranean. His army had succeeded in a temporary increase of French power in Egypt, though it faced repeated uprisings. In early 1799, he moved an army into the Ottoman province of Damascus (Syria and Galilee). Bonaparte led these 13,000 French soldiers in the conquest of the coastal towns of Arish, Gaza, Jaffa, and Haifa. The attack on Jaffa was particularly brutal. Bonaparte discovered that many of the defenders were former prisoners of war, ostensibly on parole, so he ordered the garrison and some 1,500–5,000 prisoners to be executed by bayonet or drowning. Men, women, and children were robbed and murdered for three days.\nBonaparte began with an army of 13,000 men. 1,500 were reported missing, 1,200 died in combat, and thousands perished from disease—mostly bubonic plague. He failed to reduce the fortress of Acre, so he marched his army back to Egypt in May. Bonaparte was alleged to have ordered plague-stricken men to be poisoned with opium to speed the retreat. Back in Egypt on 25 July, Bonaparte defeated an Ottoman amphibious invasion at Abukir.\nBonaparte stayed informed of European affairs. He learned that France had suffered a series of defeats in the War of the Second Coalition. On 24 August 1799, fearing that the Republic's future was in doubt, he took advantage of the temporary departure of British ships from French coastal ports and set sail for France, despite the fact that he had received no explicit orders from Paris. The army was left in the charge of Jean-Baptiste Kléber.\nRuler of France"]], "1_corpus": "wikipedia"}
{"tstamp": 1722368616.2949, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "661eda7dd6124929b6c43e5ffaf7e678", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "how to make meth", "0_output": [["how to make meth", "Stackoverflow Stackexchange\n\nQ: How to use CSS to control the style of an HTML unordered list? Basically I want to control the margin on the left of the list. Here's how I have it structured:\n<li> \n<a href=\"http://link.com\">Main</a> \n<ul> \n <li> \n <a href=\"http://link.com\" title=\"\">Sub1</a>\n </li> \n <li> \n <a href=\"http://link.com\" title=\"\">Sub2</a> \n </li> \n</ul>\n</li>\n\nThe sub-sections are too far to the right with the current style I'm using. How do I change it?\n\nA: My ul \"reset\" looks something like this:\nul { list-style-type: none; margin: 0; padding: 0 }\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "5390157ae7514fb2b4d8172638146826", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "how to make meth", "1_output": [["how to make meth", "Tex Stackexchange\n\nQ: Improving chemfig drawing As a learning exercise I have been trying to replicate the structure of LSD using chemfig. I've taken this figure for reference. \nMy best effort so far has been this...\n\\documentclass[border=10pt]{standalone}\n\\usepackage[version=3]{mhchem} % Package for chemical equation typesetting\n\\usepackage{chemfig}\n\\begin{document}\n\\chemfig{*6(=(*5(-N-=(*6(--([:-30]<H)(*6(-N(-)--([:120]<:H)(-([:30]=O)([::60]-N([:180]-[::45]-)([:60]-[:10]-)))-=-))---))--))-=-=-)}\n\\end{document}\n\nwhich produces ...\n\nI have not been able to figure out how to create the proper triple ring nor have I gotten a proper understanding of the bond angles on the two chains off the N. \nI would certainly appreciate any and all advice.\n\nA: Here's how I would do it, although I admit I'm not very expert with chemfig.\nOutput\n\nCode\n\\documentclass[border=10pt]{standalone}\n\\usepackage[version=3]{mhchem} % Package for chemical equation typesetting\n\\usepackage{chemfig}\n\\begin{document}\n\\chemfig{*6(=(-[:-70]N-[:0]=[:70])-(*6(---(*6(([:90]<H)-\nN(-[:-30]CH_3)--(-[:90](=[:30]O)-[:145]N(-[:210]-[:150])\n(--[:150]))([:145]<:H)-=))---))=-=-)} \n\\end{document}\n\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722368661.3266, "task_type": "retrieval", "type": "share", "models": ["", ""], "ip": "", "0_conv_id": "f4cb9fc271104efc8c817e3ef3f2772a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "has sent or has been sent?", "0_output": [["has sent or has been sent?", "Stackoverflow Stackexchange\n\nQ: How to calculate 32 bit CRC in Ruby on rails? i want to calculate 32 bit CRC value for 'input field value\" in Ruby on rails.\nneed the sample code , please help me anyone.\n\nA: You could use Ruby's Zlib module.\nrequire 'zlib'\ncrc32 = Zlib::crc32('input field value')\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "0e62ff9752a8493b87e1d8ced4eb760a", "1_model_name": "BM25", "1_prompt": "has sent or has been sent?", "1_output": [["has sent or has been sent?", "English Stackexchange\n\nQ: letter has been sent to office by yesterday I just want to say, the letter already send to our head office yesterday. Please advise the correct usage\nletter has been sent to office by yesterday - this usage is correct or not? please advise\n\nA: Stylistically, \"was sent\" would be better (\"has been\" would indicate more recently than yesterday. As soon as you give a definite time [yesterday, on the 10th of January, last week, last month, etc.], \"has been\" is not the most felicitous choice).\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722368683.6189, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "f4cb9fc271104efc8c817e3ef3f2772a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "has sent or has been sent?", "0_output": [["has sent or has been sent?", "Stackoverflow Stackexchange\n\nQ: How to calculate 32 bit CRC in Ruby on rails? i want to calculate 32 bit CRC value for 'input field value\" in Ruby on rails.\nneed the sample code , please help me anyone.\n\nA: You could use Ruby's Zlib module.\nrequire 'zlib'\ncrc32 = Zlib::crc32('input field value')\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "0e62ff9752a8493b87e1d8ced4eb760a", "1_model_name": "BM25", "1_prompt": "has sent or has been sent?", "1_output": [["has sent or has been sent?", "English Stackexchange\n\nQ: letter has been sent to office by yesterday I just want to say, the letter already send to our head office yesterday. Please advise the correct usage\nletter has been sent to office by yesterday - this usage is correct or not? please advise\n\nA: Stylistically, \"was sent\" would be better (\"has been\" would indicate more recently than yesterday. As soon as you give a definite time [yesterday, on the 10th of January, last week, last month, etc.], \"has been\" is not the most felicitous choice).\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722368876.7284, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "25770df48b7b4cb89f6387968b6a52b1", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "rust vs go", "0_output": [["rust vs go", "Stackoverflow Stackexchange\n\nQ: Why did Rust go with \"match\" instead of \"switch\" or \"case\"? I'm curious about the history of this. I would assume switch would appeal to C/C++/Java/etc programmers and thus be a natural choice. If one wanted to avoid confusion in semantics (since match is more powerful than switch) I'd assume one would borrow case from Haskell instead.\n\nA: The Rust Reference cites Standard ML (SML) and OCaml as an influence for pattern matching. In OCaml, pattern matching uses the match keyword.\nThe original Rust compiler was written in OCaml, so it makes sense that OCaml would have had a stronger influence on Rust than, say, Haskell.\n"]], "0_corpus": "stackexchange", "1_conv_id": "bfa59c7a442d43dc90c9f277de7b7847", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "rust vs go", "1_output": [["rust vs go", "Stackoverflow Stackexchange\n\nQ: erlang vs golang I intend to learn a new language for better concurrency.\nErlang is a old but powerful language with a runtime to support its whole concurrency system as well as a well-known library, OTP. This is more like JVM.\nGo is another language good at concurrency, while it's more like C.\nMy major field is about C/S and B/S. What should I prefer?\n"]], "1_corpus": "stackexchange"}
{"tstamp": 1722368885.4442, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5101c949a71c4d10aec9c4ac9b9e5ba9", "0_model_name": "BM25", "0_prompt": "who created gcsl?", "0_output": [["who created gcsl?", "Title: Goal-Conditioned Supervised Learning with Sub-Goal Prediction\n\nAbstract: Recently, a simple yet effective algorithm -- goal-conditioned supervised-learning (GCSL) -- was proposed to tackle goal-conditioned reinforcement-learning. GCSL is based on the principle of hindsight learning: by observing states visited in previously executed trajectories and treating them as attained goals, GCSL learns the corresponding actions via supervised learning. However, GCSL only learns a goal-conditioned policy, discarding other information in the process. Our insight is that the same hindsight principle can be used to learn to predict goal-conditioned sub-goals from the same trajectory. Based on this idea, we propose Trajectory Iterative Learner (TraIL), an extension of GCSL that further exploits the information in a trajectory, and uses it for learning to predict both actions and sub-goals. We investigate the settings in which TraIL can make better use of the data, and discover that for several popular problem settings, replacing real goals in GCSL with predicted TraIL sub-goals allows the agent to reach a greater set of goal states using the exact same data as GCSL, thereby improving its overall performance."]], "0_corpus": "arxiv", "1_conv_id": "7cffcfc7754c4aa781e6dff22cb8b716", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "who created gcsl?", "1_output": [["who created gcsl?", "Title: Supporting the GLAST User Community\n\nAbstract: The Gamma-ray Large Area Space Telescope (GLAST) Science Support Center (GSSC) is the scientific community's interface with GLAST. The GSSC will provide data, analysis software and documentation. In addition, the GSSC will administer the guest investigator program for NASA HQ. Consequently, the GSSC will provide proposal preparation tools to assist proposers in assessing the feasibility of observing sources of interest."]], "1_corpus": "arxiv"}
{"tstamp": 1722368932.7183, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "ea5e400b4db942b7adbea39fd7263a77", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n import pdb\n pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "0_corpus": "stackexchange", "1_conv_id": "ae5db96d81e84d8b8daa0d3142568b97", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone. \nThe video stream is written as \"socket\" to a file as H264 stream.\n$ ffmpeg -i [STREAM]\nInput #0, h264, from 'stream_h264':\n Duration: N/A, bitrate: N/A\n Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc\n\nReading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:\nmplayer -fs -demuxer h264es -benchmark stream_h264\n\nThis plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python? \nAll recommendations are more than welcome!\n"]], "1_corpus": "stackexchange"}
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