Datasets:

mteb
/

arena-results

@@ -10,3 +10,5 @@
 {"tstamp": 1722619649.8625, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e009dbc6d4784fe5903303bb2dfda2e7", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "How can I avoid disinformation in open domain question answering?", "0_output": [["How can I avoid disinformation in open domain question answering?", "Title: t-Soft Update of Target Network for Deep Reinforcement Learning\n\nAbstract: This paper proposes a new robust update rule of target network for deep reinforcement learning (DRL), to replace the conventional update rule, given as an exponential moving average. The target network is for smoothly generating the reference signals for a main network in DRL, thereby reducing learning variance. The problem with its conventional update rule is the fact that all the parameters are smoothly copied with the same speed from the main network, even when some of them are trying to update toward the wrong directions. This behavior increases the risk of generating the wrong reference signals. Although slowing down the overall update speed is a naive way to mitigate wrong updates, it would decrease learning speed. To robustly update the parameters while keeping learning speed, a t-soft update method, which is inspired by student-t distribution, is derived with reference to the analogy between the exponential moving average and the normal distribution. Through the analysis of the derived t-soft update, we show that it takes over the properties of the student-t distribution. Specifically, with a heavy-tailed property of the student-t distribution, the t-soft update automatically excludes extreme updates that differ from past experiences. In addition, when the updates are similar to the past experiences, it can mitigate the learning delay by increasing the amount of updates. In PyBullet robotics simulations for DRL, an online actor-critic algorithm with the t-soft update outperformed the conventional methods in terms of the obtained return and/or its variance. From the training process by the t-soft update, we found that the t-soft update is globally consistent with the standard soft update, and the update rates are locally adjusted for acceleration or suppression."]], "0_corpus": "arxiv", "1_conv_id": "9e90964e7ec047c5aa7883f5a6854f25", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How can I avoid disinformation in open domain question answering?", "1_output": [["How can I avoid disinformation in open domain question answering?", "Title: Defending Against Disinformation Attacks in Open-Domain Question Answering\n\nAbstract: Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts."]], "1_corpus": "arxiv"}
 {"tstamp": 1722619675.5705, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "35ff0c119eec4739866126295e365998", "0_model_name": "voyage-multilingual-2", "0_prompt": "How can I avoid disinformation in open domain question answering?\n\n\n\n", "0_output": [["How can I avoid disinformation in open domain question answering?\n\n\n\n", "Title: Attacking Open-domain Question Answering by Injecting Misinformation\n\nAbstract: With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion."]], "0_corpus": "arxiv", "1_conv_id": "f10cc5e685e1469892556a962313541a", "1_model_name": "BM25", "1_prompt": "How can I avoid disinformation in open domain question answering?\n\n\n\n", "1_output": [["How can I avoid disinformation in open domain question answering?\n\n\n\n", "Title: Defending Against Disinformation Attacks in Open-Domain Question Answering\n\nAbstract: Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts."]], "1_corpus": "arxiv"}
 {"tstamp": 1722624874.1713, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "098aeb5c2b384c4f8712defdb0c68e7a", "0_model_name": "text-embedding-3-large", "0_prompt": "How do i get finasteride without prescription?", "0_output": [["How do i get finasteride without prescription?", "Finasteride\n\nFinasteride was developed by Merck under the code name MK-906.  A team led by chemist Gary Rasmusson and biologist Jerry Brooks developed potential 5α-reductase inhibitors based on transition state inhibitors, using an iterative process of molecular design, testing, and redesign.  In 1992, finasteride (5 mg) was approved by the U.S. Food and Drug Administration (FDA) for treatment of BPH, which Merck marketed under the brand name Proscar. Rasmusson and Brooks were awarded IPO's \"Inventor of the Year\" award in 1993 for their work on finasteride.  In 1997, Merck was successful in obtaining FDA approval for a second indication of finasteride (1 mg) for treatment of male pattern hair loss, which was marketed under the brand name Propecia. It was the first 5α-reductase inhibitor to be introduced and was followed by dutasteride in 2001. The first study of finasteride in the treatment of hirsutism in women was published in 1994.\nSociety and culture\nGeneric names\nFinasteride is the generic name of the drug and its , , , and , while finastéride is its . It is also known by its former developmental code names MK-906, YM-152, and L-652,931.\nBrand names\nFinasteride is marketed primarily under the brand names Propecia, for pattern hair loss, and Proscar, for BPH, both of which are products of Merck & Co. There is 1 mg of finasteride in Propecia and 5 mg in Proscar. Merck's patent on finasteride for the treatment of BPH expired in June 2006. Merck was awarded a separate patent for the use of finasteride to treat pattern hair loss and it expired in November 2013. Finasteride is also marketed under a variety of other brand names throughout the world."]], "0_corpus": "wikipedia", "1_conv_id": "466894e408704bca9746c97609416b5b", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How do i get finasteride without prescription?", "1_output": [["How do i get finasteride without prescription?", "Finasteride\n\nAdverse effects from finasteride are rare in men with already enlarged prostates; however, some men experience sexual dysfunction, depression, and breast enlargement. In some men, sexual dysfunction may persist after stopping the medication. It may also hide the early symptoms of certain forms of prostate cancer.\nFinasteride was patented in 1984 and approved for medical use in 1992. It is available as a generic medication. In 2021, it was the 88th most commonly prescribed medication in the United States, with more than 8million prescriptions.\nMedical uses\nFinasteride has been used for the treatment of symptomatic benign prostatic hyperplasia (BPH) in men with an enlarged prostate and for the treatment of male pattern hair loss (androgenetic alopecia) in men.\nEnlarged prostate\nPhysicians sometimes prescribe finasteride for the treatment of benign prostatic hyperplasia (BPH), informally known as an enlarged prostate. Finasteride may improve the symptoms associated with BPH such as difficulty urinating, getting up during the night to urinate, hesitation at the start and end of urination, and decreased urinary flow.\nThe use of the drug showed significant sexual adverse effects such as erectile dysfunction and less sexual desire, in particular when obstructive symptoms due to an enlarged prostate were present."]], "1_corpus": "wikipedia"}

 {"tstamp": 1722619649.8625, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e009dbc6d4784fe5903303bb2dfda2e7", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "How can I avoid disinformation in open domain question answering?", "0_output": [["How can I avoid disinformation in open domain question answering?", "Title: t-Soft Update of Target Network for Deep Reinforcement Learning\n\nAbstract: This paper proposes a new robust update rule of target network for deep reinforcement learning (DRL), to replace the conventional update rule, given as an exponential moving average. The target network is for smoothly generating the reference signals for a main network in DRL, thereby reducing learning variance. The problem with its conventional update rule is the fact that all the parameters are smoothly copied with the same speed from the main network, even when some of them are trying to update toward the wrong directions. This behavior increases the risk of generating the wrong reference signals. Although slowing down the overall update speed is a naive way to mitigate wrong updates, it would decrease learning speed. To robustly update the parameters while keeping learning speed, a t-soft update method, which is inspired by student-t distribution, is derived with reference to the analogy between the exponential moving average and the normal distribution. Through the analysis of the derived t-soft update, we show that it takes over the properties of the student-t distribution. Specifically, with a heavy-tailed property of the student-t distribution, the t-soft update automatically excludes extreme updates that differ from past experiences. In addition, when the updates are similar to the past experiences, it can mitigate the learning delay by increasing the amount of updates. In PyBullet robotics simulations for DRL, an online actor-critic algorithm with the t-soft update outperformed the conventional methods in terms of the obtained return and/or its variance. From the training process by the t-soft update, we found that the t-soft update is globally consistent with the standard soft update, and the update rates are locally adjusted for acceleration or suppression."]], "0_corpus": "arxiv", "1_conv_id": "9e90964e7ec047c5aa7883f5a6854f25", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How can I avoid disinformation in open domain question answering?", "1_output": [["How can I avoid disinformation in open domain question answering?", "Title: Defending Against Disinformation Attacks in Open-Domain Question Answering\n\nAbstract: Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts."]], "1_corpus": "arxiv"}
 {"tstamp": 1722619675.5705, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "35ff0c119eec4739866126295e365998", "0_model_name": "voyage-multilingual-2", "0_prompt": "How can I avoid disinformation in open domain question answering?\n\n\n\n", "0_output": [["How can I avoid disinformation in open domain question answering?\n\n\n\n", "Title: Attacking Open-domain Question Answering by Injecting Misinformation\n\nAbstract: With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion."]], "0_corpus": "arxiv", "1_conv_id": "f10cc5e685e1469892556a962313541a", "1_model_name": "BM25", "1_prompt": "How can I avoid disinformation in open domain question answering?\n\n\n\n", "1_output": [["How can I avoid disinformation in open domain question answering?\n\n\n\n", "Title: Defending Against Disinformation Attacks in Open-Domain Question Answering\n\nAbstract: Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts."]], "1_corpus": "arxiv"}
 {"tstamp": 1722624874.1713, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "098aeb5c2b384c4f8712defdb0c68e7a", "0_model_name": "text-embedding-3-large", "0_prompt": "How do i get finasteride without prescription?", "0_output": [["How do i get finasteride without prescription?", "Finasteride\n\nFinasteride was developed by Merck under the code name MK-906.  A team led by chemist Gary Rasmusson and biologist Jerry Brooks developed potential 5α-reductase inhibitors based on transition state inhibitors, using an iterative process of molecular design, testing, and redesign.  In 1992, finasteride (5 mg) was approved by the U.S. Food and Drug Administration (FDA) for treatment of BPH, which Merck marketed under the brand name Proscar. Rasmusson and Brooks were awarded IPO's \"Inventor of the Year\" award in 1993 for their work on finasteride.  In 1997, Merck was successful in obtaining FDA approval for a second indication of finasteride (1 mg) for treatment of male pattern hair loss, which was marketed under the brand name Propecia. It was the first 5α-reductase inhibitor to be introduced and was followed by dutasteride in 2001. The first study of finasteride in the treatment of hirsutism in women was published in 1994.\nSociety and culture\nGeneric names\nFinasteride is the generic name of the drug and its , , , and , while finastéride is its . It is also known by its former developmental code names MK-906, YM-152, and L-652,931.\nBrand names\nFinasteride is marketed primarily under the brand names Propecia, for pattern hair loss, and Proscar, for BPH, both of which are products of Merck & Co. There is 1 mg of finasteride in Propecia and 5 mg in Proscar. Merck's patent on finasteride for the treatment of BPH expired in June 2006. Merck was awarded a separate patent for the use of finasteride to treat pattern hair loss and it expired in November 2013. Finasteride is also marketed under a variety of other brand names throughout the world."]], "0_corpus": "wikipedia", "1_conv_id": "466894e408704bca9746c97609416b5b", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How do i get finasteride without prescription?", "1_output": [["How do i get finasteride without prescription?", "Finasteride\n\nAdverse effects from finasteride are rare in men with already enlarged prostates; however, some men experience sexual dysfunction, depression, and breast enlargement. In some men, sexual dysfunction may persist after stopping the medication. It may also hide the early symptoms of certain forms of prostate cancer.\nFinasteride was patented in 1984 and approved for medical use in 1992. It is available as a generic medication. In 2021, it was the 88th most commonly prescribed medication in the United States, with more than 8million prescriptions.\nMedical uses\nFinasteride has been used for the treatment of symptomatic benign prostatic hyperplasia (BPH) in men with an enlarged prostate and for the treatment of male pattern hair loss (androgenetic alopecia) in men.\nEnlarged prostate\nPhysicians sometimes prescribe finasteride for the treatment of benign prostatic hyperplasia (BPH), informally known as an enlarged prostate. Finasteride may improve the symptoms associated with BPH such as difficulty urinating, getting up during the night to urinate, hesitation at the start and end of urination, and decreased urinary flow.\nThe use of the drug showed significant sexual adverse effects such as erectile dysfunction and less sexual desire, in particular when obstructive symptoms due to an enlarged prostate were present."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722629605.2255, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "ac1d87f51835437b8ba2e2e76f179248", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "The most popular movie about the cyborg from the future", "0_output": [["The most popular movie about the cyborg from the future", "Science fiction film\n\nOne popular theme in science fiction film is whether robots will someday replace humans, a question raised in the film adaptation of Isaac Asimov's I, Robot (in jobs) and in the film Real Steel (in sports), or whether intelligent robots could develop a conscience and a motivation to protect, take over, or destroy the human race (as depicted in The Terminator, Transformers, and in Avengers: Age of Ultron). Another theme is remote telepresence via androids as depicted in Surrogates and Iron Man 3. As artificial intelligence becomes smarter due to increasing computer power, some sci-fi dreams have already been realized. For example, the computer Deep Blue beat the world chess champion in 1997 and a documentary film, Game Over: Kasparov and the Machine, was released in 2003. Another famous computer called Watson defeated the two best human Jeopardy (game show) players in 2011 and a NOVA documentary film, Smartest Machine on Earth, was released in the same year.\nBuilding-size robots are also becoming a popular theme in movies as featured in Pacific Rim. Future live action films may include an adaptation of popular television series like Voltron and Robotech. The CGI robots of Pacific Rim and the Power Rangers (2017) reboot was greatly improved as compared to the original Mighty Morphin Power Rangers: The Movie (1995). While \"size does matter\", a famous tagline of the movie Godzilla, incredibly small robots, called nanobots, do matter as well (e.g. Borg nanoprobes in Star Trek and nanites in I, Robot)."]], "0_corpus": "wikipedia", "1_conv_id": "26cd7579b4d848ab8c644dc32c73b4c4", "1_model_name": "BM25", "1_prompt": "The most popular movie about the cyborg from the future", "1_output": [["The most popular movie about the cyborg from the future", "James Cameron\n\nIn 1982, inspired by John Carpenter's horror film Halloween (1978), as well as a nightmare about an invincible robot hit-man sent from the future to assassinate him, Cameron wrote the script for The Terminator (1984), a sci-fi action film about a cyborg sent from the future to carry out a lethal mission. Cameron wanted to sell the script so that he could direct the movie. Whilst some film studios expressed interest in the project, many executives were unwilling to let a new and unfamiliar director make the movie. Gale Anne Hurd, a colleague and founder of Pacific Western Productions, agreed to buy Cameron's script for one dollar, on the condition that Cameron direct the film. He convinced the president of Hemdale Pictures to make the film, with Cameron as director and Hurd as a producer. Lance Henriksen, who starred in Piranha II: The Spawning, was considered for the lead role, but Cameron decided that Arnold Schwarzenegger was more suitable as the cyborg villain due to his bodybuilder appearance. Henriksen was given a smaller role instead. Michael Biehn and Linda Hamilton also joined the cast. The Terminator was a box office success, exceeding expectations set by Orion Pictures. The film proved popular with audiences and earned over $78 million worldwide. George Perry of the BBC praised Cameron's direction, writing \"Cameron laces the action with ironic jokes, but never lets up on hinting that the terror may strike at any moment\". In 2008, the film was selected for preservation in the United States National Film Registry, being deemed \"culturally, historically, or aesthetically significant\"."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722629629.8812, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "5ae2f6045baf4711af29da95eb89c574", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "The most popular movie about the cyborg from the future", "0_output": [["The most popular movie about the cyborg from the future", "Cyborg She\n\nThe story then jumps to one year later, as Jiro again celebrates his birthday alone in the same restaurant. All of a sudden, the same-looking girl appears in front of him. Jiro's older self from 65 years in the future had sent this girl to save him from a disastrous fate. She was actually a cyborg, modelled after the girl he met a year before in 2007. While he rejoices in her presence, the restaurant is suddenly attacked by a gunman, but she saves him and the other guests by throwing the gunman out of the window. Despite her 'cute' outward appearance, she is incredibly strong and behaves erratically. Later, in Jiro's home, she reveals her true identity by showing him a 3D projection of a video in which an elderly Jiro from the future warns him about an upcoming disaster. The old Jiro told him that the shooting at the restaurant paralyzed him for life. However, a lottery ticket he bought earlier was fortunate for him. He spent all his time and money on one thing: creating the cyborg girl to save his past self about 60 years ago. Now, he has recreated the history of his timeline by sending her. This was not supposed to happen, but things would correct themselves by recalibrating to the right dimension. In a short span of time, she becomes Jiro's protector as well as a loyal friend and they both share some wonderful moments. She also saves many other lives from tragic deaths that old Jiro had regretted witnessing.\nOver time, Jiro not only becomes dependent on, but also falls in love with her. However, when she cannot return his feelings, he gets irritated and forbids her from seeing him unless she can do so. He begins to regret this, especially when it becomes apparent that she is still helping him while staying out of his sight. Another disaster soon occurs: a gigantic earthquake completely devastates Tokyo. As his apartment block collapses, she appears to help him, but even her superhuman strength isn't enough to save him. After telling Jiro that she now understands his feelings, she is destroyed while saving him. Later, distraught, Jiro finds her body and spends the next 61 years trying to rebuild her. He eventually succeeds but dies shortly after."]], "0_corpus": "wikipedia", "1_conv_id": "10146f56cf5743cf892a73530bc29b07", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "The most popular movie about the cyborg from the future", "1_output": [["The most popular movie about the cyborg from the future", "Cyborg (film)\n\nCyborg is a 1989 American martial-arts cyberpunk film directed by Albert Pyun. Jean-Claude van Damme stars as Gibson Rickenbacker, a mercenary who battles a group of murderous marauders led by Fender Tremolo (Vincent Klyn) along the East coast of the United States in a post-apocalyptic future. It was followed by the sequels Cyborg 2 (1993) and Cyborg 3: The Recycler (1994).\nPlot\nA plague known as the living death cripples civilization. A small group of surviving scientists and doctors — located in Atlanta, home of the CDC — work on a cure to save what remains of humanity. To complete their work they need information stored on a computer system in New York City. Pearl Prophet volunteers for the dangerous courier mission and is made into a cyborg through surgical augmentation.\nPearl, accompanied by bodyguard Marshall Strat, retrieves the data in New York but is pursued by the vicious Fender Tremolo and his gang of pirates. Fender wants the cure so he can have a monopoly on its production. Strat, badly injured while fighting the pirates, tells Pearl to leave him and find a mercenary, known as a \"slinger\", who can escort her to safety. She gets cornered but is saved by a slinger named Gibson Rickenbacker. After she explains her situation, they are overrun by Fender's gang, and Gibson is knocked out by falling debris. Fender demands that she accompany him to Atlanta or die."]], "1_corpus": "wikipedia"}

data/retrieval_individual-1948e9c4-e613-4487-b12e-0e988013b994.jsonl CHANGED Viewed

@@ -70,3 +70,5 @@
 {"tstamp": 1722629510.3989, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722629510.063, "finish": 1722629510.3989, "ip": "", "conv_id": "1f7d4077a3454d589398c874e9161dff", "model_name": "text-embedding-004", "prompt": "The most movie about the cyborg from the future", "output": [["The most movie about the cyborg from the future", "Cyborg (film)\n\nCyborg is a 1989 American martial-arts cyberpunk film directed by Albert Pyun. Jean-Claude van Damme stars as Gibson Rickenbacker, a mercenary who battles a group of murderous marauders led by Fender Tremolo (Vincent Klyn) along the East coast of the United States in a post-apocalyptic future. It was followed by the sequels Cyborg 2 (1993) and Cyborg 3: The Recycler (1994).\nPlot\nA plague known as the living death cripples civilization. A small group of surviving scientists and doctors — located in Atlanta, home of the CDC — work on a cure to save what remains of humanity. To complete their work they need information stored on a computer system in New York City. Pearl Prophet volunteers for the dangerous courier mission and is made into a cyborg through surgical augmentation.\nPearl, accompanied by bodyguard Marshall Strat, retrieves the data in New York but is pursued by the vicious Fender Tremolo and his gang of pirates. Fender wants the cure so he can have a monopoly on its production. Strat, badly injured while fighting the pirates, tells Pearl to leave him and find a mercenary, known as a \"slinger\", who can escort her to safety. She gets cornered but is saved by a slinger named Gibson Rickenbacker. After she explains her situation, they are overrun by Fender's gang, and Gibson is knocked out by falling debris. Fender demands that she accompany him to Atlanta or die."]], "corpus": "wikipedia"}
 {"tstamp": 1722629531.8408, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722629523.1325, "finish": 1722629531.8408, "ip": "", "conv_id": "ac1d87f51835437b8ba2e2e76f179248", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "The most popular movie about the cyborg from the future", "output": [["The most popular movie about the cyborg from the future", "Science fiction film\n\nOne popular theme in science fiction film is whether robots will someday replace humans, a question raised in the film adaptation of Isaac Asimov's I, Robot (in jobs) and in the film Real Steel (in sports), or whether intelligent robots could develop a conscience and a motivation to protect, take over, or destroy the human race (as depicted in The Terminator, Transformers, and in Avengers: Age of Ultron). Another theme is remote telepresence via androids as depicted in Surrogates and Iron Man 3. As artificial intelligence becomes smarter due to increasing computer power, some sci-fi dreams have already been realized. For example, the computer Deep Blue beat the world chess champion in 1997 and a documentary film, Game Over: Kasparov and the Machine, was released in 2003. Another famous computer called Watson defeated the two best human Jeopardy (game show) players in 2011 and a NOVA documentary film, Smartest Machine on Earth, was released in the same year.\nBuilding-size robots are also becoming a popular theme in movies as featured in Pacific Rim. Future live action films may include an adaptation of popular television series like Voltron and Robotech. The CGI robots of Pacific Rim and the Power Rangers (2017) reboot was greatly improved as compared to the original Mighty Morphin Power Rangers: The Movie (1995). While \"size does matter\", a famous tagline of the movie Godzilla, incredibly small robots, called nanobots, do matter as well (e.g. Borg nanoprobes in Star Trek and nanites in I, Robot)."]], "corpus": "wikipedia"}
 {"tstamp": 1722629531.8408, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722629523.1325, "finish": 1722629531.8408, "ip": "", "conv_id": "26cd7579b4d848ab8c644dc32c73b4c4", "model_name": "BM25", "prompt": "The most popular movie about the cyborg from the future", "output": [["The most popular movie about the cyborg from the future", "James Cameron\n\nIn 1982, inspired by John Carpenter's horror film Halloween (1978), as well as a nightmare about an invincible robot hit-man sent from the future to assassinate him, Cameron wrote the script for The Terminator (1984), a sci-fi action film about a cyborg sent from the future to carry out a lethal mission. Cameron wanted to sell the script so that he could direct the movie. Whilst some film studios expressed interest in the project, many executives were unwilling to let a new and unfamiliar director make the movie. Gale Anne Hurd, a colleague and founder of Pacific Western Productions, agreed to buy Cameron's script for one dollar, on the condition that Cameron direct the film. He convinced the president of Hemdale Pictures to make the film, with Cameron as director and Hurd as a producer. Lance Henriksen, who starred in Piranha II: The Spawning, was considered for the lead role, but Cameron decided that Arnold Schwarzenegger was more suitable as the cyborg villain due to his bodybuilder appearance. Henriksen was given a smaller role instead. Michael Biehn and Linda Hamilton also joined the cast. The Terminator was a box office success, exceeding expectations set by Orion Pictures. The film proved popular with audiences and earned over $78 million worldwide. George Perry of the BBC praised Cameron's direction, writing \"Cameron laces the action with ironic jokes, but never lets up on hinting that the terror may strike at any moment\". In 2008, the film was selected for preservation in the United States National Film Registry, being deemed \"culturally, historically, or aesthetically significant\"."]], "corpus": "wikipedia"}

 {"tstamp": 1722629510.3989, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722629510.063, "finish": 1722629510.3989, "ip": "", "conv_id": "1f7d4077a3454d589398c874e9161dff", "model_name": "text-embedding-004", "prompt": "The most movie about the cyborg from the future", "output": [["The most movie about the cyborg from the future", "Cyborg (film)\n\nCyborg is a 1989 American martial-arts cyberpunk film directed by Albert Pyun. Jean-Claude van Damme stars as Gibson Rickenbacker, a mercenary who battles a group of murderous marauders led by Fender Tremolo (Vincent Klyn) along the East coast of the United States in a post-apocalyptic future. It was followed by the sequels Cyborg 2 (1993) and Cyborg 3: The Recycler (1994).\nPlot\nA plague known as the living death cripples civilization. A small group of surviving scientists and doctors — located in Atlanta, home of the CDC — work on a cure to save what remains of humanity. To complete their work they need information stored on a computer system in New York City. Pearl Prophet volunteers for the dangerous courier mission and is made into a cyborg through surgical augmentation.\nPearl, accompanied by bodyguard Marshall Strat, retrieves the data in New York but is pursued by the vicious Fender Tremolo and his gang of pirates. Fender wants the cure so he can have a monopoly on its production. Strat, badly injured while fighting the pirates, tells Pearl to leave him and find a mercenary, known as a \"slinger\", who can escort her to safety. She gets cornered but is saved by a slinger named Gibson Rickenbacker. After she explains her situation, they are overrun by Fender's gang, and Gibson is knocked out by falling debris. Fender demands that she accompany him to Atlanta or die."]], "corpus": "wikipedia"}
 {"tstamp": 1722629531.8408, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722629523.1325, "finish": 1722629531.8408, "ip": "", "conv_id": "ac1d87f51835437b8ba2e2e76f179248", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "The most popular movie about the cyborg from the future", "output": [["The most popular movie about the cyborg from the future", "Science fiction film\n\nOne popular theme in science fiction film is whether robots will someday replace humans, a question raised in the film adaptation of Isaac Asimov's I, Robot (in jobs) and in the film Real Steel (in sports), or whether intelligent robots could develop a conscience and a motivation to protect, take over, or destroy the human race (as depicted in The Terminator, Transformers, and in Avengers: Age of Ultron). Another theme is remote telepresence via androids as depicted in Surrogates and Iron Man 3. As artificial intelligence becomes smarter due to increasing computer power, some sci-fi dreams have already been realized. For example, the computer Deep Blue beat the world chess champion in 1997 and a documentary film, Game Over: Kasparov and the Machine, was released in 2003. Another famous computer called Watson defeated the two best human Jeopardy (game show) players in 2011 and a NOVA documentary film, Smartest Machine on Earth, was released in the same year.\nBuilding-size robots are also becoming a popular theme in movies as featured in Pacific Rim. Future live action films may include an adaptation of popular television series like Voltron and Robotech. The CGI robots of Pacific Rim and the Power Rangers (2017) reboot was greatly improved as compared to the original Mighty Morphin Power Rangers: The Movie (1995). While \"size does matter\", a famous tagline of the movie Godzilla, incredibly small robots, called nanobots, do matter as well (e.g. Borg nanoprobes in Star Trek and nanites in I, Robot)."]], "corpus": "wikipedia"}
 {"tstamp": 1722629531.8408, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722629523.1325, "finish": 1722629531.8408, "ip": "", "conv_id": "26cd7579b4d848ab8c644dc32c73b4c4", "model_name": "BM25", "prompt": "The most popular movie about the cyborg from the future", "output": [["The most popular movie about the cyborg from the future", "James Cameron\n\nIn 1982, inspired by John Carpenter's horror film Halloween (1978), as well as a nightmare about an invincible robot hit-man sent from the future to assassinate him, Cameron wrote the script for The Terminator (1984), a sci-fi action film about a cyborg sent from the future to carry out a lethal mission. Cameron wanted to sell the script so that he could direct the movie. Whilst some film studios expressed interest in the project, many executives were unwilling to let a new and unfamiliar director make the movie. Gale Anne Hurd, a colleague and founder of Pacific Western Productions, agreed to buy Cameron's script for one dollar, on the condition that Cameron direct the film. He convinced the president of Hemdale Pictures to make the film, with Cameron as director and Hurd as a producer. Lance Henriksen, who starred in Piranha II: The Spawning, was considered for the lead role, but Cameron decided that Arnold Schwarzenegger was more suitable as the cyborg villain due to his bodybuilder appearance. Henriksen was given a smaller role instead. Michael Biehn and Linda Hamilton also joined the cast. The Terminator was a box office success, exceeding expectations set by Orion Pictures. The film proved popular with audiences and earned over $78 million worldwide. George Perry of the BBC praised Cameron's direction, writing \"Cameron laces the action with ironic jokes, but never lets up on hinting that the terror may strike at any moment\". In 2008, the film was selected for preservation in the United States National Film Registry, being deemed \"culturally, historically, or aesthetically significant\"."]], "corpus": "wikipedia"}
+{"tstamp": 1722629622.7145, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722629622.509, "finish": 1722629622.7145, "ip": "", "conv_id": "5ae2f6045baf4711af29da95eb89c574", "model_name": "GritLM/GritLM-7B", "prompt": "The most popular movie about the cyborg from the future", "output": [["The most popular movie about the cyborg from the future", "Cyborg She\n\nThe story then jumps to one year later, as Jiro again celebrates his birthday alone in the same restaurant. All of a sudden, the same-looking girl appears in front of him. Jiro's older self from 65 years in the future had sent this girl to save him from a disastrous fate. She was actually a cyborg, modelled after the girl he met a year before in 2007. While he rejoices in her presence, the restaurant is suddenly attacked by a gunman, but she saves him and the other guests by throwing the gunman out of the window. Despite her 'cute' outward appearance, she is incredibly strong and behaves erratically. Later, in Jiro's home, she reveals her true identity by showing him a 3D projection of a video in which an elderly Jiro from the future warns him about an upcoming disaster. The old Jiro told him that the shooting at the restaurant paralyzed him for life. However, a lottery ticket he bought earlier was fortunate for him. He spent all his time and money on one thing: creating the cyborg girl to save his past self about 60 years ago. Now, he has recreated the history of his timeline by sending her. This was not supposed to happen, but things would correct themselves by recalibrating to the right dimension. In a short span of time, she becomes Jiro's protector as well as a loyal friend and they both share some wonderful moments. She also saves many other lives from tragic deaths that old Jiro had regretted witnessing.\nOver time, Jiro not only becomes dependent on, but also falls in love with her. However, when she cannot return his feelings, he gets irritated and forbids her from seeing him unless she can do so. He begins to regret this, especially when it becomes apparent that she is still helping him while staying out of his sight. Another disaster soon occurs: a gigantic earthquake completely devastates Tokyo. As his apartment block collapses, she appears to help him, but even her superhuman strength isn't enough to save him. After telling Jiro that she now understands his feelings, she is destroyed while saving him. Later, distraught, Jiro finds her body and spends the next 61 years trying to rebuild her. He eventually succeeds but dies shortly after."]], "corpus": "wikipedia"}
+{"tstamp": 1722629622.7145, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722629622.509, "finish": 1722629622.7145, "ip": "", "conv_id": "10146f56cf5743cf892a73530bc29b07", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "The most popular movie about the cyborg from the future", "output": [["The most popular movie about the cyborg from the future", "Cyborg (film)\n\nCyborg is a 1989 American martial-arts cyberpunk film directed by Albert Pyun. Jean-Claude van Damme stars as Gibson Rickenbacker, a mercenary who battles a group of murderous marauders led by Fender Tremolo (Vincent Klyn) along the East coast of the United States in a post-apocalyptic future. It was followed by the sequels Cyborg 2 (1993) and Cyborg 3: The Recycler (1994).\nPlot\nA plague known as the living death cripples civilization. A small group of surviving scientists and doctors — located in Atlanta, home of the CDC — work on a cure to save what remains of humanity. To complete their work they need information stored on a computer system in New York City. Pearl Prophet volunteers for the dangerous courier mission and is made into a cyborg through surgical augmentation.\nPearl, accompanied by bodyguard Marshall Strat, retrieves the data in New York but is pursued by the vicious Fender Tremolo and his gang of pirates. Fender wants the cure so he can have a monopoly on its production. Strat, badly injured while fighting the pirates, tells Pearl to leave him and find a mercenary, known as a \"slinger\", who can escort her to safety. She gets cornered but is saved by a slinger named Gibson Rickenbacker. After she explains her situation, they are overrun by Fender's gang, and Gibson is knocked out by falling debris. Fender demands that she accompany him to Atlanta or die."]], "corpus": "wikipedia"}