Scheduled Commit
Browse files
data/retrieval_individual-2b220830-844b-4128-a00a-03ab29c3b3ac.jsonl
CHANGED
@@ -376,3 +376,5 @@
|
|
376 |
{"tstamp": 1731355995.5178, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1731355995.2357, "finish": 1731355995.5178, "ip": "", "conv_id": "47661538c4d54b4f893c821e69945ec0", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
|
377 |
{"tstamp": 1731357872.8336, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1731357870.5195, "finish": 1731357872.8336, "ip": "", "conv_id": "808427ce00b1405b90ac0f58ce58393c", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "first order optimization with multiple EMAs", "output": [["first order optimization with multiple EMAs", "Title: The effect of the annealing temperature on the local distortion of La$_{0.67}$Ca$_{0.33}$MnO$_3$ thin films\n\nAbstract: Mn $K$-edge fluorescence data are presented for thin film samples (3000~\\AA) of Colossal Magnetoresistive (CMR) La$_{0.67}$Ca$_{0.33}$MnO$_3$: as-deposited, and post-annealed at 1000 K and 1200 K. The local distortion is analyzed in terms of three contributions: static, phonon, and an extra, temperature-dependent, polaron term. The polaron distortion is very small for the as-deposited sample and increases with the annealing temperature. In contrast, the static distortion in the samples decreases with the annealing temperature. Although the local structure of the as-deposited sample shows very little temperature dependence, the change in resistivity with temperature is the largest of these three thin film samples. The as-deposited sample also has the highest magnetoresistance (MR), which indicates some other mechanism may also contribute to the transport properties of CMR samples. We also discuss the relationship between local distortion and the magnetization of the sample."]], "corpus": "arxiv"}
|
378 |
{"tstamp": 1731357872.8336, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1731357870.5195, "finish": 1731357872.8336, "ip": "", "conv_id": "425b80930aaf4c9ab1afe12cdb938bad", "model_name": "text-embedding-004", "prompt": "first order optimization with multiple EMAs", "output": [["first order optimization with multiple EMAs", "Title: Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima\n\nAbstract: Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single solution. While this can be counteracted by applying niching strategies, the number of optima is nonetheless trivially bounded by the population size. Estimation-of-distribution algorithms (EDAs) are an alternative, maintaining a probabilistic model of the solution space instead of a population. Such a model is able to implicitly represent a solution set far larger than any realistic population size. To support the study of how optimization algorithms handle large sets of optima, we propose the test function EqualBlocksOneMax (EBOM). It has an easy fitness landscape with exponentially many optima. We show that the bivariate EDA mutual-information-maximizing input clustering, without any problem-specific modification, quickly generates a model that behaves very similarly to a theoretically ideal model for EBOM, which samples each of the exponentially many optima with the same maximal probability. We also prove via mathematical means that no univariate model can come close to having this property: If the probability to sample an optimum is at least inverse-polynomial, there is a Hamming ball of logarithmic radius such that, with high probability, each sample is in this ball."]], "corpus": "arxiv"}
|
|
|
|
|
|
376 |
{"tstamp": 1731355995.5178, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1731355995.2357, "finish": 1731355995.5178, "ip": "", "conv_id": "47661538c4d54b4f893c821e69945ec0", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
|
377 |
{"tstamp": 1731357872.8336, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1731357870.5195, "finish": 1731357872.8336, "ip": "", "conv_id": "808427ce00b1405b90ac0f58ce58393c", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "first order optimization with multiple EMAs", "output": [["first order optimization with multiple EMAs", "Title: The effect of the annealing temperature on the local distortion of La$_{0.67}$Ca$_{0.33}$MnO$_3$ thin films\n\nAbstract: Mn $K$-edge fluorescence data are presented for thin film samples (3000~\\AA) of Colossal Magnetoresistive (CMR) La$_{0.67}$Ca$_{0.33}$MnO$_3$: as-deposited, and post-annealed at 1000 K and 1200 K. The local distortion is analyzed in terms of three contributions: static, phonon, and an extra, temperature-dependent, polaron term. The polaron distortion is very small for the as-deposited sample and increases with the annealing temperature. In contrast, the static distortion in the samples decreases with the annealing temperature. Although the local structure of the as-deposited sample shows very little temperature dependence, the change in resistivity with temperature is the largest of these three thin film samples. The as-deposited sample also has the highest magnetoresistance (MR), which indicates some other mechanism may also contribute to the transport properties of CMR samples. We also discuss the relationship between local distortion and the magnetization of the sample."]], "corpus": "arxiv"}
|
378 |
{"tstamp": 1731357872.8336, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1731357870.5195, "finish": 1731357872.8336, "ip": "", "conv_id": "425b80930aaf4c9ab1afe12cdb938bad", "model_name": "text-embedding-004", "prompt": "first order optimization with multiple EMAs", "output": [["first order optimization with multiple EMAs", "Title: Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima\n\nAbstract: Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single solution. While this can be counteracted by applying niching strategies, the number of optima is nonetheless trivially bounded by the population size. Estimation-of-distribution algorithms (EDAs) are an alternative, maintaining a probabilistic model of the solution space instead of a population. Such a model is able to implicitly represent a solution set far larger than any realistic population size. To support the study of how optimization algorithms handle large sets of optima, we propose the test function EqualBlocksOneMax (EBOM). It has an easy fitness landscape with exponentially many optima. We show that the bivariate EDA mutual-information-maximizing input clustering, without any problem-specific modification, quickly generates a model that behaves very similarly to a theoretically ideal model for EBOM, which samples each of the exponentially many optima with the same maximal probability. We also prove via mathematical means that no univariate model can come close to having this property: If the probability to sample an optimum is at least inverse-polynomial, there is a Hamming ball of logarithmic radius such that, with high probability, each sample is in this ball."]], "corpus": "arxiv"}
|
379 |
+
{"tstamp": 1731363484.2759, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1731363484.0541, "finish": 1731363484.2759, "ip": "", "conv_id": "d8b77ca5c9b9471aa39afd3afe03e5a9", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "when was the first commercial cell phone released", "output": [["when was the first commercial cell phone released", "Mobile phone\n\nHistory A handheld mobile radio telephone service was envisioned in the early stages of radio engineering. In 1917, Finnish inventor Eric Tigerstedt filed a patent for a \"pocket-size folding telephone with a very thin carbon microphone\". Early predecessors of cellular phones included analog radio communications from ships and trains. The race to create truly portable telephone devices began after World War II, with developments taking place in many countries. The advances in mobile telephony have been traced in successive \"generations\", starting with the early zeroth-generation (0G) services, such as Bell System's Mobile Telephone Service and its successor, the Improved Mobile Telephone Service. These 0G systems were not cellular, supported a few simultaneous calls, and were very expensive.\nThe first handheld cellular mobile phone was demonstrated by John F. Mitchell and Martin Cooper of Motorola in 1973, using a handset weighing . The first commercial automated cellular network (1G) analog was launched in Japan by Nippon Telegraph and Telephone in 1979. This was followed in 1981 by the simultaneous launch of the Nordic Mobile Telephone (NMT) system in Denmark, Finland, Norway, and Sweden. Several other countries then followed in the early to mid-1980s. These first-generation (1G) systems could support far more simultaneous calls but still used analog cellular technology. In 1983, the DynaTAC 8000x was the first commercially available handheld mobile phone."]], "corpus": "wikipedia"}
|
380 |
+
{"tstamp": 1731363484.2759, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1731363484.0541, "finish": 1731363484.2759, "ip": "", "conv_id": "ffeea8d2606e478a8870377c852edfe8", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "when was the first commercial cell phone released", "output": [["when was the first commercial cell phone released", "History of mobile phones\n\nIn 1959, a private telephone company in Brewster, Kansas, US, the S&T Telephone Company, (still in business today) with the use of Motorola Radio Telephone equipment and a private tower facility, offered to the public mobile telephone services in that local area of NW Kansas. This system was a direct dial up service through their local switchboard, and was installed in many private vehicles including grain combines, trucks, and automobiles. For some as yet unknown reason, the system, after being placed online and operated for a very brief time period, was shut down. The management of the company was immediately changed, and the fully operable system and related equipment was immediately dismantled in early 1960, not to be seen again.\nIn 1966, Bulgaria presented the pocket mobile automatic phone RAT-0,5 combined with a base station RATZ-10 (RATC-10) on Interorgtechnika-66 international exhibition. One base station, connected to one telephone wire line, could serve up to six customers.\nOne of the first successful public commercial mobile phone networks was the ARP network in Finland, launched in 1971. Posthumously, ARP is sometimes viewed as a zero generation (0G) cellular network, being slightly above previous proprietary and limited coverage networks.\nHandheld mobile phone\nPrior to 1973, mobile telephony was limited to phones installed in cars and other vehicles. The first portable cellular phone commercially available for use on a cellular network was developed by E.F. Johnson and Millicom, Inc. It was introduced by Millicom subsidiary Comvik in Sweden in September 1981."]], "corpus": "wikipedia"}
|