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 {"tstamp": 1722325027.3, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722325026.7929, "finish": 1722325027.3, "ip": "", "conv_id": "9332df36b036426a98f95027f37c0247", "model_name": "text-embedding-3-large", "prompt": "A metric for diversity in rankings in retrieval", "output": [["A metric for diversity in rankings in retrieval", "Title: Structural Learning of Diverse Ranking\n\nAbstract: Relevance and diversity are both crucial criteria for an effective search system. In this paper, we propose a unified learning framework for simultaneously optimizing both relevance and diversity. Specifically, the problem is formalized as a structural learning framework optimizing Diversity-Correlated Evaluation Measures (DCEM), such as ERR-IA, a-NDCG and NRBP. Within this framework, the discriminant function is defined to be a bi-criteria objective maximizing the sum of the relevance scores and dissimilarities (or diversity) among the documents. Relevance and diversity features are utilized to define the relevance scores and dissimilarities, respectively. Compared with traditional methods, the advantages of our approach lie in that: (1) Directly optimizing DCEM as the loss function is more fundamental for the task; (2) Our framework does not rely on explicit diversity information such as subtopics, thus is more adaptive to real application; (3) The representation of diversity as the feature-based scoring function is more flexible to incorporate rich diversity-based features into the learning framework. Extensive experiments on the public TREC datasets show that our approach significantly outperforms state-of-the-art diversification approaches, which validate the above advantages."]], "corpus": "arxiv"}
 {"tstamp": 1722325255.6968, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722325255.5049, "finish": 1722325255.6968, "ip": "", "conv_id": "8fd0f41e8c554c77a868abae1d83248a", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Which programming paradigm supposedly eliminates side-effects?", "output": [["Which programming paradigm supposedly eliminates side-effects?", "Stackoverflow Stackexchange\n\nQ: I want to call it side-effect programming, but there's gotta be a better term for this What is the name of the following programming paradigm:\nCode is executed based on a set of logical tests resolving to true (the clause). The clause is comprised of operators and operands. Each operand is a value/object.\nInstead of evaluating the clause explicitly, as is done in imperative languages (e.g., normal flow control like if(){}), the clause is declared and bound to the resulting code. When the clause is satisfied, at any point in the future, the code will execute. \nSo, it's basically a dependency tree that re-evaluates whether the code should execute whenever a dependency changes state.\nE.g.,\nwhen(a && b && c < 3 && d.changes())\n{\n  runThisCode();\n}\n\nI'm looking for a more formal name and definition, and I haven't come up with anything after searching for it. It's somewhere between declarative and imperative, but I've never seen a language or paradigm that lets one do this.\nThanks,\nSean\n\nA: Maybe it is dataflow programming? Or reactive programming?\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722325255.6968, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722325255.5049, "finish": 1722325255.6968, "ip": "", "conv_id": "7a4eb2fc2e1c49adb1cf34025bab88e4", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which programming paradigm supposedly eliminates side-effects?", "output": [["Which programming paradigm supposedly eliminates side-effects?", "Stackoverflow Stackexchange\n\nQ: I want to call it side-effect programming, but there's gotta be a better term for this What is the name of the following programming paradigm:\nCode is executed based on a set of logical tests resolving to true (the clause). The clause is comprised of operators and operands. Each operand is a value/object.\nInstead of evaluating the clause explicitly, as is done in imperative languages (e.g., normal flow control like if(){}), the clause is declared and bound to the resulting code. When the clause is satisfied, at any point in the future, the code will execute. \nSo, it's basically a dependency tree that re-evaluates whether the code should execute whenever a dependency changes state.\nE.g.,\nwhen(a && b && c < 3 && d.changes())\n{\n  runThisCode();\n}\n\nI'm looking for a more formal name and definition, and I haven't come up with anything after searching for it. It's somewhere between declarative and imperative, but I've never seen a language or paradigm that lets one do this.\nThanks,\nSean\n\nA: Maybe it is dataflow programming? Or reactive programming?\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722326117.1111, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722326114.3629, "finish": 1722326117.1111, "ip": "", "conv_id": "fc4c84e604734519ab3644eee251be4a", "model_name": "text-embedding-004", "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"}
+{"tstamp": 1722326117.1111, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722326114.3629, "finish": 1722326117.1111, "ip": "", "conv_id": "20d1e09e55c3417987a2411ee289b0af", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}