Scheduled Commit

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

@@ -1,3 +1,4 @@
 {"tstamp": 1722441607.5868, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e7d52d0aadc94702b20e07a14e36ab57", "0_model_name": "BM25", "0_prompt": "when did the ottoman empire surrender in ww1", "0_output": [["when did the ottoman empire surrender in ww1", "Minority rights\n\nThe 1856 Congress of Paris paid special attention to the status of Jews and Christians in the Ottoman Empire. In Britain, William Gladstone made the massacres of Bulgarians by the Ottoman Empire a major campaign issue and demanded international attention. The Congress of Berlin in 1878 dealt with the status of Jews in Romania, especially, and also Serbia, and Bulgaria. On the whole, the 19th-century congresses failed to impose significant reforms.\nThe first minority rights were proclaimed and enacted by the revolutionary Parliament of Hungary in July 1849. Minority rights were codified in Austrian law in 1867.\nRussia was especially active in protecting Orthodox Christians and Slavic peoples under the control of the Ottoman Empire.  However the Russian government tolerated vicious pogroms against Jews in its villages.  Russia was widely attacked for this policy.  By contrast there was little or no international outrage regarding the treatment of other minorities, such as black people in the southern United States before the 1950s when African colonies became independent.\nBefore the World War I, only three European countries declared ethnic minority rights, and enacted minority-protecting laws: the first was Hungary (1849 and 1868), the second was Austria (1867), and the third was Belgium (1898). In the pre-WW1 era, the legal systems of other European countries did not allow the use of European minority languages in primary schools, in cultural institutions, in offices of public administration and at the legal courts.\nMinority rights at the Paris Peace Conference of 1919"]], "0_corpus": "wikipedia", "1_conv_id": "5289e5d21c2b4d949286103c399f1e19", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "when did the ottoman empire surrender in ww1", "1_output": [["when did the ottoman empire surrender in ww1", "Ottoman Empire in World War I\n\nThe Ottoman Empire was one of the Central Powers of World War I. It entered the war on 29 October 1914 with a small surprise attack on the Black Sea coast of Russia, which prompted Russia to declare war on 2 November 1914. Ottoman forces fought the Entente in the Balkans and the Middle Eastern theatre of World War I. The Ottoman Empire's defeat in the war in 1918 was crucial in the eventual dissolution of the empire in 1922.\nEntry into World War I\nThe Ottoman entry into World War I was the result of two recently purchased ships of its navy, still manned by their German crews and commanded by their German admiral, carrying out the Black Sea Raid on 29 October 1914. There were a number of factors that conspired to influence the Ottoman government and encourage them to enter the war. The political reasons for the Ottoman sultan's entry into the war are disputed, and the Ottoman Empire was an agricultural state in an age of industrial warfare. Also, the economic resources of the empire were depleted by the cost of the Balkan Wars of 1912 and 1913. The reasons for the Ottoman action were not immediately clear."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722441638.7331, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "03187db5197c4fe7b645e21973a23910", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "can minecraft wii u play with xbox?", "0_output": [["can minecraft wii u play with xbox?", "Gaming Stackexchange\n\nQ: Can you play minecraft on Wii U and Xbox together on mulitplayer? I want to play with my friend but don't know how!\n\nA: What you are referring to is called \"Cross Platform Multiplayer\" Where two or more players on different consoles can play the same game. Currently, Cross platform multiplayer is very rare, and few games have this feature available.\nThere have been rumors that improved Cross platform multiplayer may be included at a future date on some or all of the minecraft versions, but this is not yet confirmed.(http://www.ign.com/articles/2015/08/05/gamescom-2015-mojang-wants-minecraft-to-work-cross-platform) \nUnfortunatly, at the moment, You can only play Minecraft online multiplayer if you, and everyone you are trying to play with are using the same version of minecraft on the same console or platform. \n*EDIT\nAs Pointed out by colorfusion in the comments, Minecraft Pocket Edition is compatible with Minecraft Windows 10 Edition. (Here's an article about it http://www.pcworld.com/article/2982932/software-games/minecraft-pocket-edition-now-lets-windows-10-ios-and-android-gamers-play-together.html)\n"]], "0_corpus": "stackexchange", "1_conv_id": "4405fb6401d8453189dbfe07d1e675e3", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "can minecraft wii u play with xbox?", "1_output": [["can minecraft wii u play with xbox?", "Gaming Stackexchange\n\nQ: Can you play minecraft on Wii U and Xbox together on mulitplayer? I want to play with my friend but don't know how!\n\nA: What you are referring to is called \"Cross Platform Multiplayer\" Where two or more players on different consoles can play the same game. Currently, Cross platform multiplayer is very rare, and few games have this feature available.\nThere have been rumors that improved Cross platform multiplayer may be included at a future date on some or all of the minecraft versions, but this is not yet confirmed.(http://www.ign.com/articles/2015/08/05/gamescom-2015-mojang-wants-minecraft-to-work-cross-platform) \nUnfortunatly, at the moment, You can only play Minecraft online multiplayer if you, and everyone you are trying to play with are using the same version of minecraft on the same console or platform. \n*EDIT\nAs Pointed out by colorfusion in the comments, Minecraft Pocket Edition is compatible with Minecraft Windows 10 Edition. (Here's an article about it http://www.pcworld.com/article/2982932/software-games/minecraft-pocket-edition-now-lets-windows-10-ios-and-android-gamers-play-together.html)\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722441688.0512, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "6b45321f7e8743f6ba37ad8fbb58bcba", "0_model_name": "embed-english-v3.0", "0_prompt": "A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "0_output": [["A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue\n\nAbstract: Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018)."]], "0_corpus": "arxiv", "1_conv_id": "d4ac56d1a9f84dc5ba91f489a57a6eac", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "1_output": [["A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue\n\nAbstract: Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018)."]], "1_corpus": "arxiv"}
+{"tstamp": 1722441726.5986, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "0eb1b97ea1ea4813bcb0d07561d9762b", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Paper exploring algorithmic fidelity in language models for social science research.", "0_output": [["Paper exploring algorithmic fidelity in language models for social science research.", "Title: Analysis and modeling of scale-invariance in plankton abundance\n\nAbstract: The power spectrum, $S$, of horizontal transects of plankton abundance are often observed to have a power-law dependence on wavenumber, $k$, with exponent close to -2: $S(k)\\propto k^{-2}$ over a wide range of scales. I present power spectral analyses of aircraft lidar measurements of phytoplankton abundance from scales of 1 to 100 km. A power spectrum $S(k)\\propto k^{-2}$ is obtained. As a model for this observation, I consider a stochastic growth equation where the rate of change of plankton abundance is determined by turbulent mixing, modeled as a diffusion process in two dimensions, and exponential growth with a stochastically variable net growth rate representing a fluctuating environment. The model predicts a lognormal distribution of abundance and a power spectrum of horizontal transects $S(k)\\propto k^{-1.8}$, close to the observed spectrum. The model equation predicts that the power spectrum of variations in abundance in time at a point in space is $S(f)\\propto f^{-1.5}$ (where $f$ is the frequency). Time series analysis of local variations of phytoplankton and zooplankton yield a power-law power spectrum with exponents -1.3 and -1.2, respectively from time scales of one hour to one year. These values are roughly consistent with the model prediction of -1.5. The distribution of abundances is nearly lognormal as predicted. The model may be more generally applicable than for the spatial distribution of plankton. I relate the model predictions to observations of spatial patchiness in vegetation."]], "0_corpus": "arxiv", "1_conv_id": "519f89cd54734d5a8b84fc365f499beb", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Paper exploring algorithmic fidelity in language models for social science research.", "1_output": [["Paper exploring algorithmic fidelity in language models for social science research.", "Title: Neural language representations predict outcomes of scientific research\n\nAbstract: Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and allocate resources. Using a dataset of approximately 170,000 correlational findings extracted from leading social science journals, we show that a trained neural network can accurately predict the reported correlations using only the text descriptions of the correlates. Accurate predictive models such as these can guide scientists towards promising untested correlates, better quantify the information gained from new findings, and has implications for moving artificial intelligence systems from predicting structures to predicting relationships in the real world."]], "1_corpus": "arxiv"}

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

@@ -4,3 +4,5 @@
 {"tstamp": 1722441623.0554, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722441622.6634, "finish": 1722441623.0554, "ip": "", "conv_id": "4405fb6401d8453189dbfe07d1e675e3", "model_name": "GritLM/GritLM-7B", "prompt": "can minecraft wii u play with xbox?", "output": [["can minecraft wii u play with xbox?", "Gaming Stackexchange\n\nQ: Can you play minecraft on Wii U and Xbox together on mulitplayer? I want to play with my friend but don't know how!\n\nA: What you are referring to is called \"Cross Platform Multiplayer\" Where two or more players on different consoles can play the same game. Currently, Cross platform multiplayer is very rare, and few games have this feature available.\nThere have been rumors that improved Cross platform multiplayer may be included at a future date on some or all of the minecraft versions, but this is not yet confirmed.(http://www.ign.com/articles/2015/08/05/gamescom-2015-mojang-wants-minecraft-to-work-cross-platform) \nUnfortunatly, at the moment, You can only play Minecraft online multiplayer if you, and everyone you are trying to play with are using the same version of minecraft on the same console or platform. \n*EDIT\nAs Pointed out by colorfusion in the comments, Minecraft Pocket Edition is compatible with Minecraft Windows 10 Edition. (Here's an article about it http://www.pcworld.com/article/2982932/software-games/minecraft-pocket-edition-now-lets-windows-10-ios-and-android-gamers-play-together.html)\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722441665.4403, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722441647.9506, "finish": 1722441665.4403, "ip": "", "conv_id": "6b45321f7e8743f6ba37ad8fbb58bcba", "model_name": "embed-english-v3.0", "prompt": "A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "output": [["A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue\n\nAbstract: Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018)."]], "corpus": "arxiv"}
 {"tstamp": 1722441665.4403, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722441647.9506, "finish": 1722441665.4403, "ip": "", "conv_id": "d4ac56d1a9f84dc5ba91f489a57a6eac", "model_name": "GritLM/GritLM-7B", "prompt": "A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "output": [["A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.", "Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue\n\nAbstract: Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018)."]], "corpus": "arxiv"}
+{"tstamp": 1722441702.2496, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722441701.8684, "finish": 1722441702.2496, "ip": "", "conv_id": "0eb1b97ea1ea4813bcb0d07561d9762b", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Paper exploring algorithmic fidelity in language models for social science research.", "output": [["Paper exploring algorithmic fidelity in language models for social science research.", "Title: Analysis and modeling of scale-invariance in plankton abundance\n\nAbstract: The power spectrum, $S$, of horizontal transects of plankton abundance are often observed to have a power-law dependence on wavenumber, $k$, with exponent close to -2: $S(k)\\propto k^{-2}$ over a wide range of scales. I present power spectral analyses of aircraft lidar measurements of phytoplankton abundance from scales of 1 to 100 km. A power spectrum $S(k)\\propto k^{-2}$ is obtained. As a model for this observation, I consider a stochastic growth equation where the rate of change of plankton abundance is determined by turbulent mixing, modeled as a diffusion process in two dimensions, and exponential growth with a stochastically variable net growth rate representing a fluctuating environment. The model predicts a lognormal distribution of abundance and a power spectrum of horizontal transects $S(k)\\propto k^{-1.8}$, close to the observed spectrum. The model equation predicts that the power spectrum of variations in abundance in time at a point in space is $S(f)\\propto f^{-1.5}$ (where $f$ is the frequency). Time series analysis of local variations of phytoplankton and zooplankton yield a power-law power spectrum with exponents -1.3 and -1.2, respectively from time scales of one hour to one year. These values are roughly consistent with the model prediction of -1.5. The distribution of abundances is nearly lognormal as predicted. The model may be more generally applicable than for the spatial distribution of plankton. I relate the model predictions to observations of spatial patchiness in vegetation."]], "corpus": "arxiv"}
+{"tstamp": 1722441702.2496, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722441701.8684, "finish": 1722441702.2496, "ip": "", "conv_id": "519f89cd54734d5a8b84fc365f499beb", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Paper exploring algorithmic fidelity in language models for social science research.", "output": [["Paper exploring algorithmic fidelity in language models for social science research.", "Title: Neural language representations predict outcomes of scientific research\n\nAbstract: Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and allocate resources. Using a dataset of approximately 170,000 correlational findings extracted from leading social science journals, we show that a trained neural network can accurately predict the reported correlations using only the text descriptions of the correlates. Accurate predictive models such as these can guide scientists towards promising untested correlates, better quantify the information gained from new findings, and has implications for moving artificial intelligence systems from predicting structures to predicting relationships in the real world."]], "corpus": "arxiv"}