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{"tstamp": 1722305246.8396, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6cf9d4db20f8436c8c7f2ccaca29d2fa", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "what is the difference between cluster point and limit point?", "0_output": [["what is the difference between cluster point and limit point?", "Askubuntu Stackexchange\n\nQ: How to enable user sharing per instructions? I have this entry in .xsession-errors log\n\"Nautilus-Share-Message: Called \"net usershare info\" but it failed: |
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{"tstamp": 1722306411.5045, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5993407c26d04c54981a49217a38518d", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "when did scotland last qualify for world cup", "0_output": [["when did scotland last qualify for world cup", "Scotland national football team\n\nScotland lost just two of their first 43 international matches. It was not until a 2–0 home defeat by Ireland in 1903 that Scotland lost a match to a team other than England. This run of success meant that Scotland would have regularly topped the Elo ratings, which were calculated in 1998, between 1876 and 1904. Scotland won the British Home Championship outright on 24 occasions, and shared the title 17 times with at least one other team. A noteworthy victory for Scotland before the Second World War was the 5–1 victory over England in 1928, which led to that Scotland side being known as the \"Wembley Wizards\". Scotland played their first match outside the British Isles in 1929, beating Norway 7–3 in Bergen. Scotland continued to contest regular friendly matches against European opposition and enjoyed wins against Germany and France before losing to the Austrian \"Wunderteam\" and Italy in 1931.\nScotland, like the other Home Nations, did not enter the three FIFA World Cups held during the 1930s. This was because the four associations had been excluded from FIFA due to a disagreement regarding the status of amateur players. The four associations, including Scotland, returned to the FIFA fold after the Second World War. A match between a United Kingdom team and a \"Rest of the World\" team was played at Hampden Park in 1947 to celebrate this reconciliation.\n1950s: Entering the World Cup"]], "0_corpus": "wikipedia", "1_conv_id": "940948f56e9c4f5297cb17f69935bd8b", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "when did scotland last qualify for world cup", "1_output": [["when did scotland last qualify for world cup", "Scotland at the FIFA World Cup\n\nThe World Cup consists of two parts, the qualification phase and the final phase (officially called the World Cup Finals). The qualification phase, which currently take place over the three years preceding the Finals, is used to determine which teams qualify for the Finals. The current format of the Finals involves 32 teams competing at venues within the host nation (or nations) over a period of about a month. The World Cup Finals is the most widely viewed sporting event in the world, with an estimated 715.1 million people watching the 2006 tournament final.\nHistory\n1930s\nScotland did not compete in the first three World Cup competitions, in 1930, 1934 and 1938. Because of a dispute with FIFA over \"broken-time\" payments to players, the SFA, with the football associations of England, Ireland and Wales, withdrew from FIFA in 1928, and did not rejoin as a permanent member until 1946.\n1950s\nThe readmission of the Scottish Football Association to FIFA in 1946 meant that Scotland were now eligible to enter the 1950 FIFA World Cup. FIFA advised that places would be awarded to the top two teams in the 1949–50 British Home Championship, but the SFA announced that Scotland would attend the finals only if Scotland won the competition. Scotland won their first two matches, but a 1–0 home defeat by England meant that the Scots finished as runners-up. The SFA stood by this proclamation, despite pleas to the contrary by the Scotland players, supported by England captain Billy Wright and the other England players. Sportswriter Brian Glanville later described the SFA as showing \"baffling insularity and pique\" in their refusal to send a team to the World Cup."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722307573.5271, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9deb3465ffd3458081570489c3810813", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "are opened or are open?", "0_output": [["are opened or are open?", "Ell Stackexchange\n\nQ: Are they the same in meaning, the open door and the opened door? What is the difference between \"the open door\" and \"the opened door\"? Are they the same in meaning? Please explain it. Thanks a lot.\n\nA: Open is an adjective. \nOpened is a past participle used as a modifier. \nThe difference in meaning is that open is in contrast to not open. The door could have been open for ten years or a century. It could always be open. \nOpened expands to The \"having been opened\" door...\nThis refers to the action of the door having been opened. The door used to be shut, now it is open. It has been opened. It has not always been open. It could have been opened ten years ago or two seconds ago, but at some point it was closed. \n"]], "0_corpus": "stackexchange", "1_conv_id": "9913e8b8325c4550a0bfb3f5298189d2", "1_model_name": "BM25", "1_prompt": "are opened or are open?", "1_output": [["are opened or are open?", "Ell Stackexchange\n\nQ: Are they the same in meaning, the open door and the opened door? What is the difference between \"the open door\" and \"the opened door\"? Are they the same in meaning? Please explain it. Thanks a lot.\n\nA: Open is an adjective. \nOpened is a past participle used as a modifier. \nThe difference in meaning is that open is in contrast to not open. The door could have been open for ten years or a century. It could always be open. \nOpened expands to The \"having been opened\" door...\nThis refers to the action of the door having been opened. The door used to be shut, now it is open. It has been opened. It has not always been open. It could have been opened ten years ago or two seconds ago, but at some point it was closed. \n"]], "1_corpus": "stackexchange"} |
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{"tstamp": 1722309466.1126, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "bc8a08f1718846e58aa907a16ce9620b", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "where did german immigrants settled in the 1800s", "0_output": [["where did german immigrants settled in the 1800s", "German Americans\n\nA particularly attractive destination was Milwaukee, which came to be known as \"the German Athens\". Radical Germans trained in politics in the old country dominated the city's Socialists. Skilled workers dominated many crafts, while entrepreneurs created the brewing industry; the most famous brands included Pabst, Schlitz, Miller, and Blatz.\nWhereas half of German immigrants settled in cities, the other half established farms in the Midwest. From Ohio to the Plains states, a heavy presence persists in rural areas into the 21st century.\nDeep South\nFew German immigrants settled in the Deep South, apart from New Orleans, the German Coast, and Texas.\nTexas\nTexas attracted many Germans who entered through Galveston and Indianola, both those who came to farm, and later immigrants who more rapidly took industrial jobs in cities such as Houston. As in Milwaukee, Germans in Houston built the brewing industry. By the 1920s, the first generation of college-educated German Americans were moving into the chemical and oil industries.\nTexas had about 20,000 German Americans in the 1850s. They did not form a uniform bloc, but were highly diverse and drew from geographic areas and all sectors of European society, except that very few aristocrats or upper middle class businessmen arrived. In this regard, Texas Germania was a microcosm of the Germania nationwide."]], "0_corpus": "wikipedia", "1_conv_id": "b7cfab5df92f40cb97e749a1057688d3", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "where did german immigrants settled in the 1800s", "1_output": [["where did german immigrants settled in the 1800s", "Immigration to Germany\n\nImmigration to Germany, both in the country's modern borders and the many political entities that preceded it, has occurred throughout the country's history. Today, Germany is one of the most popular destinations for immigrants in the world, with well over 1 million people moving there each year since 2013. As of 2019, around 13.7 million people living in Germany, or about 17% of the population, are first-generation immigrants."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722310078.9673, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "723e06bd28704a6b90a00a505ecb7438", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "0_output": [["A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "Title: ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts\n\nAbstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."]], "0_corpus": "arxiv", "1_conv_id": "6584a2b86688459ca86f0cbfc241df00", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "1_output": [["A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "Title: ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts\n\nAbstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."]], "1_corpus": "arxiv"} |
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{"tstamp": 1722310105.1175, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "daca567e0fff4693a219f1ccdcb2330c", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who votes for nba defensive player of year", "0_output": [["who votes for nba defensive player of year", "NBA Defensive Player of the Year Award\n\nThe NBA Defensive Player of the Year Award is an annual National Basketball Association (NBA) award given since the 1982–83 NBA season to the best defensive player of the regular season. The winner is selected by a panel of 124 sportswriters and broadcasters throughout the United States and Canada, each of whom casts a vote for first, second and third place selections. Each first-place vote is worth five points, second-place votes are worth three points, and a third-place vote is worth one. The player with the highest point total, regardless of the number of first-place votes, wins the award. Since the 2022–23 NBA season, winners receive the Hakeem Olajuwon Trophy, named after the two-time defensive player of the year winner.\nDikembe Mutombo, Ben Wallace, and Rudy Gobert have each won the award a record four times. Dwight Howard has won the award three times, and is the only player ever to have won it in three consecutive seasons. Sidney Moncrief, Mark Eaton, Dennis Rodman, Hakeem Olajuwon, Alonzo Mourning and Kawhi Leonard have each won it twice. The most recent award recipient is Rudy Gobert of the Minnesota Timberwolves."]], "0_corpus": "wikipedia", "1_conv_id": "2eb4d1f035c0457d8b0f19fc28c2af8b", "1_model_name": "text-embedding-3-large", "1_prompt": "who votes for nba defensive player of year", "1_output": [["who votes for nba defensive player of year", "NBA Defensive Player of the Year Award\n\nThe NBA Defensive Player of the Year Award is an annual National Basketball Association (NBA) award given since the 1982–83 NBA season to the best defensive player of the regular season. The winner is selected by a panel of 124 sportswriters and broadcasters throughout the United States and Canada, each of whom casts a vote for first, second and third place selections. Each first-place vote is worth five points, second-place votes are worth three points, and a third-place vote is worth one. The player with the highest point total, regardless of the number of first-place votes, wins the award. Since the 2022–23 NBA season, winners receive the Hakeem Olajuwon Trophy, named after the two-time defensive player of the year winner.\nDikembe Mutombo, Ben Wallace, and Rudy Gobert have each won the award a record four times. Dwight Howard has won the award three times, and is the only player ever to have won it in three consecutive seasons. Sidney Moncrief, Mark Eaton, Dennis Rodman, Hakeem Olajuwon, Alonzo Mourning and Kawhi Leonard have each won it twice. The most recent award recipient is Rudy Gobert of the Minnesota Timberwolves."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722310164.7577, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "fc308a6564e84da2b576fda8f5b0f500", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "0_output": [["Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "Title: Spatio-temporal dynamics and plastic flow of vortices in superconductors with periodic arrays of pinning sites\n\nAbstract: We present simulations of flux-gradient-driven superconducting rigid vortices interacting with square and triangular arrays of columnar pinning sites in an increasing external magnetic field. These simulations allow us to quantitatively relate spatio-temporal microscopic information of the vortex lattice with typically measured macroscopic quantities, such as the magnetization $M(H)$. The flux lattice does not become completely commensurate with the pinning sites throughout the sample at the magnetization matching peaks, but forms a commensurate lattice in a region close to the edge of the sample. Matching fields related to unstable vortex configurations do not produce peaks in $M(H)$. We observe a variety of evolving complex flux profiles, including flat terraces or plateaus separated by winding current-carrying strings and, near the peaks in $M(H)$, plateaus only in certain regions, which move through the sample as the field increases."]], "0_corpus": "arxiv", "1_conv_id": "659b5d7e65414017a44941dc4860d179", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "1_output": [["Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "Title: Local Hypercomplex Analyticity\n\nAbstract: The notion of analyticity is studied in the context of hypercomplex numbers. A critical review of the problems arising from the conventional approach is given. We describe a local analyticity condition which yields the desired type of hypercomplex solutions. The result is the definition of a generalized complex analyticity to hypercomplex space. 02.10.Tq/Vr, 02.30.-f/Dk, 02.90.+p"]], "1_corpus": "arxiv"} |
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{"tstamp": 1722310269.5459, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "356862c28de946ae8a434438c62a06e4", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "diablo 2 is not working on windows 7?", "0_output": [["diablo 2 is not working on windows 7?", "Gaming Stackexchange\n\nQ: Playing Diablo 1 on Windows 7 I just found an old copy of Diablo (the first game) in a box and whacked it into my laptop I purchased just yesterday.\nAfter the quick install I hit play and found that the screen remained black (nothing was rendering). There was however sound.\nI downloaded the patch but still nothing.\nWith it being a new laptop, I figure I might be missing some required software like Direct X maybe? I'm not really sure.\nIf anyone has any suggestions or knows how to fix this issue, that would be really helpful.\n\nA: This may sound weird (and I have actually done this to play Diablo on my Win 7 laptop), but right click on your desktop and click on Screen Resolution. Leave that open and then run Diablo.\n"]], "0_corpus": "stackexchange", "1_conv_id": "375b8963e38a4bd191128977a52d9b97", "1_model_name": "BM25", "1_prompt": "diablo 2 is not working on windows 7?", "1_output": [["diablo 2 is not working on windows 7?", "Gaming Stackexchange\n\nQ: How do I configure Diablo 2 in Wine to -skiptobnet and -w? I am using Ubuntu Gnome and Diablo 2 works ok with Wine. How do I add shortcut options to the game? I want the -skiptobnet and -w for windowed mode.\nPath is ~/.wine/drive_c/Program Files (x86)/Diablo II/Game.exe.\n\nA: I was able to get options to work using the windows .lnk file that was created in the wine start menu directory during installation.\nYou can launch Diablo II through terminal or create a new launcher using this command replacing PATH_TO_WINE and PATH_TO_SHORTCUT:\nenv WINEPREFIX=\"PATH_TO_WINE\" wine C:\\\\windows\\\\command\\\\start.exe PATH_TO_SHORTCUT -skiptobnet -w\n\nFor example:\nenv WINEPREFIX=\"/home/nonterrorist/.wine\" wine C:\\\\windows\\\\command\\\\start.exe /Unix /home/nonterrorist/.wine/dosdevices/c:/users/Public/Start\\ Menu/Programs/Diablo\\ II/Diablo\\ II\\ -\\ Lord\\ of\\ Destruction.lnk -skiptobnet -w\n\nNote in the example above, you will need to escape any spaces in the shortcut path using \\\n"]], "1_corpus": "stackexchange"} |
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{"tstamp": 1722310291.9285, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "9776843ed00d4cab89c0894cad832500", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "what are chapters in the bible called?", "0_output": [["what are chapters in the bible called?", "Stackoverflow Stackexchange\n\nQ: How to strip executables thoroughly I'd like to strip as much as I can - on Linux: an ELF. I only want in there the stuff I need to run it.\nI tried using strip:\nstrip --strip-all elf\n\nBut it doesn't seem to do a good job: nm still displays lots of stuff, and the binary is still big.\nWhat should I do?\n\nA: I would check out this great article that goes into depth on making an ELF executable as small as possible. Maybe it has info that could help!\nhttp://www.muppetlabs.com/~breadbox/software/tiny/teensy.html\n"]], "0_corpus": "stackexchange", "1_conv_id": "87810785bece4d6dab306293a64980f2", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what are chapters in the bible called?", "1_output": [["what are chapters in the bible called?", "English Stackexchange\n\nQ: Why are the divisions of the Bible called \"verses\"? In common parlance, a verse is a writing arranged with a metrical rhythm, typically having a rhyme. Most of the books of Bible contain prose which do not follow metrical rhythm or rhyme. But their sentences are called verses. My question therefore is: Why are the divisions of the Bible called verses?\n\nA: A number of questions on Stack Exchange - English Language and Usage have commented on the words 'stanza' and 'verse' in relation to poetry and to scripture.\n'Verses' are also used in Shakespeare.\nMerriam Webster defines a 'verse' in three ways : metrical verse, a stanza or a biblical verse.\nIn any work which requires to be referenced and quoted as part of its function, stanzas or verses are numbered accordingly so that they can be cited.\n"]], "1_corpus": "stackexchange"} |
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{"tstamp": 1722310355.5437, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "256e5bb6399a4ba7b599c9ba9a402e95", "0_model_name": "embed-english-v3.0", "0_prompt": "Learning word representations to improve IR tasks like query expansion and classification", "0_output": [["Learning word representations to improve IR tasks like query expansion and classification", "Title: Relevance-based Word Embedding\n\nAbstract: Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe."]], "0_corpus": "arxiv", "1_conv_id": "2f7dc574c8b94ae7a397af80c4cf05f5", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Learning word representations to improve IR tasks like query expansion and classification", "1_output": [["Learning word representations to improve IR tasks like query expansion and classification", "Title: Relevance-based Word Embedding\n\nAbstract: Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe."]], "1_corpus": "arxiv"} |
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{"tstamp": 1722310388.2572, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8c01a7990c7d433c8f9c9a38ccfd45b3", "0_model_name": "voyage-multilingual-2", "0_prompt": "who starred in the film far from the madding crowd", "0_output": [["who starred in the film far from the madding crowd", "Far from the Madding Crowd (2015 film)\n\nPlot The film is set in 1870 in Britain. While working on her aunt's farm in Dorset, Bathsheba Everdene (Carey Mulligan) meets neighbouring farmer, Gabriel Oak (Matthias Schoenaerts). He eventually proposes, but the headstrong Bathsheba declines, saying she is too independent. One night, Gabriel's new sheepdog herds his entire flock off a steep cliff. Penniless, Gabriel leaves to search for work. In contrast, Bathsheba inherits an uncle's farm and leaves to run it.\nWhile at a fair trying to find employment, Gabriel sees recruiting officers. A girl there, Fanny Robbin, points out one soldier, Sergeant Frank Troy, her sweetheart. She suggests Gabriel seek employment at a farm in Weatherbury. Upon Gabriel's arrival, several buildings are on fire. He saves the barn from destruction. At dawn the next day, he meets the farm's new mistress, Bathsheba, who hires him as a shepherd.\nWhile in town trading her seed, Bathsheba sees her neighbour William Boldwood (Michael Sheen), a prosperous and mature bachelor. Bathsheba sends Boldwood a Valentine as a joke. He is both offended and intrigued. In the meantime, Fanny goes to the wrong church for her wedding; Troy, believing he's been jilted, is devastated. Boldwood comes to speak to Bathsheba, sees her handling sheep, and is charmed. He invites her to his home, where he proposes marriage. Bathsheba delays giving him a final answer, and Gabriel admonishes her for toying with Boldwood's affections. Stung by the criticism, she fires him. The next day, a crisis arises with the sheep that only Gabriel can manage. She successfully persuades him to return."]], "0_corpus": "wikipedia", "1_conv_id": "2dafecf528e44c34a578dd2bad99f101", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "who starred in the film far from the madding crowd", "1_output": [["who starred in the film far from the madding crowd", "Far from the Madding Crowd (2015 film)\n\nFar from the Madding Crowd is a 2015 British romantic drama film directed by Thomas Vinterberg and starring Carey Mulligan, Matthias Schoenaerts, Tom Sturridge, Michael Sheen, and Juno Temple. An adaptation by David Nicholls of the 1874 novel Far from the Madding Crowd by Thomas Hardy, it is the fourth film adaptation of the novel."]], "1_corpus": "wikipedia"} |
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