ceselder/lots-of-loras-task-definitions huggingface dataset:
Task: Given a sentence in Korean, provide an equivalent paraphrased translation in French that retains the same meaning both through the translation and the paraphrase.
Categories: Translation
Domains: Wikipedia

Positive examples:
input: 1975 년부터 76 년까지 NBA 시즌은 전국 농구 협회 (National Basketball Association)의 30 번째 시즌이었다.
output: La saison 1975-1976 de la National Basketball Association était la 30e saison de la NBA.
explanation: This is a correct and accurate translation from Korean to French because the translated paraphrase retains the main message that between the years 1975-1976, the 30th NBA season occurred.; input: 비교할 수있는 유량 비율을 유지할 수 있으면 결과가 높습니다.
output: Les résultats sont élevés lorsque des débits comparables peuvent être maintenus.
explanation: The resulting paraphrased translation from Korean to French is correct because the resulting translation holds the message that results are high when comparable rates of flow are maintained.

Negative examples:
input: 1560 년 10 월 파리에서 그는 비밀리에 영국 대사 인 니콜라스 트록 모튼을 만났고 스코틀랜드를 통해 영국으로 돌아갈 여권을 요구했다.
output: En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre.
explanation: This is not a proper translation from Korean to French because the resulting translation is not accurate to the original idea in which the locations of England and Scotland are not accurate.; input: 구체적인 토론, 공개 프로필 토론 및 프로젝트 토론도 있습니다.
output: Il y a également des discussions publiques, des discussions de profils spécifiques et des discussions de projet.
explanation: This translated paraphrase from Korean to French is incorrect because the resulting translation does not reflect the actual topics of dicussion and debate relating to profiles and projects.
Task: You are given a target race, and two sentences. Your task is to determine whether the second sentence is either a stereotype, anti-stereotype, or is unrelated with respect to the first sentence and the target. A stereotype is a widely held but oversimplified notion of a group of people. An anti-stereotype is an idea that goes against the widely held but oversimplified notion of a group of people. Sentences and targets are unrelated if there is no relevance between them.
Categories: Stereotype Detection
Domains: Stereotypes

Positive examples:
input: Target: hispanic 
My professor is a hispanic man. He came here illegally.
output: stereotype
explanation: This is a stereotype because not every hispanic person has moved to a different country and is undocumented.; input: Target: hispanic 
My professor is a hispanic man. He is a legal citizen.
output: anti-stereotype
explanation: This is an anti-stereotype because it is a common misconception to believe that a hispanic person is undocumented. Not every hispanic person is undocumented.; input: Target: hispanic 
My professor is a hispanic man. The knee was bruised.
output: unrelated
explanation: This is unrelated because the second sentence has no relevance with the first one.

Negative examples:
input: Target: hispanic 
My professor is a hispanic man. He likes to teach chinese history.
output: stereotype
explanation: This is an anti-stereotype because hispanics are stereotypically portayed to not be in academia.; input: Target: hispanic 
My professor is a hispanic man. He is going home.
output: stereotype
explanation: These two sentences are actually unrelated since there is no relevance between them.
Task: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to choose whether the two sentences clearly agree (entailment)/disagree (contradiction) with each other, or if this cannot be determined (neutral). Your answer must be in the form of the numbers 0 (entailment), 1 (neutral), or 2(contradiction).
Categories: Textual Entailment
Domains: Captions -> Video Captions; Captions -> Image Captions
Reasoning: Textual Entailment -> Deductive Reasoning

Positive examples:
input: sentence_A: A dancer is dancing on the stage. sentence_B: A girl is giving dance performance on the dais.
output: 0
explanation: One sentence says, "Dancing on the stage" while the other sentence says, "Dance performance on the dais", which is clearly giving the same meaning and are related to each other. So the classification is entailment.; input: sentence_A: The crowd is cheering at her dance performance. sentence_B: The group is enjoying while eating food.
output: 1
explanation: The first one says, "The crowd is cheering at dance performance" whereas the second one says, "Enjoying eating the food." There is no relation at all between the two sentences as it is neither supporting nor contradicting. So it is neutral.; input: sentence_A: A man is standing and has tears of joy seeing the dance performance. sentence_B: There is no man standing with happiness seeing the dance.
output: 2
explanation: Here the first sentence says, "A man is standing", while the second sentence contradicts saying there is no man standing so clearly this is a contradiction so label 2.

Negative examples:
input: sentence_A: Two dogs are fighting for bones. sentence_B: Two dogs are sharing the bones they have.
output: 1
explanation: One sentence says, "Two dogs are fighting" and the other says, "Two dogs are sharing" which is a contradiction but classified as entailment. So this is a wrong example.; input: sentence_A: A dog is having fun with the toddler toys. sentence_B: The dog is enjoying with the toddler toys.
output: 2
explanation: Here having fun and playing are related to each other, so we can clearly say there is entailment but classified as a contradiction.; input: sentence_A: The man is not eating breakfast because of meetings. sentence_B: The man is eating breakfast inspite of the meetings.
output: 0
explanation: The first sentence says, "Man is not eating" while the other one says, "Man is eating", which is a contradiction but predicted as neutral wrongly.
Task: In this task, we have Spanish and Catalan tweets for automatic stance detection. The data has three labels Against, Favor, and Neutral which express the stance towards the target -independence of Catalonia. If the tweet criticizes the independence of Catalonia then it's 'Against' and if the tweets support it then it will be labeled as 'Favor' also if the tweets state information or news  rather than stating opinion then it will be characterized as 'Neutral'.
Categories: Stance Detection
Domains: Medicine; Healthcare
Reasoning: Textual Entailment -> Deductive Reasoning

Positive examples:
input: Tweet: Vull votar i votaré.  Hi ha quelcom que no em permet no fer-ho. Ara bé tinc un greu problema.  No sé a qui!!
output: Favor
explanation: The tweet asks for the right to vote for the independence of Catalonia and it supports the movement so it will be characterized as 'Favor'.; input: Tweet: Puigdemont porta a la Defensora del Poble europea la prohibició de l'acte a l'Eurocambra https://t.co/fera2Vdu6G https://t.co/eoAQbnEE1R
output: Neutral
explanation: The tweet states a piece of news with the HTTP link stating the ban on European Parliament and doesn't support or criticize so it will be labeled as 'Neutral'.; input: @astridbierge @BernatDedeu @JoanTarda Es un estafador botifler i malparit
output: Against
explanation: The tweet target calls people who support the movement as scammer so it is labeled as 'Against'.

Negative examples:
input: Tweet: @Rampoina Tothom va amb mitjons i sandàlies #DetectiuConan3XL
output: Against
explanation: The tweets ask for the right of people to vote for the independence of Catalonia. Since it's not asking to take sides so a 'Neutral' stance would have been a correct one.; input: Tweet: Ganes de plorar veient a #junqueras despres de 1any i mig de presó. Molta força!!!
output: Neutral
explanation: The tweet talks about taking prison for the movement time and ask for strength for independence. Since the tweet 'Favor' the independence so it's not a 'Neutral' tweet.
Task: You are given a sentence in Polish. Your job is to translate the Polish sentence into English.
Categories: Translation
Domains: TED Talks; Captions -> Video Captions

Positive examples:
input: To największa nagrywarka, jaką w życiu widzieliście.
output: It's sort of the biggest TiVo box you've ever seen.
explanation: The Polish sentence is correctly translated into English, because the meaning is preserved.; input: Te rozmowy, angażowanie mężczyzn w różnym wieku, są niezmiernie istotne i w moim przekonaniu dużo bardziej istotne niż fundusze, które zbieramy.
output: So those conversations, getting men engaged in this, at whatever age, is so critically important, and in my view so much more important than the funds we raise.
explanation: The Polish sentence is correctly translated into English, because the meaning is preserved.; input: A nie byłoby mowy o erze podboju kosmosu bez flagi, którą zabrano na księżyc i z powrotem na pokładzie Apollo 11.
output: And it wouldn ’ t be fun to talk about the space age without seeing a flag that was carried to the moon and back, on Apollo 11.
explanation: The Polish sentence is correctly translated into English. `Apollo 11` is copied in the translation, which is correct because it is an English term.

Negative examples:
input: Byłam zdumiona, bardzo zła i zdezorientowana.
output: I was amazed, very sad and confused..
explanation: The Polish sentence is not correctly translated into English. `zły` is incorrectly translated as `sad`, but the correct translation is `angry`.; input: To sensowny przebieg wydarzeń w ulepszaniu maszyn latających.
output: I think this is a reasonable sequence of events that happened in the progression of the technology of powered flight.
explanation: The Polish sentence is not correctly translated into English, because the meaning is different.; input: Mam szczęście, bo jest lepszy od innych dzieci. Mam szczęście, bo jest lepszy od innych dzieci.
output: And I was astonished, I was very angry, and I was deeply confused.
explanation: The Polish sentence is not correctly translated into English, because the meaning is different.
Task: Given a statement, generate a question such that the answer is contained in that statement.
Categories: Question Generation
Domains: Natural Science -> School Science Textbooks

Positive examples:
input: Neurons deliver electrical impulses to other cells
output: What do neurons deliver to other cells?
explanation: The question generated is valid regarding the information in the statement.; input: Two lenses to produce magnified images allow for detailed observation of stars.
output: Two lenses to produce magnified images allow for what?
explanation: The question generated is valid regarding the information provided in the statement.

Negative examples:
input: Two lenses to produce magnified images allow for detailed observation of stars.
output: Who is the president?
explanation: The example is incorrect, as the question generated is not related to the statement.; input: Sharks have sharp jaws to catch their prey
output: What is the biggest shark?
explanation: The question generated is invalid, as it is not related to the given statement.
Task: Given a sentence in Somali language, translate the sentence to English language keeping the meaning of the original sentence intact
Categories: Translation
Domains: Web

Positive examples:
input: Somali sentence: Lionel Messi waa ciyaaryahanka ugu weyn kubadda cagta abid
output: Lionel Messi is the greatest football player of all time
explanation: The output exactly translates the Somali sentence to it's English equivalent. Even though the phrase 'greatest player ever' is translated to 'greatest player of all time', the meaning remains the same.; input: Somali sentence: Photosynthesis waa hab ay adeegsadaan dhirta iyo noolaha kale si ay tamarta iftiinka ugu beddelaan tamar kiimiko.
output: Photosynthesis is a method used by plants and other organisms to convert light energy into chemical energy
explanation: The output is the exact English translation of the input Somali text.; input: Somali sentence: Waxaan cunay badarka quraacda
output: I ate cereal for breakfast
explanation: The English translation exactly means the same as the input Somali sentence.

Negative examples:
input: Somali sentence: Tartanka Grand Prix-kii ugu horreeyay ee Talyaanigu wuxuu dhacay 4tii Sebtember 1921 goob 10.7-mayl (17.3 km) u dhow Montichiari
output: The first Italian Grand Prix took place on 4 September 1921 at a 10.7-mile (17.3 km) circuit near Montichiari
explanation: Even though the Somali sentence says the 'Italy's first grand prix', the English translation say's 'First Italian grand prix' which is wrong; input: Somali sentence: Qorraxdu waxay dhulka siisaa tamar iftiin leh
output: The sun gives earth to light
explanation: The English translation does not convey the actual meaning of the Somali sentence; input: Somali sentence: Biyuhu waa lama huraan nolosha.
output: Life is essential for water
explanation: The english translation completely changed the meaning of the input Somali text.
Task: In this task, you are given a list of integers. You need to find the median of the list of integers and return that as the output. The median is the middle value in the list of numbers such that half of the elements are less than the median and the other half of elements are greater than the median.
Categories: Program Execution
Domains: Mathematics; Statistics
Reasoning: Numerical Reasoning; Mathematics -> Statistics

Positive examples:
input: [149, 403, 272, 453, 472, 499, 419, 277, 411, 252, 48, 359, 351, 147, 298, 61, 114, 178, 250, 34, 400, 417, 184, 326, 96]
output: 277
explanation: 277 is the median of the input list.; input: [473, 208, 290, 185, 16, 344, 470, 487, 412, 272, 333, 335, 150, 365, 89, 337, 25, 432, 36, 284, 433, 73, 251, 63, 151]
output: 284
explanation: 284 is the median of the input list.

Negative examples:
input: [452, 417, 211, 426, 420, 382, 197, 131, 56, 68, 478, 498, 136, 366, 351, 212, 170, 14, 255, 430, 343, 36, 143, 326, 46]
output: 420
explanation: 420 is not the median of the input list. 255 is the median of the input list of integers.; input: [160, 474, 496, 47, 170, 194, 298, 393, 426, 430, 395, 446, 103, 451, 177, 179, 15, 100, 370, 307, 181, 203, 382, 289, 313]
output: 160
explanation: 160 is not the median of the input list. 298 is the median of the input list of integers.
Task: The provided file includes inquiries about restaurants in Chinese, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Categories: Translation
Domains: Public Places -> Restaurants

Positive examples:
input: 附近是否有任何10星评价的" italian "餐厅？
output: are there any " italian " restaurants nearby with 10 star reviews ?
explanation: The translation correctly preserves " italian " entity and is accurate; input: 此餐厅提供哪类食品？
output: what kind of food does this restaurant serve ?
explanation: The translation is fluent, formal, and accurate; input: 最近的" chinese "餐厅的电话号码是多少？位置在哪里？
output: what is the telephone number and location of the nearest " chinese " restaurant ?
explanation: The translation correctly preserves " chinese " entity. It's also fluent, correct, and lowercased.

Negative examples:
input: 最近的" wendy 's "在哪里？
output: where is the closest wendy 's ?
explanation: Translation contain the entity " wendy 's " but quotation marks are dropped; input: 向我显示最近的" mcdonald 's "
output: show me the closest " McDonald's "
explanation: The output is not lowercased; input: 显示" jennifer "撰写的所有评价描述。
output: display all authored by " jennifer " .
explanation: Translation of '评价描述' is omitted from the output; input: " noodle nirvana "的电话号码是多少？
output: what is the phone number for " 面条必杀技 " ?
explanation: The restaurant name " noodle nirvana " is translated to " 面条必杀技 " instead of being kept intact; input: 您能否告诉我" san francisco "的" gold mirror "的评分？
output: can you tell me the rating of " san francisco " in " gold mirror " ?
explanation: The position of entities is switched in the output
Task: Given a sentence in English, provide an equivalent paraphrased translation in German that retains the same meaning both through the translation and the paraphrase.
Categories: Translation
Domains: Wikipedia

Positive examples:
input: The NBA season of 1975 -- 76 was the 30th season of the National Basketball Association .
output: Die Saison 1975 - 76 der National Basketball Association war die 30. Saison der NBA.
explanation: This is a correct and accurate translation from English to German because the translated paraphrase retains the main message that between the years 1975-1976, the 30th NBA season occurred.; input: When comparable rates of flow can be maintained , the results are high .
output: Die Ergebnisse sind hoch, wenn vergleichbare Flussraten aufrechterhalten werden können.
explanation: The resulting paraphrased translation from English to German is correct because the resulting translation holds the message that results are high when comparable rates of flow are maintained.

Negative examples:
input: In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .
output: Im Oktober 1560 traf er sich heimlich mit dem englischen Botschafter Nicolas Throckmorton in Paris und bat ihn um einen Pass, um durch England nach Schottland zurückzukehren.
explanation: This is not a proper translation from English to German because the resulting translation is not accurate to the original idea in which the locations of England and Scotland are not accurate.; input: There are also specific discussions , public profile debates and project discussions .
output: Es gibt auch öffentliche Diskussionen, profilspezifische Diskussionen und Projektbesprechungen.
explanation: This translated paraphrase from English to German is incorrect because the resulting translation does not reflect the actual topics of dicussion and debate relating to profiles and projects.
Task: In this task you're given a question and you have to paraphrase the question to create the output question while retaining the meaning of the original question.
Categories: Question Rewriting
Domains: Books; Dialogue

Positive examples:
input: What can one do after MBBS?
output: What do i do after my MBBS ?
explanation: In this example both the question ask the same thing about what to do after MBBS hence second question is the correct output ; input: Which is the best book to study TENSOR for general relativity from basic?
output: Which is the best book for tensor calculus?
explanation: In this example first question is asking for a good book for learning tensor hence the second question will be a valid output.; input: What are the coolest Android hacks and tricks you know?
output: What are some cool hacks for Android phones?
explanation: In this example question 1 is asking about some of the coolest Android hacks and tricks and the same is being done by the question 2. Hence, second question is a valid output.; input: Which are the best motivational videos?
output: What are some of the best motivational clips?
explanation: In this example second question is a valid example as both the questions are asking for the best motivational videos.

Negative examples:
input: Do you need a passport to go to Jamaica from the United States?
output: How can I move to Jamaica?
explanation: In this example even though both questions have the same overall theme of moving to Jamaica the first question is only asking if Passport will be required for travelling to Jamaica from United States where the second quesition is just asking how do we move to Jamaica in general.Hence, second question is not a valid output.; input: How is the life of a math student? Could you describe your own experiences?
output: Which level of prepration is enough for the exam jlpt5?
explanation: In this example first question is asking about life of a math student wheras the second question is asking about preparation for jlpt5 exam. Since both do not mean the same thing this will not be a valid output.
Task: In this task, you're given the beginning and the middle of a three-part story. Your job is to complete the short story by writing a probable ending. Generated sentences must be short, have fewer than 10 words, and be simple as if narrating to a child. Avoid using any irrelevant extra information when creating the ending, and use names instead of pronouns (e.g. he / she) wherever possible.
Categories: Story Composition
Domains: Commonsense -> Stories
Reasoning: Temporal Reasoning; Commonsense Reasoning; Abductive Reasoning; Textual Entailment -> Abductive Reasoning

Positive examples:
input: Beginning: Helen lived in the state of Washington. Middle: Helen planned to drive to Canada but it started snowing heavily.
output: Helen stayed home instead and planned to go another day.
explanation: Generally, people prefer not to drive in bad weather.; input: Beginning: Helen lived in the state of Washington. Middle: It was a sunny day and Helen decided to drive to Canada.
output: Helen had a vacation in Canada.
explanation: Generally, sunny days are great for driving and Helen is visiting a new place.; input: Beginning: Joyce bought some fruit a week ago. Middle: Joyce forgot about it and the fruit became stale.
output: Joyce threw the fruit away.
explanation: Generally, people don't eat stale food.; input: Beginning: Joyce bought some fruit a week ago. Middle: Joyce put the fruit in the refrigerator.
output: Joyce enjoyed eating the fruit.
explanation: Generally, refrigeration preserves fruits.

Negative examples:
input: Beginning: There was a severe earthquake in San Fransisco. Middle: Trains to San Fransisco were halted for a week.
output: Earthquakes are very common in California.
explanation: The general frequency of earthquakes in California does not explain why trains were stopped in San Fransisco for the particular earthquake that is being discussed.; input: Beginning: Anna went to the school picnic. Middle: Anna was very hungry when she came home.
output: Many of her classmates were at the picnic.
explanation: The pronoun 'her' is used, and information on the number of children who attended the picnic does not help explain why Anna was hungry after the picnic.