ceselder/lots-of-loras-task-definitions huggingface dataset:
Task: Given an entity, a before event, an after event, and an attribute related to the entity, generate a sentence as output. Your sentence should show the changes in the attribute of the entity.
Categories: Data to Text
Domains: Web

Positive examples:
input:  entity: arm 
before: free 
after: grasped 
attr: state
output: state of arm was free before and grasped afterwards
explanation: The state of the arm was free before and afterwards it was grasped hence the output is correct.; input: entity: person 
before: standing 
after: in air 
attr: location
output: location of person was standing before and in air afterwards.
explanation: The attribute location of the entity person was in the standing state before and in air afterwards.; input: entity: mixture 
before: chunky 
after: smoother 
attr: texture
output: texture of mixture was chunky before and smoother afterwards.
explanation: The attribute is concatenated with the entity using of and with the before state with was and the after state with and. The word afterwards is added at the end to complete the sentence.

Negative examples:
input: entity: buckles 
before: being worn 
after: removed 
attr: wearing
output: wearing of buckles was being worn before and removed afterwards
explanation: Here wearing of buckles is grammatically incorrect and the output sentence should be the buckles were worn before and removed afterwards.; input: entity: tape measure 
before: in box 
after: measured around head 
attr: tape measure
output: tape measure of tape measure was in box before and measured around head afterwards
explanation: Here the attribute is the same as entity and hence can be used only once in the output sentence. So the output should be tape measure was in box before and measured around head afterwards.; input: entity: person 
before: unskilled 
after: skilled 
attr: skill
output: skill of person was unskilled before and skilled afterwards
explanation: The output sentence symantically incorrect. The output should be the person was unskilled before and skilled afterwards.
Task: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
Categories: Question Answering
Domains: Scientific Research Papers

Positive examples:
input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
output: No
explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.; input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
output: DISPLAYFORM0
explanation: Based on the context, the loss function is DISPLAYFORM0.; input: In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. In this work, we investigate the application of BERT-based models for the task of subjective language detection. Experiments ::: Dataset and Experimental Settings
We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019 
 Question: Which experiments are perfomed?
output: They used BERT-based models to detect subjective language in the WNC corpus
explanation: Based on the context, the answer is correct.

Negative examples:
input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
output: No
explanation: Based on the context, the answer is incorrect.; input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
output: GPT-3
explanation: Based on the context, the answer should be RoBERTa.
Task: Two analogies on manipulating items in a kitchen is given in the form "A : B. C : ?". The phrase "A : B" relates item A to its appropriate manipulation B. Your task is to replace the question mark (?) with the appropriate manipulation of item C, following the "A : B" relation. Your answer should be a verb which shows a way to manipulate the given object.
Categories: Word Analogy
Domains: Commonsense
Reasoning: Relational Reasoning; Commonsense Reasoning; Analogical Reasoning

Positive examples:
input: jug : pour. shoe : ?
output: untie
explanation: The given analogy relates items to how they are manipulated. Jugs can be poured into. Shoes can be untied.; input: jar : open. box : ?
output: open
explanation: The given analogy relates items to how they are manipulated. Jars can be opened. Boxes can be opened.; input: bottle : pour. safe : ?
output: unlock
explanation: Bottles can be poured into, and safes can be unlocked. So, unlock is a correct output here.

Negative examples:
input: backpack : unzip. safe : ?
output: travel
explanation: The given analogy relates items to how they are manipulated. Backpacks can be unzipped. Safes CANNOT be travelled, and so it not the correct answer.; input: faucet : turn. bag : ?
output: cupboard
explanation: The given analogy relates items to how they are manipulated. Faucets can be turned. Cupboard is not the right answer for bag, as it does not model the same relation, and it instead models containment.; input: peas : shell. present : ?
output: unlock
explanation: The given analogy relates items to how they are manipulated. Peas can be shelled. Unlock is not the right answer for present, as presents cannot be unlocked.
Task: You are given first 5 messages from a series of message exchanges between 2 persons playing the game of Diplomacy which is an American strategic board game. You need to generate the next message. The message should be generated such that it fits the context seen so far. Avoid the text that is (i) tangent to the context, (ii) repeats the context.
Categories: Dialogue Generation
Domains: Game; Dialogue
Reasoning: Reasoning on Social Interactions; Commonsense Reasoning -> Social Situations

Positive examples:
input: Heyyyy Turkey; Whatcha thinking re: start of the game?; It kind of depends. I'll probably want to stop Russia from advancing south; I'm kind of afraid of Austria and Russia teaming together on me; I mean if that happens you’re donezos
output: What vibes are you getting from each of them?
explanation: The message fits the context as it asks for the vibes of 2 of them which refers to 2 countries: Austria and Russia in the second last message. Thus it is a positive example.; input: Gahhh I’m Italy fuck my life; Non aggression pact for these first two turns?  Seems to me we both have other things to work about.  Lmk what you’re thinking.; Sounds good with me; Cool.  So no moves towards each other?; Yeah that's right. I'm not moving into Piedmont
output: Rgr- neither will I
explanation: The sentence adds to the context at it supports the last message in the input by showing aggreement. Thus it is a positive example.

Negative examples:
input: Hey italy! good luck this game. I'm guessing you and Austria will be pals, you and France will be rivals?; Well good luck to you too! No idea yet who is a friend. Have you heard anything interesting?; I have some agreements with France. If those are upheld, I'd bet France goes for Por and Spain in 1901. You might want to ask about whether a fleet gets built in Mar during the 1901 build, if you haven't already, as this would be an indicator of aggression towards you.; Germany seems like a bit of a wild card. Haven't talked in depth with Austria.; Do you have any sort of aggression in your opening, or are you just planning to be neutral and secure Tun?
output: Have you heard anything interesting?
explanation: The output does not answer the question in the last message of the input and thus does not fit into the context. Moreover it repeats the context by asking same question as second message. Thus it is a negative example.; input: Gah I’m Italy fuck everything; Let’s talk my dude.  I think an initial ceasefire for this first turn- or maybe longer!- may be in both of our advantages.  What’re you thinking?; I tend to agree, I would rather move east here, and I'm fine with some sort of Lepanto.; Slow day at work so I'm free if you want to talk a bit.  I've sent one message to Turkey and heard nothing yet so I don't feel super great rn.; Hey Italy, would love to talk strategy if you're free for a bit!
output: How is russia doing?
explanation: The message does not fit the context as it does not answer the last question about having time to talk about the strategy. Rather the output is a tangent to the context as it suddenly asks about russia from nowhere. Thus it is a negative example.
Task: In this task, you will be given sentences and a drug name in which your task is to indicate the doses of intake of that particular drug. A dose refers to a specified amount of medication taken at one time. Although there might be several correct answers, you need to write one of them.
Categories: Information Extraction
Domains: Biology -> Clinical Knowledge; Healthcare

Positive examples:
input: An episode of subacute encephalopathy after the infusion of methotrexate (1500 mg/m2) (MTX) is reported in a young adult with metastastic gastric cancer.	 methotrexate
output: 1500 mg/m2
explanation: Here, the drug methotrexate mentioned after '	 ' has been mentioned to be taken as 1500 mg/m2 within brackets and hence should be tagged as dosage.; input: A case of normotensive scleroderma renal crisis after high-dose methylprednisolone treatment.	 methylprednisolone
output: high
explanation: Amount of methylprednisolone is taken in high amount and hence should be tagged with that.

Negative examples:
input: Ibuprofen overdose is usually characterized by GI upset, dizziness, and mild sedation	 ibuprofen
output: mild
explanation: Here the drug ibuprofen has been mentioned to be taken in excess but mild is the qualifier of the sedation caused by overdose of the drug, hence mild should not be tagged as drug dosage.; input: She took three doses of methotrexate and two doses of ibuprofen.	 methotrexate
output: two
explanation: Amount of methotrexate is taken in three doses and hence should be tagged with that, instead of the the dosage mentioned for ibuprofen.
Task: In this task, you are given a paragraph, and your job is to generate comma-separated entities present in the given paragraph. Generate entities from a given passage in such a way that (i) they are present in the paragraph, (ii) they are non-duplicate, (iii) they underwent a state change during the process. Avoid creating entities that (i) aren't present in the process paragraph and (ii) aren't significant. 
Categories: Named Entity Recognition
Domains: Natural Science

Positive examples:
input:  Magma rises from deep in the earth. The magma goes into volcanos. The volcanos pressure the magma upwards. The pressure causes the magma to push through the surface of the volcano. The lava cools. The lava forms new rock. New magma is pressured to the surface of the volcano. The volcano bursts through the rock the formed after the last eruption. 
output:  magma ,lava ,new rock 
explanation: The entities are present in the process paragraph and they undergo state change. So, this is a positive example.; input:  Water from the ocean washes onto beaches in waves. The waves contain sediment from the ocean. The water and particles in it hit rocks and sand on the beach. The wind and current sometimes push the water and particles harder. Tiny parts of the rocks on the shore come off the larger rocks. The waves pick up sand and small rocks from the beach. The waves go back out into the ocean. The waves carry the new particles with them. Rocks and the beach look a little different. 
output:  waves ,rocks ,tiny parts of rocks 
explanation: The entities are present in the process paragraph and they undergo state change. So, this is a positive example.

Negative examples:
input:  Water from oceans, lakes, swamps, rivers, and plants turns into water vapor. Water vapor condenses into millions of tiny droplets that form clouds. Clouds lose these droplets through rain or snow, also caused precipitation. Precipitation is either absorbed into the ground or runs off into rivers. Water that was absorbed into the ground is taken up by plants. Plants lose water from their surfaces as vapor. The vapor goes back into the atmosphere. Water that runs off into rivers flows into ponds, lakes, or oceans. The water evaporates back into the atmosphere. 
output:  water ,magma ,droplets; rocks; rain ,snow 
explanation: All the entities aren't present in the process paragraph. Magma and rocks aren't part of the paragraph. So, this is a negative example.; input:  Different kinds of rocks melt into magma. Magma cools in the crust. Magma goes to the surface and becomes lava. Lava cools. Cooled magma and lava become igneous rock. 
output:  water ,clouds ,lava ,igneous rock 
explanation: All the entities aren't present in the process paragraph. Water and clouds aren't part of the paragraph. So, this is a negative example.
Task: In this task you are given a sentence with one coordinating conjunction (for, and, nor, but, or, yet, and so). You must judge whether the order of two coordinated clausal conjoints have been inverted or not. Inversion would result in the sentence sounding unnatural. Label the instances as "Original" or "Inversion" based on your judgment.
Categories: Linguistic Probing
Domains: Narrative; Commonsense -> Concepts and Relations

Positive examples:
input: He knew it , and he deserved no answer .
output: Inversion
explanation: "He knew it" and "he deserved no answer" have been inverted.; input: Clary saw his features tighten , but his face was in shadow .
output: Inversion
explanation: "Clary saw his features tighten" and "his face was in shadow" have been inverted.; input: I didn 't know how to solve any of those problems , and I was the master at problem solving .
output: Original
explanation: The sentence doesn't contain any inversion.

Negative examples:
input: My eyes adjust to the darkness and I can see signs of a scuffle behind the counter .
output: Inversion
explanation: The two clausal conjoints in this sentence have not been inverted so output should be "Original".
Task: In this task, you are given two lists A,B. Find the longest common subsequence in the lists A and B.
Categories: Program Execution
Domains: Mathematics

Positive examples:
input: [5797, 8817, '8297', 'b', 'U', 'b'], [5843, 8809, '8297', 'b', 'W', 'C']
output: 8297, b
explanation: Here, '8297, b' is the longest common subsequence in both the input lists [5797, 8817, '8297', 'b', 'U', 'b'] and [5843, 8809, '8297', 'b', 'W', 'C'].; input: [3353, 'x', '6339', 'v', 'M', 3727], ['U', 1497, '6339', 'v', 'M', 3997]
output: 6339, v, M
explanation: Here, '6339, v, M' is the longest common subsequence in both the input lists [3353, 'x', '6339', 'v', 'M', 3727] and ['U', 1497, '6339', 'v', 'M', 3997].

Negative examples:
input: ['Q', 7681, 'n', 'A', 'I', 'Q', 'N', 'c'], ['n', 4799, 'n', 'A', 'I', 'T', 'Y']
output: n, A
explanation: Here, the answer should have been 'n, A, I' as it is the longest common subsequence in both the input lists ['Q', 7681, 'n', 'A', 'I', 'Q', 'N', 'c'] and ['n', 4799, 'n', 'A', 'I', 'T', 'Y'].; input: ['b', 'H', 7201, 5121, '4997', '107', '9019', 'h', 'u', 'H', 'B'], [1759, 'C', '4997', '107', '9019', 'h', 'G', 3993, 'L', 'n']
output: 4997, 107, 9019
explanation: Here, the answer should have been '4997, 107, 9019, h' as it is the longest common subsequence in both the input lists ['b', 'H', 7201, 5121, '4997', '107', '9019', 'h', 'u', 'H', 'B'] and [1759, 'C', '4997', '107', '9019', 'h', 'G', 3993, 'L', 'n'].
Task: In this task, you will be presented with a question that has multiple possible answers. You should choose the most suitable option out of "A", "B", "C", "D", and "E", based on your commonsense knowledge.
Categories: Question Answering
Domains: Commonsense
Reasoning: Commonsense Reasoning

Positive examples:
input: Question: The dental office handled a lot of patients who experienced traumatic mouth injury, where were these patients coming from? 
 Options: (A) town (B) michigan (C) hospital (D) schools (E) office building 
output: C
explanation: This is the correct answer because the traumatic injuries are generally handled by the hospitals.; input: Question: Why would a person stay at their job without being paid? 
 Options: (A) thank god (B) wait in line (C) volunteer (D) offer help (E) enjoy working 
output: E
explanation: This is the correct answer. If someone loves to work too much then they are likely to keep working even without getting paid.; input: Question: The electric fan had gotten too loud and was bothering others, though they allowed many personal things on desks he was required to turn it off where? 
 Options: (A) closet (B) office (C) hot room (D) home (E) hallway 
output: B
explanation: The predicted answer is correct as the phrase: "they allowed personal things on the desk", suggests that the fan is in the office.; input: Question: Major Tom wanted to preserve the weapons. As dangerous as they were, he felt that they could have what in the future? 
 Options: (A) ruin (B) street (C) use (D) rot (E) waste 
output: C
explanation: One preserves something mainly if he/she thinks that it might be useful in the future. Hence, the predicted answer is correct.; input: Question: It was a popular and common gift this year, so he headed to the what where it would be easy to find? 
 Options: (A) department store (B) christmas stocking (C) specialty store (D) christmas tree (E) closet 
output: A
explanation: The predicted answer is correct because out of given many options only department stores are easy to find.

Negative examples:
input: Question: The ticket booth informed riders that the train was running late, so where was the ticket booth located? 
 Options: (A) movie theatre (B) sports arena (C) school (D) metro station (E) auditorium 
output: A
explanation: This is incorrect answer. Because the train is running late. Hence, the ticket booth is located in metro station. Therefore, the correct answer is (D).; input: Question: John didn't know what to get for anyone. Buying Christmas presents was stressful for him, and finishing was what? 
 Options: (A) debt (B) happiness (C) very sad (D) lack of money (E) relief 
output: D
explanation: John was stressed on buying the Christmas presents. Hence, the fishing was relief to him. Hence. the predicted answer is incorrect. And correct answer is (E).
Task: You are given a concatenated string consisting of two parts that are separated by a newline character. The first part is the user's initial question during a human computer conversation, the second part is a clarifying question asked by the computer in response to the question asked by the user. You should read the input, then generate a valid prediction of the user's response to the computer's clarifying question. A valid prediction will clarify the user's correct intent in the initial question.
Categories: Dialogue Generation
Domains: Dialogue
Reasoning: Commonsense Reasoning -> Social Situations; Reasoning on Social Interactions; Abductive Reasoning

Positive examples:
input: User: Find information about joints in the human body. 
Computer: what specific joint are you interested in
output: i am interested in joint pain treatments in general
explanation: The user want to know joints in general, not a specific joint.; input: User: Looking for locations and dates of rock and gem shows. 
Computer: are you looking for a list of clubs that host rock and gem shows
output: yes
explanation: The response is exactly what the user needed, it matches the user's intent.; input: User: Find music, tour dates, and information about the musician Neil Young. 
Computer: are you looking for similar artists to neil young
output: no i am looking for lyrics of sheet music of neil youngs songs
explanation: The user does not want to find similar artists, the user only wants to know about the artist himself.

Negative examples:
input: User: Find historical information about Madam C. J. Walker. 
Computer: would you like to know about madam cj walkers products
output: Yes, I want to know about the products.
explanation: This is a negative example because the user's true intent is to find historical information about the person, not buying products.; input: User: Find information about the television show. 
Computer: do you want to know who the main actor is in er tv show
output: show me the homepage of the tennessee titans
explanation: The is a negative example because the output is completely unrelated to the original questions and intent.; input: User: Find beginners instructions to sewing, both by hand and by machine. 
Computer: would you like to know how to sew a dress
output: Yes, I want to sew the best dress in the world.
explanation: This is a negative example because the user wants to find beginner tutorials, not an advanced one.
Task: In this task, you will be given sentences in which your task is to recognize the name of the location or place. Although there might be several correct answers, you need to write one of them.
Categories: Named Entity Recognition
Domains: Geography; Social Media -> Twitter

Positive examples:
input: I have been staying in New York for quite some time.
output: New York
explanation: New York, being a popular city in US, is considered as a location and hence should be tagged.; input: The beaches at Florida are amazingly beautiful !
output: Florida
explanation: Florida, being a popular state in US, is considered as a location and hence should be tagged.

Negative examples:
input: Shakespeare has been writing a lot of his novels centering around central Europe.
output: Europe
explanation: Here, Europe is an incomplete mention of the location . It is because the main qualifier, which actually helps to figure out the location is missing. The correct answer is central Europe.; input: Let us meet at the city centre 2.
output: city centre
explanation: Here, city centre is an incomplete mention of the location. It is because the qualifier or the numeric entity following it, which actually helps to figure out the location is missing. The correct answer is city centre 2.