DataHammer/scimrc huggingface dataset:
Question: Can you write a Haiku about using AI?
Answer: Computers are smart, I wonder who's in control, Beware of Skynet
Question: What is the structure of Public Forum debate, as used in US debate competitions?
Answer: A public forum debate takes place between two teams of two individuals each. For the assigned topic, one team argues for the affirmative, or pro side, and the other argues for the negative, or con side.

The debate is organized in a sequence of speeches according to the following outline:
•	Constructives (4 minutes): The first speaker of each team gives a pre-prepared case that makes arguments for their side.
•	1st Crossfire (3 minutes): The crossfire, or cross-examination, is a time for members of each team to take turns asking each other questions about their arguments. The first crossfire takes place between the first speakers of each team.
•	Rebuttals (4 minutes): The second speaker of each team presents a rebuttal, in which they respond to the points made in their opponents' constructive speech. The second speaking team's rebuttal will sometimes defend their own case along with attacking their opponents'.
•	2nd Crossfire (3 minutes): This crossfire is similar to the first crossfire, except it is between the second speakers of each team.
•	Summaries (3 minutes): The first speaker of each team gives a speech that aims to break down the main issues of the round.
•	Grand Crossfire (3 minutes): A crossfire where both members of both teams ask each other questions.
•	Final Focuses (2 minutes): The second speaker of each team gives a speech that aims to emphasize the reasons the judge should vote for them.

Additionally, each team is given 3 minutes of preparation time throughout the round, which they can choose to take before any of their speeches. It is important to note that the time duration of each stage may vary across different leagues.

After all speeches are given, the judges, or judge panel, will take some time to deliberate before casting their ballot for one of the two teams. There are no ties.
Question: Which country has the most inhabitants?
Answer: #	Country (or dependency)	Population (2020)
1	China	1,439,323,776
2	India	1,380,004,385
3	United States	331,002,651
4	Indonesia	273,523,615
Question: Identify which car manufacturer is Japanese or American: Dodge, Subaru
Answer: Dodge is American, Subaru is Japanese
Question: Are there more wheels or doors in New York City?
Answer: This has been a long-term debate. I think there are more wheels than doors in New York City. Here is why. Many people who argue there are more doors bring up office doors, car doors (cars also have 4 wheels), cabinets, refrigerators, etc. However, I think what sets the number of wheels apart from doors is office chairs. Each office chair has four wheels and zero doors. Now multiply the number of workers in New York by four and that will get you the number of additional wheels - which I think offsets the number of doors that exist in the city.
Question: Give me a bulleted list of reasons why Lionel Messi is the best soccer player of all time.
Answer: The following are reasons why Lionel Messi is the best soccer player:
1. He has won 7 Ballon d'Or awards
2. He has won 4 Champions League titles
3. He won the World Cup
4. He won the Copa America
5. He has over 800 goals and over 300 assists in his career
Question: How big is the dataset used?
Evidence: We utilize the VATEX dataset for video captioning, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. It covers 600 human activities and a variety of video content. Each video is paired with 10 English and 10 Chinese diverse captions. We follow the official split with 25,991 videos for training, 3,000 videos for validation and 6,000 public test videos for final testing.
We utilize the VATEX dataset for video captioning, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. It covers 600 human activities and a variety of video content. Each video is paired with 10 English and 10 Chinese diverse captions. We follow the official split with 25,991 videos for training, 3,000 videos for validation and 6,000 public test videos for final testing.
Answer: The answers are shown as follows:
* over 41,250 videos and 825,000 captions in both English and Chinese.
* over 206,000 English-Chinese parallel translation pairs
Question: Give me a list of the Avengers
Answer: These are some of the Avengers that have appeared in the Marvel Comics: 
- Iron Man
- Captain America
- Hulk
- Thor
- Spiderman
- Black Panther
- Black Widow
- Hawkeye
- Ant Man
- Doctor Strange
Question: What is the difference compared to previous methods?
Evidence: We perform qualitative analysis to understand how the latent embeddings help induce syntactic structures. First we filter out low-frequency words and punctuations in WSJ, and visualize the rest words (10k) with t-SNE BIBREF42 under different embeddings. We assign each word with its most likely gold POS tags in WSJ and color them according to the gold POS tags.
For our Markov-structured model, we have displayed the embedding space in Figure SECREF5 , where the gold POS clusters are well-formed. Further, we present five example target words and their five nearest neighbors in terms of cosine similarity. As shown in Table TABREF53 , the skip-gram embedding captures both semantic and syntactic aspects to some degree, yet our embeddings are able to focus especially on the syntactic aspects of words, in an unsupervised fashion without using any extra morphological information.
In Figure FIGREF54 we depict the learned latent embeddings with the DMV-structured syntax model. Unlike the Markov structure, the DMV structure maps a large subset of singular and plural nouns to the same overlapping region. However, two clusters of singular and plural nouns are actually separated. We inspect the two clusters and the overlapping region in Figure FIGREF54 , it turns out that the nouns in the separated clusters are words that can appear as subjects and, therefore, for which verb agreement is important to model. In contrast, the nouns in the overlapping region are typically objects. This demonstrates that the latent embeddings are focusing on aspects of language that are specifically important for modeling dependency without ever having seen examples of dependency parses. Some previous work has deliberately created embeddings to capture different notions of similarity BIBREF43 , BIBREF44 , while they use extra morphology or dependency annotations to guide the embedding learning, our approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora.
Some previous work has deliberately created embeddings to capture different notions of similarity (Levy and Goldberg, 2014; Cotterell and Schutze ¨ , 2015), while they use extra morphology or dependency annotations to guide the embedding learning, our approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora.
Answer: Their approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora.
Question: Name three Italian Football Teams in Serie A.
Answer: Three Italian Football Teams in Serie A are AC Milan, Inter Milan, and Juventus.
Question: Give me a list of the best exploration-focused video games.
Answer: Here are some games where the primary goal is exploration:

- Subnautica
- Outer Wilds
- Journey
- No Man's Sky
- Stray
- Astroneer
- Grow: Song of the Evertree
- Firewatch
- Call of the Sea
- ABZU
- What Remains of Edith Finch
Question: How is the back-translation model trained?
Evidence: Following the back-translation paradigm BIBREF3 , BIBREF4 , we have a semantic parser, which maps a natural language question INLINEFORM0 to a logical form INLINEFORM1 , and a question generator, which maps INLINEFORM2 to INLINEFORM3 . The semantic parser works for the primary task, and the question generator mainly works for generating pseudo datapoints. We start the training process by applying the rule INLINEFORM4 to a set of natural language questions INLINEFORM5 . The resulting dataset is considered as the training data to initialize both the semantic parser and the question generator. Afterwards, both models are improved following the back-translation protocol that target sequences should follow the real data distribution, yet source sequences can be generated with noises. This is based on the consideration that in an encoder-decoder model, the decoder is more sensitive to the data distribution than the encoder. We use datapoints from both models to train the semantic parser because a logical form is structural which follows a grammar, whose distribution is similar to the ground truth.
We start the training process by applying the rule INLINEFORM4 to a set of natural language questions INLINEFORM5 . The resulting dataset is considered as the training data to initialize both the semantic parser and the question generator. Afterwards, both models are improved following the back-translation protocol that target sequences should follow the real data distribution, yet source sequences can be generated with noises.
Answer: The answers are shown as follows:
*  applying the rule INLINEFORM4 to a set of natural language questions INLINEFORM5
* both models are improved following the back-translation protocol that target sequences should follow the real data distribution
Question: Categorize the following list into color names and non-color words: red, yellow, leafy, bankers, fuchsia, magenta, cerulean, purple, lavender, blue, orange, felt
Answer: Color names: red, fuchsia, magenta, cerulean, purple, lavender, blue, orange
Non-color names: leafy, bankers, felt
Question: Kingsway a 3 km stretch was built as a ceremonial boulevard by whom?
Answer: Edwin Lutyens & Herbert Baker
Question: How authors create adversarial test set to measure model robustness?
Evidence: To evaluate model robustness, we devise a test set consisting of ‘adversarial’ examples, i.e, perturbed examples that can potentially change the base model's prediction. These could stem from paraphrasing a sentence, e.g., lexical and syntactical changes. We use two approaches described in literature: back-translation and noisy sequence autoencoder. Note that these examples resemble black-box attacks but are not intentionally designed to fool the system and hence, we use the term 'adversarial' broadly. We use these techniques to produce many paraphrases and find a subset of utterances that though very similar to the original test set, result in wrong predictions. We will measure the model robustness against such changes.
To evaluate model robustness, we devise a test set consisting of ‘adversarial’ examples, i.e, perturbed examples that can potentially change the base model's prediction. These could stem from paraphrasing a sentence, e.g., lexical and syntactical changes. We use two approaches described in literature: back-translation and noisy sequence autoencoder. Note that these examples resemble black-box attacks but are not intentionally designed to fool the system and hence, we use the term 'adversarial' broadly. We use these techniques to produce many paraphrases and find a subset of utterances that though very similar to the original test set, result in wrong predictions. We will measure the model robustness against such changes.
Answer: The answers are shown as follows:
* we devise a test set consisting of ‘adversarial’ examples, i.e, perturbed examples that can potentially change the base model's prediction. 
* We use two approaches described in literature: back-translation and noisy sequence autoencoder.
Question: When Aegon the Conqueror took over Westeros, which great house of the Reach became extinct as a result?
Answer: House Gardener became extinct during Aegon's Conquest. House Tyrell was then granted rule of The Reach.
Question: What is the source of the triples?
Evidence: We downloaded 76 videos from a tutorial website about an image editing program . Each video is pre-processed to provide the transcripts and the time-stamp information for each sentence in the transcript. We then used Amazon Mechanical Turk to collect the question-answer pairs . One naive way of collecting the data is to prepare a question list and then, for each question, ask the workers to find the relevant parts in the video. However, this approach is not feasible and error-prone because the videos are typically long and finding a relevant part from a long video is difficult. Doing so might also cause us to miss questions which were relevant to the video segment. Instead, we took a reversed approach. First, for each video, we manually identified the sentence spans that can serve as answers. These candidates are of various granularity and may overlap. The segments are also complete in that they encompass the beginning and end of a task. In total, we identified 408 segments from the 76 videos. Second we asked AMT workers to provide question annotations for the videos.
We downloaded 76 videos from a tutorial website about an image editing program . 
Answer: The answers are shown as follows:
* a tutorial website about an image editing program
Question: Provide me a list of the different types of harnesses used in rock climbing
Evidence: Different types of climbing warrant particular features for harnesses. Sport climbers typically use minimalistic harnesses, some with sewn-on gear loops. Alpine climbers often choose lightweight harnesses, perhaps with detachable leg loops. Big Wall climbers generally prefer padded waist belts and leg loops. There are also full body harnesses for children, whose pelvises may be too narrow to support a standard harness safely. These harnesses prevent children from falling even when inverted, and are either manufactured for children or constructed out of webbing. Some climbers use full body harnesses when there is a chance of inverting, or when carrying a heavy bag. There are also chest harnesses, which are used only in combination with a sit harness. Test results from UIAA show that chest harnesses do not put more impact on the neck than sit harnesses, giving them the same advantages as full body harness.

Apart from these harnesses, there are also caving and canyoning harnesses, which all serve different purposes. For example, a caving harness is made of tough waterproof and unpadded material, with dual attachment points. Releasing the maillon from these attachment points loosens the harness quickly.

Canyoning harnesses are somewhat like climbing harnesses, often without the padding, but with a seat protector, making it more comfortable to rappel. These usually have a single attachment point of Dyneema.
Answer: Minimalistic Harness: has gear loops that are sewn on
Lightweight Harness: has leg loops that are removable
Big Wall Harness: uses padded waist belts and leg loops
Full Body Harness: mostly for children with small pelvises, or for scenarios that risk inversion
Chest Harness: to be used with a sit harness
Caving Harness: made of ultra durable material and multiple attachment points
Canyon Harness: has a built-in seat protector for rappeling.
Question: Give me a bunch of excuses about why I don't have my homework to turn in
Answer: Your dog ate your homework, you forget it at home, you spilled water all over it, your printer broke, you had a test in another class and were too busy studying
Question: How was the previous dataset annotated?
Evidence: We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments. In this consolidation task, the worker validates questions, merges, splits or modifies answers for the same role according to guidelines, and removes redundant roles by picking the more naturally phrased questions. For example, in Table TABREF4 ex. 1, one worker could have chosen “47 people”, while another chose “the councillor”; in this case the consolidator would include both of those answers. In Section SECREF4, we show that this process yields better coverage. For example annotations, please refer to the appendix.
We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments. 
Answer: The answers are shown as follows:
* the annotation machinery of BIBREF5
Question: How many variants do authors experiment with?
Evidence: Based on the models described in Section we experiment with eight variants: (a) baseline transformer model (base); (b) base with AIC (base+sum); (c) base with AIF using spacial (base+att) or object based (base+obj) image features; (d) standard deliberation model (del); (e) deliberation models enriched with image information: del+sum, del+att and del+obj.
Based on the models described in Section 3 we experiment with eight variants: (a) baseline transformer model (base);  (b) base with AIC (base+sum);  (c) base with AIF using spacial (base+att) or object based (base+obj) image features;  (d) standard deliberation model (del);  (e) deliberation models enriched with image information: del+sum, del+att and del+obj.
Answer: Eight variants.
Question: Does their model also take the expected answer style as input?
Evidence: Moreover, to be able to make use of multiple answer styles within a single system, our model introduces an artificial token corresponding to the target style at the beginning of the answer sentence ( $y_1$ ), like BIBREF14 . At test time, the user can specify the first token to control the answer styles. This modification does not require any changes to the model architecture. Note that introducing the tokens on the decoder side prevents the passage ranker and answer possibility classifier from depending on the answer style.
Moreover, to be able to make use of multiple answer styles within a single system, our model introduces an artificial token corresponding to the target style at the beginning of the answer sentence ( $y_1$ ), like BIBREF14 . At test time, the user can specify the first token to control the answer styles.
Answer: Yes.
Question: Identify which instrument is string or percussion: Zatula, Jarana huasteca
Answer: Zatula is percussion, Jarana huasteca is string.
Question: Why is Mediterranean diet considered the healthiest?
Answer: Typical Mediterranean diet is well balanced, full of healthy fats, lean protein, and vast variety of whole grains, vegetables, and fruits. As a main source of animal protein it focuses on fish and poultry, instead of red meat. Mediterranean diet includes low processed and plant-based carbs, as well as heart healthy fats - especially olive oil. Typical dessert would be fruit which is full of vitamins and antioxidants. As a result people who follow Mediterranean diet tend to see a number of health benefits, including reduced risk of diabetes, obesity and heart disease. It also helps to manage cholesterol and blood pressure. Due to high amount of legumes, vegetables and fruits, this diet is high in antioxidants and offers anti-inflammatory benefits. Some studies also show its positive effects to brain health and cognitive function.
Question: What are the essential skills required for a communication?
Answer: Effective communication is the process of exchanging ideas, thoughts, opinions, knowledge, and data so that the message is received and understood with clarity and purpose. When we communicate effectively, both the sender and receiver feel satisfied. The most essential skill required is listening during a conversation. The other skills include being humble and understanding others opinions. Effective communication also requires you to help others understand in simplest ways possible about your points and views where the receiver should feel satisfied.
Question: Identify which instrument is string or percussion: Sikulu, Sharki
Answer: Sharki is string, Sikulu is percussion.
Question: How is "crisp" pronounced?
Answer: The word "crisp" is pronounced from the back of the mouth to the front.
Question: When going on a backcountry hike, which of the following are among the 10 essentials for a safe hike: a navigation device (such as a map or compass), a headlamp with extra batteries, a fire starter (such as matches or a lighter), extra food, extra water, a camera.
Answer: A navigation device (such as a map or compass), a headlamp with extra batteries, a firestarter (such as matches or a lighter, extra food, and extra water are among the 10 essentials for a safe hike. While a camera may add to your enjoyment of the hike, it is not essential.
Question: I'm thinking about joining the military to help pay for college.  What are my options, and what do you recommend?
Answer: The military can help pay for college in a few different ways.  If you are already planning to go to a specific school, you can check to see if they have a Reserve Officer Training Corps (ROTC) unit at the school and apply for an ROTC scholarship.  An ROTC scholarship can help pay for most of your college degree, and you will have the option to join an active or reserve unit after college is complete.  You will likely owe about 4 years of service as an officer in return for the scholarship.  Another option is to go to a military academy.  Military academies provide full tuition in exchange for 5 years of service as an officer after graduation.  Finally, you could enlist in the military, serve 3-4 years as an enlisted soldier, and then use the G.I. Bill to attend college afterward.  The best choice for you depends on your college preferences, your financial situation, and the timeline on which you want to earn your college degree.
Question: What country was Frida Kahlo from and what career was she famous for?
Answer: Frida Kahlo is a well known Mexican artist
Question: Given this paragraph about the Northamptonshire County Cricket Club, can you list down some points that can be inferred about the County Championship?
Evidence: Northamptonshire County Cricket Club is in Division Two of the County Championship; the team (also known as The Steelbacks) play their home games at the County Cricket Ground, Northampton. They finished as runners-up in the Championship on four occasions in the period before it split into two divisions.

In 2013 the club won the Friends Life t20, beating Surrey in the final. Appearing in their third final in four years, the Steelbacks beat Durham by four wickets at Edgbaston in 2016 to lift the Natwest t20 Blast trophy for the second time. The club also won the NatWest Trophy on two occasions, and the Benson & Hedges Cup once.
Answer: Based on this paragraph, it seems like the Country Championship refers to some form of organized competition between cricket teams belonging to different counties. The Championship has been organized for at least four times given that the Northamptonshire County Cricket Club finished as runners-up in it for four times. It also seems like the Championship was split into two divisions at some point.