DataHammer/scimrc huggingface dataset:
Question: Where is their data from?
Evidence: We base our experiments on a large collection of Bible translations crawled from the web, coming from various sources and periods of times. Any other multilingual data collection would work as well, but with the selected corpus we have the advantage that we cover the same genre and roughly the same coverage for each language involved. It is also easy to divide the data into training and test sets by using Bible verse numbers, which allows us to control for semantic similarity between languages in a way that would have been difficult in a corpus that is not multi-parallel. Altogether we have 1,303 translations in 990 languages that we can use for our purposes. These were chosen so that the model alphabet size is below 1000 symbols, which was satisfied by choosing only translations in Latin, Cyrillic or Greek script.
Certainly, there are disadvantages as well, such as the limited size (roughly 500 million tokens in total, with most languages having only one translation of the New Testament each, with roughly 200 thousand tokens), the narrow domain and the high overlap of named entities. The latter can lead to some unexpected effects when using nonsensical language vectors, as the model will then generate a sequence of random names.
The corpus deviates in some ways from an ideal multi-parallel corpus. Most translations are of the complete New Testament, whereas around 300 also contain the Old Testament (thus several times longer), and around ten contain only portions of the New Testament. Additionally, several languages have multiple translations, which are then concatenated. These translations may vary in age and style, but historical versions of languages (with their own ISO 639-3 code) are treated as distinct languages. During training we enforce a uniform distribution between languages when selecting training examples.
We base our experiments on a large collection of Bible translations crawled from the web, coming from various sources and periods of times.
Answer: A large collection of Bible translations.
Question: give me a list of all the ways a person can hydrate
Answer: - drink water from a glass
- drink water using a straw
- drink water from a hose
- suck on an ice cube
- have a cucumber
- get an IV
- drink an electrolyte
Question: Identify which instrument is string or percussion: Ikembe, Cimbalom
Answer: Cimbalom is string, Ikembe is percussion.
Question: What did the CFO do after Paypal?
Evidence: Individuals whom the media refers to as members of the PayPal Mafia include:

Peter Thiel, PayPal founder and former chief executive officer who is sometimes referred to as the "don" of the PayPal Mafia
Max Levchin, founder and chief technology officer at PayPal
Elon Musk, co-founder of Zip2, founder of X.com which merged with Confinity to form PayPal. Musk later founded SpaceX, co-founded Tesla, Inc., co-founded OpenAI, Neuralink, founded The Boring Company, and became owner of Twitter, Inc.
David O. Sacks, former PayPal COO who later founded Geni.com and Yammer
Scott Banister, early advisor and board member at PayPal.
Roelof Botha, former PayPal CFO who later became a partner and Senior Steward of venture capital firm Sequoia Capital
Steve Chen, former PayPal engineer who co-founded YouTube.
Reid Hoffman, former executive vice president who later founded LinkedIn and was an early investor in Facebook, Aviary
Ken Howery, former PayPal CFO who became a partner at Founders Fund
Chad Hurley, former PayPal web designer who co-founded YouTube
Eric M. Jackson, who wrote the book The PayPal Wars and became chief executive officer of WND Books and co-founded CapLinked
Jawed Karim, former PayPal engineer who co-founded YouTube
Jared Kopf, former PayPal (executive assistant to Peter Thiel) who co-founded Slide, HomeRun and NextRoll
Dave McClure, former PayPal marketing director, a super angel investor for start up companies
Andrew McCormack, co-founder of Valar Ventures
Luke Nosek, PayPal co-founder and former vice president of marketing and strategy, became a partner at Founders Fund with Peter Thiel and Ken Howery
Keith Rabois, former executive at PayPal who later worked at LinkedIn, Slide, Square, Khosla Ventures, and currently with Peter Thiel at Founders Fund, and personally invested in Tokbox, Xoom, Slide, LinkedIn, Geni, Room 9 Entertainment, YouTube, and Yelp
Jack Selby, former vice president of corporate and international development at PayPal who co-founded Clarium Capital with Peter Thiel, later becoming managing director of Grandmaster Capital Management
Premal Shah, former product manager at PayPal, became the founding president of Kiva.org
Russel Simmons, former PayPal engineer who co-founded Yelp Inc.
Jeremy Stoppelman, former vice president of technology at PayPal who later co-founded Yelp
Yishan Wong, former engineering manager at PayPal, later worked at Facebook and became the CEO of Reddit
Answer: Roelof Botha, one of the former CFOs, became partner and Senior Steward of venture capital firm Sequoia Capital. Ken Howery, the other CFO, became a partner at Founders Fund.
Question: In the series A Song of Ice and Fire, who is the founder of House Foote?
Answer: Philip Foote
Question: Give me a list of things to do when travelling by plane
Answer: 1. Eat little
2. stay hydrated with plenty of water
3. Take a travel pillow for your neck
4 Take an eye mask to wear for sleep
5. Ask to upgrade if in coach
6. Be prepared to not be comfortable
Question: Give me a random bulleted list of ten landlocked countries
Answer: - Uzbekistan 
- Liechtenstein
- Bhutan
- Botswana
- Laos
- Mongolia
- Switzerland
- Uganda
- Zambia
- Vatican City
Question: Which future direction in NLG are discussed?
Evidence: The diversity of NLG applications poses challenges on the employment of unsupervised pre-training, yet it also raises more scientific questions for us to explore. In terms of the future development of this technology, we emphasize the importance of answering four questions: 1) How to introduce unsupervised pre-training into NLG tasks with cross-modal context? 2) How to design a generic pre-training algorithm to fit a wide range of NLG tasks? 3) How to reduce the computing resources required for large-scale pre-training? 4) What aspect of knowledge do the pre-trained models provide for better language generation?
In terms of the future development of this technology, we emphasize the importance of answering four questions: 1) How to introduce unsupervised pre-training into NLG tasks with cross-modal context? 2) How to design a generic pre-training algorithm to fit a wide range of NLG tasks? 3) How to reduce the computing resources required for large-scale pre-training? 4) What aspect of knowledge do the pre-trained models provide for better language generation?
Answer: The answers are shown as follows:
* 1) How to introduce unsupervised pre-training into NLG tasks with cross-modal context?
* 2) How to design a generic pre-training algorithm to fit a wide range of NLG tasks?
* 3) How to reduce the computing resources required for large-scale pre-training?
* 4) What aspect of knowledge do the pre-trained models provide for better language generation?
Question: Who is the Disney character named Grogu?
Answer: Grogu is a character in the fictional Star Wars universe otherwise known as "baby Yoda." This character first appears in The Mandalorian and then also in the Book of Boba Fett. Grogu is a young member of the same species as Yoda from the original Star Wars movies.
Question: Identify which animal species is alive or extinct: Arctodus, Cross River Gorilla
Answer: Cross River Gorilla is alive, Arctodus is extinct
Question: What does the term March Madness refer to in college basketball?
Answer: March Madness is another name for the NCAA tournament, which is a single-elimination basketball tournament that takes place each year in the month of March among the 64 (plus two additional "play-in" team) selected as the top teams by a tournament committee.
Question: which existing metrics do they compare with?
Evidence: To create a final metric, we combine the individual components from Section 2 as features into a Support Vector Machine. The final results for our F1-score from this classification technique are 0.52 and 0.31 for the single and multi-turn data respectively.
We compare our results for both the single-turn and multi-turn experiments to the accuracies on the test data based off the BLEU score. We see an increase of 6% for our method with respect to the BLEU score in the single turn data, and a no change when using the multi-turn test set.
The final results for our F1-score from this classification technique are 0.52 and 0.31 for the single and multi-turn data respectively.

We compare our results for both the single-turn and multi-turn experiments to the accuracies on the test data based off the BLEU score.
Answer: The answers are shown as follows:
* F1-score
* BLEU score
Question: Which of the following are rock bands (e.g., garage rock, alternative, indie, etc.): Pavement, Nirvana, The Beatles, The Who, Led Zeppelin, Lynyrd Skynyrd, Pearl Jam, The Doors, dirt, grass, co2, hamburgers
Answer: The following are rock bands: Pavement, Nirvana, The Beatles, The Who, Led Zeppelin, Lynrd Skynyrd, Pearl Jam, The Doors
Question: What changes needs to be done in H1b lottery process
Answer: The H1B filing procedure needs to be modified to include some reservation requirements. For merits who receive a higher percentage and are hired by tier 1 businesses, a certain amount should be set aside. Additionally, because the same people are filing a large number of H1b applications, the government should not recognize their multiple submissions. H1b applicants must, by law, be working on a project, and their customer information and pay stubs from the previous six months must be verified during the application process. Instead of conducting additional draws later, consider selecting a few more candidates during the initial draw and providing them with the waitlist number so they will at least be aware of any changes to their initial selection.
Question: Which are the U.S. cities and which are U.S. states in this list? Montana, Chicago, New York, Atlanta, Honolulu, Florida, Austin, South Dakota
Answer: U.S. cities in the list: Chicago, New York, Atlanta, Honolulu, Austin
U.S. states in the list: Montana, New York, Florida, South Dakota
Question: Change this sentence to present tense
Evidence: John Watson was an early American painter, born in Scotland.
Answer: John Watson is an early American painter, born in Scotland.
Question: Who are the top 5 non-fiction writers of all time?
Answer: 1) Margaret Atwood
2) James Baldwin
3)Mark Bowden
4) Ta-Nehisi Coates
5)  Robin Cook
Question: What is a lepton?
Answer: A lepton is an elementary particle that interacts via the weak force and has a half-integer spin. Examples of leptons include the electron, muon and tau particles and their corresponding neutrinos - the electron neutrino, the muon neutron and the tau neutrino.
Question: Give me a list of different cat species that are good for people with allergies.
Answer: There are a few different breeds of cats who produce less of the common allergens. Some of these include the Hairless Sphynx, Siberian, Balinese, Bengal, Cornish Rex, Devon Rex, Russian Blue, Oriental Shorthair, LaPerm, and the Siamese breeds.
Question: Given a reference text about Daal dhokli, tell me three commonly used daals.
Evidence: Daal dhokli (Gujarati: દાળ ઢોકળી) is an Indian dish common in Rajasthani and Gujarati cuisine, made by boiling wheat flour pieces in a pigeon pea stew. A similar preparation is called varanfal (Marathi: वरण फळ), or chakolyaa (Marathi: चकोल्या) in Marathi.

Preparation
Daal dhokli can be made with various types of lentils (or daal). Some commonly used daals include toor (pigeon peas), masoor (red lentil), and moong (mung bean). The daal is then pressure-cooked with water and tempered with spices to create a stew. The dhokli, or wheat flour pieces, are made by kneading a dough of wheat flour, salt, and water, rolling it, and cutting into pieces.

Some variations add peanuts. Other flavors in the dish can come from kokum, jaggery, and spices like cumin and asafoetida.
Answer: Three commonly used daals are toor, masoor, and moong.
Question: Tell me whether these are pasta types or pizza types: macaroni, rigatoni, margherita, pepperoni, orzo, spaghetti, stuffed crust, Hawaiian
Answer: Pasta: macaroni, rigatoni, orzo, spaghetti
Pizza: margherita, pepperoni, stuffed crust, Hawaiian
Question: Which Baseball team won the 2016 World Series?
Answer: The Chicago Cubs won the World Series of Baseball in 2016.
Question: Extract all the dates from this paragraph provided.
Evidence: The New Joint Endeavor for Welfare, Education, and Liberation, or New JEWEL Movement (NJM), was a Marxist–Leninist vanguard party in the Caribbean island nation of Grenada that was led by Maurice Bishop.

Established in 1973, the NJM issued its manifesto prior to the granting of independence to Grenada in 1974. The movement took control of the country with a successful bloodless revolution in 1979 and ruled by decree as the People's Revolutionary Government until 1983. In 1983, Bishop was killed by paramilitaries affiliated with hard-liners in his own party. This led to a military government, which was deposed by the US military in a 1983 invasion.
Answer: 1973, 1974, 1979, 1983
Question: What dataset do the authors use in evaluating the performance of their method on the PI task?
Evidence: We evaluate our method on natural language inference (NLI), paraphrase identification (PI), and sentiment classification. We also conduct analysis on gate values and experiments on model variants. For detailed experimental settings, we refer readers to the supplemental material.
For the NLI and PI tasks, there exists recent work specializing in sentence pair classification. However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the models in extracting semantics. But note that the applicability of CAS-LSTM is not limited to sentence encoding based approaches.
We use Quora Question Pairs dataset in evaluating the performance of our method on the PI task.
Answer: Quora Question Pairs dataset
Question: Think up 5 different reasons to give my boss to take off work.
Answer: Here are 5 different reasons you can give your boss to take off work:
1. You have to go to your kid's award ceremony
2. You got food poisoning
3. The power at your house went out
4. You car is in the shop and you have no ride to work
5. Your family made a surprise visit to see you.
Question: What is a tornado?
Answer: A tornado is a major storm that consists of rotating columns of air that touch the base of a thunderstorm and surface of the earth.  They have speeds ranging from 86 - 200 miles per hour and destroy everything in their path.  They can last for seconds or minutes at a time.  States that have the most tornados are Texas, Kansas, Oklahoma, Alabama, and Mississippi.  People that are in the path of a large tornado often say it sounds like an approaching freight train.
Question: Who was Bosch's dad?
Evidence: Bosch's mother was a prostitute in Hollywood who was murdered on October 28, 1961, when Bosch was 11 years old. His father, who he met later in life, was Mickey Haller Sr., a prominent defense attorney known for representing mobster Mickey Cohen, among other clients.
Answer: Bosch's father was Mickey Haller Sr. a well known defense lawyer.
Question: Who is the lead singer of the Gorillaz?
Answer: Damon Albarn is the lead singer of the Gorillaz
Question: What's a relaxing activity to do when visiting Seoul, South Korea?
Answer: The Secret Garden guided tour is a tranquil stroll through the Changdeokgung palace in Seoul. Featuring pavilions and several ponds, you can explore the grounds once walked upon by princes and kings. In addition to a reservation for a guided tour, you will also need a ticket into the palace that can be purchased separately. Reservations may be made online or on-site, but reserving online will provide more timeframes and a better guarantee of entrance on your desired day.
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 (Pro) side, and the other argues for the negative (Con) side.

The debate is organized in a sequence of speeches according to the following outline:
•	Team 1 Constructive
•	Team 2 Constructive
•	1st Crossfire
•	Team 1 Rebuttal
•	Team 2 Rebuttal
•	2nd Crossfire
•	Team 1 Summary
•	Team 2 Summary
•	Grand Crossfire
•	Team 1 Final Focus
•	Team 2 Final Focus

Additionally, each team is given 3 minutes of preparation time throughout the round, which they can choose to take before any of their speeches. The time duration of each stage may vary across different leagues.
Question: Does the paper report the results of previous models applied to the same tasks?
Evidence: Technical and theoretical questions related to the proposed method and infrastructure for the exploration and facilitation of debates will be discussed in three sections. The first section concerns notions of how to define what constitutes a belief or opinion and how these can be mined from texts. To this end, an approach based on the automated extraction of semantic frames expressing causation is proposed. The observatory thus builds on the theoretical premise that expressions of causation such as `global warming causes rises in sea levels' can be revelatory for a person or group's underlying belief systems. Through a further technical description of the observatory's data-analytical components, section two of the paper deals with matters of spatially modelling the output of the semantic frame extractor and how this might be achieved without sacrificing nuances of meaning. The final section of the paper, then, discusses how insights gained from technologically observing opinion dynamics can inform conceptual modelling efforts and approaches to on-line opinion facilitation. As such, the paper brings into view and critically evaluates the fundamental conceptual leap from machine-guided observation to debate facilitation and intervention.
The final section of the paper, then, discusses how insights gained from technologically observing opinion dynamics can inform conceptual modelling efforts and approaches to on-line opinion facilitation. As such, the paper brings into view and critically evaluates the fundamental conceptual leap from machine-guided observation to debate facilitation and intervention.
Answer: Yes.
Question: What API do they use to retrieve additional data associated with the user identity for each tweet in the original dataset?
Evidence: We experimented with a dataset of approximately 16k short messages from Twitter, that was made available by BIBREF12 . The dataset contains 1943 tweets labeled as Racism, 3166 tweets labeled as Sexism and 10889 tweets labeled as Neutral (i.e., tweets that neither contain sexism nor racism). There is also a number of dual labeled tweets in the dataset. More particularly, we found 42 tweets labeled both as both `Neutral' and `Sexism', while six tweets were labelled as both `Racism' and `Neutral'. According to the dataset providers, the labeling was performed manually.
The relatively small number of tweets in the dataset makes the task more challenging. As reported by several authors already, the dataset is imbalanced, with a majority of neutral tweets. Additionally, we used the public Twitter API to retrieve additional data associated with the user identity for each tweet in the original dataset.
As reported by several authors already, the dataset is imbalanced, with a majority of neutral tweets. Additionally, we used the public Twitter API to retrieve additional data associated with the user identity for each tweet in the original dataset.
Answer: The public Twitter API.
Question: From the given text, list down some points about television series Sadie J
Evidence: Sadie J is a British children's television comedy-drama series about a girl named Sadie Jenkins, who is described as "the only girl in a boys' world" because she is surrounded by her Dad Steve, his apprentice Keith, her brother Danny, his best friend Jake and their dog Roger. It is produced by the BBC and airs on the CBBC Channel. The show premiered on 14 January 2011 and produced three series, with the final episode aired on 27 March 2013. As of September 2013, Sadie J is now shown in the United States on the Starz Kids & Family cable network.
Answer: 1. Sadie Jenkins is the protagonist of the British children's television comedy-drama series Sadie J. She is said to as "the only girl in a boys' world" because she lives with her father Steve, his apprentice Keith, her brother Danny, his best friend Jake, and their dog Roger.
2. It is a BBC production and is broadcast on the CBBC Channel.
3. The programme debuted on January 14, 2011, produced three seasons, and its final episode broadcast on March 27, 2013. 
4. As of September 2013, the Starz Kids & Family cable network now broadcasts Sadie J in the United States.
Question: What are the shortcomings of previous work in explaining input-output behavior?
Evidence: Interpretability of Seq2Seq models has recently been explored mainly from two perspectives: interpreting internal representations and understanding input-output behaviors. Most of the existing work focus on the former thread, which analyzes the linguistic information embeded in the learned representations BIBREF3, BIBREF4, BIBREF10 or the hidden units BIBREF6, BIBREF5. Several researchers turn to expose systematic differences between human and NMT translations BIBREF11, BIBREF12, indicating the linguistic properties worthy of investigating. However, the learned representations may depend on the model implementation, which potentially limit the applicability of these methods to a broader range of model architectures. Accordingly, we focus on understanding the input-output behaviors, and validate on different architectures to demonstrate the universality of our findings.
Concerning interpreting the input-output behavior, previous work generally treats Seq2Seq models as black-boxes BIBREF13, BIBREF7. For example, alvarez2017causal measure the relevance between two input-output tokens by perturbing the input sequence. However, they do not exploit any intermediate information such as gradients, and the relevance score only resembles attention scores. Recently, Jain2019AttentionIN show that attention scores are in weak correlation with the feature importance. Starting from this observation, we exploit the intermediate gradients to better estimate word importance, which consistently outperforms its attention counterpart across model architectures and language pairs.
The intermediate gradients have proven to be useful in interpreting deep learning models, such as NLP models BIBREF14, BIBREF15 and computer vision models BIBREF16, BIBREF9. Among all gradient-based approaches, the integrated gradients BIBREF9 is appealing since it does not need any instrumentation of the architecture and can be computed easily by calling gradient operations. In this work, we employ the IG method to interpret NMT models and reveal several interesting findings, which can potentially help debug NMT models and design better architectures for specific language pairs.
Concerning interpreting the input-output behavior, previous work generally treats Seq2Seq models as black-boxes (Li et al., 2016; Alvarez-Melis and Jaakkola, 2017). For example, Alvarez-Melis and Jaakkola (2017) measure the relevance between two input-output tokens by perturbing the input sequence. However, they do not exploit any intermediate information such as gradients, and the relevance score only resembles attention scores.
Answer: They do not exploit any intermediate information such as gradients, and the relevance score only resembles attention scores.
Question: Best car Brand in the world in Popularity?
Answer: 1. BMW
2. Audi
3. porsche