DataHammer/scimrc huggingface dataset:
Question: What are the results of the experiment?
Evidence: The BD-4SK-ASR Dataset ::: The Language Model
We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams.
The BD-4SK-ASR Dataset ::: The Language Model
We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams.
Answer: They were able to create a language model from the dataset, but did not test.
Question: How is this approach used to detect incorrect facts?
Evidence: Here, we demonstrate another potential use of adversarial modifications: finding erroneous triples in the knowledge graph. Intuitively, if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data. Formally, to find the incorrect triple $\langle s^{\prime }, r^{\prime }, o\rangle $ in the neighborhood of the train triple $\langle s, r, o\rangle $ , we need to find the triple $\langle s^{\prime },r^{\prime },o\rangle $ that results in the least change $\Delta _{(s^{\prime },r^{\prime })}(s,r,o)$ when removed from the graph.
if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data.
Here, we demonstrate another potential use of adversarial modifications: finding erroneous triples in the knowledge graph. Intuitively, if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data. Formally, to find the incorrect triple $\langle s^{\prime }, r^{\prime }, o\rangle $ in the neighborhood of the train triple $\langle s, r, o\rangle $ , we need to find the triple $\langle s^{\prime },r^{\prime },o\rangle $ that results in the least change $\Delta _{(s^{\prime },r^{\prime })}(s,r,o)$ when removed from the graph.
Answer: The answers are shown as follows:
* if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data.
Question: What are Arabic numerals?
Answer: Arabic numerals refers to the ten digits commonly used today to represent numbers.  The digits are 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.  They were adopted by European mathematicians around the 10th century C.E. from numerals used by Arabic speakers in North Africa.  The numerals are used in a positional or place-value notation which makes complex calculations by humans easier.
Question: Do they perform some annotation?
Evidence: In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 . The datasets obtained pass through these steps of processing: (i) Removal of punctuatios, stopwords, URLs, numbers, emoticons, etc. This was then followed by transliteration using the Xlit-Crowd conversion dictionary and translation of each word to English using Hindi to English dictionary. To deal with the spelling variations, we manually added some common variations of popular Hinglish words. Final dictionary comprised of 7200 word pairs. Additionally, to deal with profane words, which are not present in Xlit-Crowd, we had to make a profanity dictionary (with 209 profane words) as well. Table TABREF3 gives some examples from the dictionary.
Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign). Some examples from the dataset are shown in Table TABREF4 . We use a LSTM based classifier model for training our model to classify these tweets into these three categories. An overview of the model is given in the Figure FIGREF12 . The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. Categorical crossentropy loss was used for the last layer due to the presence of multiple classes. We use Adam optimizer along with L2 regularisation to prevent overfitting. As indicated by the Figure FIGREF12 , the model was initially trained on the dataset provided by BIBREF1 , and then re-trained on the HEOT dataset so as to benefit from the transfer of learned features in the last stage. The model hyperparameters were experimentally selected by trying out a large number of combinations through grid search.
In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 . 
Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign)
Answer: No.
Question: Write a short story about a person who discovers a hidden room in their house. The story should include a plot twist and a clear resolution at the end.
Answer: Joelle bought a new house in her dream neighborhood nestled in the hills. The house was very old but beautiful and the perfect setting for Joelle to live the quiet life she was wishing for. She worked at the same busy job for 50 years and needed somewhere gentle for retirement. The day she moved in she brought all her things from her apartment and her old cat named Trixi. When Trixi explored the house, she searched each room on the bottom floor but wouldn't walk up the stairs to the second floor. Joelle assumed it was due to her old age and didn't force Trixi up the stairs. Then one day, Joelle came home from the store and couldn't find Trixi anywhere, she was shocked. She ran around the bottom floor of the house and eventually went upstairs to her bedroom defeated. She tripped over a loose blank in the floor and suddenly a trap door opened and she pummeled into darkness. Joelle was shocked and when her eyes adjusted she realized the trap door led to a secret staircase in the house. The staircase led to an old wine cellar. That turned out to be the best day of Joelle's life because she was able to auction off all the old wine bottles and artifacts and make millions of dollars.
Question: Why is Kylie Jenner famous?
Answer: Due to her older half-sister, Kim Kardashian. Kim let out a sex tape with her ex boyfriend in the early 2000s that went viral. At the same time, Kim and her family were filming the first season of their reality tv show - Keeping Up with the Kardashians. At the time, Kylie was in elementary school. Growing up in the spot light for the remainder of her childhood as well as launching a cosmetics line that became a billion dollar business, made Kylie famous.
Question: What are the plans of the authors for the future?
Evidence: We explored improving the performance of neural abstractive summarizers when applied to the low resource domain of student reflections using three approaches: domain transfer, data synthesis and the combination of both. For domain transfer, state of the art abstractive summarization model was pretrained using out-of-domain data (CNN/DM), then tuned using in-domain data (student reflections). The process of tuning improved ROUGE scores on the student reflection data, and at the same time produced more readable summaries. To incorporate synthetic data, we proposed a new template based synthesis model to synthesize new summaries. We showed that enriching the training data with this synthesized data can further increase the benefits of using domain transfer / tuning to increase ROUGE scores. We additionally showed that the proposed synthesis model outperformed a word replacement synthesis baseline. Future plans include trying domain adaptation, enhancing the synthesising process by using other models, further exploring template-based methods, and extending the analysis of the synthesis model to cover other types of data like reviews and opinions.
Future plans include trying domain adaptation, enhancing the synthesising process by using other models, further exploring template-based methods, and extending the analysis of the synthesis model to cover other types of data like reviews and opinions.
Answer: Future plans include trying domain adaptation, enhancing the synthesising process by using other models, further exploring template-based methods, and extending the analysis of the synthesis model to cover other types of data like reviews and opinions.
Question: What is the FA Cup?
Answer: The Football Association Challenge Cup, more commonly known as the FA Cup, is an annual knockout football competition in men's domestic English football. First played during the 1871–72 season, it is the oldest national football competition in the world. It is organised by and named after The Football Association (The FA). Since 2015, it has been known as The Emirates FA Cup after its headline sponsor. A concurrent Women's FA Cup has been held since 1970.

The competition is open to all eligible clubs down to Level 9 of the English football league system with Level 10 clubs acting as stand-ins in the event of non-entries from above. Included in the competition are 20 professional clubs in the Premier League (level 1), 72 professional clubs in the English Football League (levels 2 to 4), and all clubs in steps 1–5 of the National League System (levels 5 to 9) as well as a tiny number of step 6 clubs acting as stand-ins for non-entries above. A record 763 clubs competed in 2011–12. The tournament consists of 12 randomly drawn rounds followed by the semi-finals and the final. Entrants are not seeded, although a system of byes based on league level ensures higher ranked teams enter in later rounds – the minimum number of games needed to win, depending on which round a team enters the competition, ranges from six to fourteen.

The first six rounds are the Qualifying Competition, from which 32 teams progress to the first round of the Competition Proper, meeting the first of the 48 professional teams from Leagues One and Two. The last entrants are the Premier League and Championship clubs, into the draw for the Third Round Proper. In the modern era, only one non-League team has ever reached the quarter-finals, and teams below Level 2 have never reached the final. As a result, significant focus is given to the smaller teams who progress furthest, especially if they achieve an unlikely "giant-killing" victory.

Winners receive the FA Cup trophy, of which there have been two designs and five actual cups; the latest is a 2014 replica of the second design, introduced in 1911. Winners also qualify for the UEFA Europa League and a place in the upcoming FA Community Shield. Arsenal are the most successful club with fourteen titles and Arsène Wenger is the most successful manager in the history of the competition, having won seven finals as Arsenal's manager.

Liverpool are the current holders, having beaten Chelsea 6–5 on penalties following a 0–0 draw in the 2022 final.
Question: Given this paragraph about the rock band The Basics, what is notable about the Get Back album?
Evidence: Get Back is the first studio album by Australian rock and roll band The Basics. Released on 21 March 2003, the album consists of material composed by members Wally de Backer and Kris Schroeder. The album features De Backer, Schroeder, and original lead guitarist Michael Hubbard.
Answer: Get Back was their first studio album.
Question: What is their experimental assumption?
Evidence: A natural way of testing a reader's comprehension of a text is to ask her a question the answer to which can be deduced from the text. Hence the task we are trying to solve consists of answering a cloze-style question, the answer to which depends on the understanding of a context document provided with the question. The model is also provided with a set of possible answers from which the correct one is to be selected. This can be formalized as follows:
The training data consist of tuples INLINEFORM0 , where INLINEFORM1 is a question, INLINEFORM2 is a document that contains the answer to question INLINEFORM3 , INLINEFORM4 is a set of possible answers and INLINEFORM5 is the ground-truth answer. Both INLINEFORM6 and INLINEFORM7 are sequences of words from vocabulary INLINEFORM8 . We also assume that all possible answers are words from the vocabulary, that is INLINEFORM9 . In the CBT and CNN/Daily Mail datasets it is also true that the ground-truth answer INLINEFORM10 appears in the document. This is exploited by many machine learning models BIBREF2 , BIBREF4 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF10 , BIBREF11 , BIBREF12 , however some do not explicitly depend on this property BIBREF1 , BIBREF3 , BIBREF5 , BIBREF9 
We also assume that all possible answers are words from the vocabulary, that is A ⊆ W.
Answer: All possible answers are words from the vocabulary.
Question: Identify the first run broadcast network (ABC, NBC, or CBS) of the following TV shows:
Castle, Chicago Fire, CSI, ER, Grey's Anatomy, Scandal, Seinfeld, Magnum PI
Answer: In the US, Castle, Grey's Anatomy, and Scandal were first broadcast on ABC
Chicago Fire, ER and Seinfeld were first broadcast on NBC. CSI and Magnum PI were first broadcast on CBS, though Magnum PI switched to NBC for the 5th season of the rebooted series.
Question: Tell me a romantic fact about animals.
Answer: Several animals mate for life, such as beavers, gibbons, beavers and bald eagles.
Question: Who are the best starting MLB pitchers of all time?
Answer: There are many ways to argue who the best pitchers of all time are.  Some of the criteria to consider are Cy Young Awards, ERA titles, strike out titles and records, post season success, and longevity.  With these criteria heavily considered some of the best MLB pitchers of all time could be as follows.

5. Roger Clemens (The Rocket) pitched for 24 seasons and compiled accolades such as a record holding 7 Cy Young Awards, an MVP title, 5 strike out titles, 7 ERA titles, and won 2 World Series titles. 

4. Pedro Martinez had the 4th highest win percentage in history, won the world series, won 3 Cy Young Awards, an MVP Award, and is one of only 4 pitchers to ever have more than 3,000 strike outs while walking fewer than 1,000.  

3. Randy Johnson (The Big Unit) played for 22 MLB seasons and is the all time record holder for Wins, ranks 2nd all time for strike outs, won 5 Cy Young Awards, won The World Series. 

2. Cy Young is one of the most legendary pitchers of all time and the annual award for the best pitcher in each MLB league is named after him. Some of his many accomplishments are that he is the all time leader for wins, innings pitched, and complete games. 

1. Walter Johnson (The Big Train) was one of the "First Five" inaugurated into the MLB hall of fame.  He is the all time leader in shut outs and was the all time leader for strikeouts for more than 55 years.  He won The World Series and also is the all time leader for leading the league in strike outs 12 times.
Question: Write a letter to my boss that convinces them that I deserve a raise.
Answer: Hey boss, its me.  We have worked hard over these past few periods, and while I look back fondly on our memories, I have an ask of you.  You see, given how much I have sacrificed to deliver the results you have demanded over the past fiscal cycle, and we surpassed our goals, I was thinking perhaps you could show your appreciation with an reasonable increase in salary?  
Thank you for your consideration, and I look forward to surpassing our goals together in the future.
Question: Name all of the hosts from all versions of "The Price is Right."
Answer: Bob Barker, Drew Carey, Tom Kennedy, Dennis James
Question: Is text-to-image synthesis trained is suppervized or unsuppervized manner?
Evidence: black Deep learning shed some light to some of the most sophisticated advances in natural language representation, image synthesis BIBREF7, BIBREF8, BIBREF43, BIBREF35, and classification of generic data BIBREF44. However, a bulk of the latest breakthroughs in deep learning and computer vision were related to supervised learning BIBREF8. Even though natural language and image synthesis were part of several contributions on the supervised side of deep learning, unsupervised learning saw recently a tremendous rise in input from the research community specially on two subproblems: text-based natural language and image synthesis BIBREF45, BIBREF14, BIBREF8, BIBREF46, BIBREF47. These subproblems are typically subdivided as focused research areas. DC-GAN's contributions are mainly driven by these two research areas. In order to generate plausible images from natural language, DC-GAN contributions revolve around developing a straightforward yet effective GAN architecture and training strategy that allows natural text to image synthesis. These contributions are primarily tested on the Caltech-UCSD Birds and Oxford-102 Flowers datasets. Each image in these datasets carry five text descriptions. These text descriptions were created by the research team when setting up the evaluation environment. The DC-GANs model is subsequently trained on several subcategories. Subcategories in this research represent the training and testing sub datasets. The performance shown by these experiments display a promising yet effective way to generate images from textual natural language descriptions BIBREF8.
Even though natural language and image synthesis were part of several contributions on the supervised side of deep learning, unsupervised learning saw recently a tremendous rise in input from the research community specially on two subproblems: text-based natural language and image synthesis BIBREF45, BIBREF14, BIBREF8, BIBREF46, BIBREF47.
Answer: The answers are shown as follows:
* Even though natural language and image synthesis were part of several contributions on the supervised side of deep learning, unsupervised learning saw recently a tremendous rise in input from the research community specially on two subproblems: text-based natural language and image synthesis
Question: Based on the following passage what is the more dominant force in the universe?
Evidence: The concept of the electric field was introduced by Michael Faraday. An electric field is created by a charged body in the space that surrounds it, and results in a force exerted on any other charges placed within the field. The electric field acts between two charges in a similar manner to the way that the gravitational field acts between two masses, and like it, extends towards infinity and shows an inverse square relationship with distance. However, there is an important difference. Gravity always acts in attraction, drawing two masses together, while the electric field can result in either attraction or repulsion. Since large bodies such as planets generally carry no net charge, the electric field at a distance is usually zero. Thus gravity is the dominant force at distance in the universe, despite being much weaker.
An electric field generally varies in space and its strength at any one point is defined as the force (per unit charge) that would be felt by a stationary, negligible charge if placed at that point. The conceptual charge, termed a 'test charge', must be vanishingly small to prevent its own electric field disturbing the main field and must also be stationary to prevent the effect of magnetic fields. As the electric field is defined in terms of force, and force is a vector, having both magnitude and direction, so it follows that an electric field is a vector field.
The study of electric fields created by stationary charges is called electrostatics. The field may be visualised by a set of imaginary lines whose direction at any point is the same as that of the field. This concept was introduced by Faraday, whose term 'lines of force' still sometimes sees use. The field lines are the paths that a point positive charge would seek to make as it was forced to move within the field; they are however an imaginary concept with no physical existence, and the field permeates all the intervening space between the lines. Field lines emanating from stationary charges have several key properties: first, that they originate at positive charges and terminate at negative charges; second, that they must enter any good conductor at right angles, and third, that they may never cross nor close in on themselves.
Answer: Gravity is the dominant force at distance in the universe, despite being much weaker.
Question: Which game has been involved in this study?
Evidence: Text-based games -- in which an agent interacts with the world through textual natural language -- present us with the problem of combinatorially-sized action-spaces. Most current reinforcement learning algorithms are not capable of effectively handling such a large number of possible actions per turn. Poor sample efficiency, consequently, results in agents that are unable to pass bottleneck states, where they are unable to proceed because they do not see the right action sequence to pass the bottleneck enough times to be sufficiently reinforced. Building on prior work using knowledge graphs in reinforcement learning, we introduce two new game state exploration strategies. We compare our exploration strategies against strong baselines on the classic text-adventure game, Zork1, where prior agent have been unable to get past a bottleneck where the agent is eaten by a Grue.
We compare our exploration strategies against strong baselines on the classic text-adventure game, Zork1, where prior agent have been unable to get past a bottleneck where the agent is eaten by a Grue.
Answer: Zork1.
Question: Summarize the opening of the Gettysburg Address in simple modern English. 

"Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.

Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure. We are met on a great battle-field of that war. We have come to dedicate a portion of that field, as a final resting place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this."
Answer: 87 years ago, we created this country so that people could be equal. We will see if a country like this can survive civil war. It's right that we remember people who died fighting for this country and are buried here.
Question: How big is the dataset?
Evidence: We collected COVID-19 related Arabic tweets from January 1, 2020 until April 15, 2020, using Twitter streaming API and the Tweepy Python library. We have collected more than 3,934,610 million tweets so far. In our dataset, we store the full tweet object including the id of the tweet, username, hashtags, and geolocation of the tweet. We created a list of the most common Arabic keywords associated with COVID-19. Using Twitter’s streaming API, we searched for any tweet containing the keyword(s) in the text of the tweet. Table TABREF1 shows the list of keywords used along with the starting date of tracking each keyword. Furthermore, Table TABREF2 shows the list of hashtags we have been tracking along with the number of tweets collected from each hashtag. Indeed, some tweets were irrelevant, and we kept only those that were relevant to the pandemic.
We have collected more than 3,934,610 million tweets so far.
Answer: The answers are shown as follows:
* more than 3,934,610 million tweets
Question: Who are the greatest players that have played for Manchester United?
Answer: - Sir Bobby Charlton 
- Eric Cantona 
- Paul Scholes 
- Nemanja Vidic 
- David Beckham 
- Cristiano Ronaldo 
- Ruud Van Nistelrooy 
- Wayne Rooney 
- Gary Neville 
- Dennis Law 
- George Best 
- Peter Schmeichel