DataHammer/scimrc huggingface dataset:
Question: List the names of five of Adam Sandler's movies
Answer: Waterboy
Happy Gilmore
The Wedding Singer
Anger Management
Hustle
Question: Are there any significant result from Figure 2?
Evidence: To assess the relationship between the hardness degree of samples and the misclassification rate, we compute the hardness degree of CIFAR10 and CIFAR100 test samples and then partition them into ten groups based on their hardness degree. It is important to note that the number of samples in each group is different. Afterward, we calculate what percentage of samples in each group is classified incorrectly. Figure 6 demonstrates that the misclassification rate is increased by increasing the hardness degree of samples. In other words, there is a strong positive correlation between the hardness degree of samples and the misclassification rate. The figure indicates the percentage of samples in each hardness degree range by a green curve. For example, the hardness degree of 40.65% of CIFAR10 test samples (4065 samples) is in the range [0,9] for MobileNet classifier, from which 99.88% is classified correctly, or the hardness degree of 7.4% of CIFAR10 test samples (740 samples) is in the range [90,99] for MobileNet classifier, from which 55.27% is classified correctly. More than 99% and 95% of samples being learned in the first 30 epochs (hardness degree < 30) are correctly classified in CIFAR10 and CIFAR100 test samples, respectively. On the other side, less than 55% and 36% of samples being learned in the last 10 epochs (hardness degree ≥ 90) are correctly classified in CIFAR10 and CIFAR100 test samples, respectively.
Answer: We first want to mention that since Figure 2 is not directly relevant to the main purpose of the paper, we have moved it to Appendix B and changed the figure to avoid misunderstanding. As mentioned in the paper, calculating the hardness degree of samples does not require the true label of samples. Nevertheless, since we had access to the true labels of CIFAR10 and CIFAR100 test samples, we depict Figure 2 (Figure 6) to give more intuition about the hardness degree of samples. To depict this figure, we first calculate the hardness degree of CIFAR10 and CIFAR100 test samples and then partition them into ten groups based on their hardness degree. Notably, the number of samples in each group is different. Afterward, we calculate what percentage of samples in each group is classified incorrectly. The figure demonstrates that the misclassification rate is increased by increasing the hardness degree of samples. In other words, there is a strong positive correlation between the hardness degree of samples and the misclassification rate. Figure 6 indicates the percentage of samples in each hardness degree range by a green curve. We have added an example to Appendix B to clarify Figure 6.
Question: Who were the members of England's men's football World Cup winning team in 1966?
Answer: - Gordon Banks (Goalkeeper)
- George Cohen (Defender)
- Ray Wilson (Defender)
- Nobby Stiles (Midfielder)
- Jack Charlton (Defender)
- Bobby Moore (Defender, Captain)
- Alan Ball (Midfielder)
- Jimmy Greaves (Forward)
- Bobby Charlton (Midfielder)
- Geoff Hurst (Forward)
- John Connelly (Forward)
- Ron Springett (Goalkeeper)
- Peter Bonetti (Goalkeeper)
- Jimmy Armfield (Defender)
- Gerry Byrne (Defender)
- Martin Peters (Midfielder)
- Ron Flowers (Midfielder)
- Norman Hunter (Defender)
- Terry Paine (Forward)
- Ian Callaghan (Midfielder)
- Roger Hunt (Forward)
- George Eastham (Midfielder)
Question: What are some examples of frequently purchased essential oils?
Answer: Some examples of frequently purchased essential oils include peppermint, spearmint, tea tree, grapefruit, lemon, orange, rosemary, and spruce oils.
Question: Why the structure of the authors' model is flexible in practice?
Evidence: We compare performances of our model with the implementation of BIBREF13 and the results are shown in Table TABREF43 . Our model obtains better performances in Multi-Domain scenario with an average improvement of 4.5%, where datasets are product reviews on different domains with similar sequence lengths and the same class number, thus producing stronger correlations. Multi-Cardinality scenario also achieves significant improvements of 2.77% on average, where datasets are movie reviews with different cardinalities.
However, Multi-Objective scenario benefits less from multi-task learning due to lacks of salient correlation among sentiment, topic and question type. The QC dataset aims to classify each question into six categories and its performance even gets worse, which may be caused by potential noises introduced by other tasks. In practice, the structure of our model is flexible, as couplings and fusions between some empirically unrelated tasks can be removed to alleviate computation costs.
We further explore the influence of INLINEFORM0 in TOS on our model, which can be any positive integer. A higher value means larger and more various samples combinations, while requires higher computation costs.
Figure FIGREF45 shows the performances of datasets in Multi-Domain scenario with different INLINEFORM0 . Compared to INLINEFORM1 , our model can achieve considerable improvements when INLINEFORM2 as more samples combinations are available. However, there are no more salient gains as INLINEFORM3 gets larger and potential noises from other tasks may lead to performance degradations. For a trade-off between efficiency and effectiveness, we determine INLINEFORM4 as the optimal value for our experiments.
In order to measure the correlation strength between two task INLINEFORM0 and INLINEFORM1 , we learn them jointly with our model and define Pair-wise Performance Gain as INLINEFORM2 , where INLINEFORM3 are the performances of tasks INLINEFORM4 and INLINEFORM5 when learned individually and jointly.
We calculate PPGs for every two tasks in Table TABREF35 and illustrate the results in Figure FIGREF47 , where darkness of colors indicate strength of correlation. It is intuitive that datasets of Multi-Domain scenario obtain relatively higher PPGs with each other as they share similar cardinalities and abundant low-level linguistic characteristics. Sentences of QC dataset are much shorter and convey unique characteristics from other tasks, thus resulting in quite lower PPGs.
In practice, the structure of our model is flexible, as couplings and fusions between some empirically unrelated tasks can be removed to alleviate computation costs.
Answer: Because couplings and fusions between some empirically unrelated tasks can be removed to alleviate computation costs.
Question: Give me a list of the best bikes for riding on roads.
Answer: The best bikes for riding on the road are Cervelo, BMC, Pinarello, Specialized and Felt.
Question: If Phil has three siblings, Margaret, Janis, and Louis then what are all are the names of Phil’s mother’s children?
Answer: Phil’s mother named her children Phil, Margaret, Janis, and Louis.
Question: What is economics?
Answer: Economics is the study of the ALLOCATION of SCARCE resources to meet UNLIMITED
human wants.
Question: When did space exploration begin ?
Evidence: Space exploration stared during the late 1920s leading to the first crewed rocket cars and rocket planes, then there was the US and Soviet activities from 1950 onwards
Answer: The world's first large-scale experimental rocket program was Opel-RAK under the leadership of Fritz von Opel and Max Valier during the late 1920s leading to the first crewed rocket cars and rocket planes, which paved the way for the Nazi era V2 program and US and Soviet activities from 1950 onwards. The Opel-RAK program and the spectacular public demonstrations of ground and air vehicles drew large crowds, as well as caused global public excitement as so-called "Rocket Rumble" and had a large long-lasting impact on later spaceflight pioneers like Wernher von Braun. Common rationales for exploring space include advancing scientific research, national prestige, uniting different nations, ensuring the future survival of humanity, and developing military and strategic advantages against other countries. The early era of space exploration was driven by a "Space Race" between the Soviet Union and the United States. The launch of the first human-made object to orbit Earth, the Soviet Union's Sputnik 1, on 4 October 1957, and the first Moon landing by the American Apollo 11 mission on 20 July 1969 are often taken as landmarks for this initial period. The Soviet space program achieved many of the first milestones, including the first living being in orbit in 1957, the first human spaceflight (Yuri Gagarin aboard Vostok 1) in 1961, the first spacewalk (by Alexei Leonov) on 18 March 1965, the first automatic landing on another celestial body in 1966, and the launch of the first space station (Salyut 1) in 1971. After the first 20 years of exploration, focus shifted from one-off flights to renewable hardware, such as the Space Shuttle program, and from competition to cooperation as with the International Space Station (ISS).

With the substantial completion of the ISS following STS-133 in March 2011, plans for space exploration by the U.S. remain in flux. Constellation, a Bush Administration program for a return to the Moon by 2020 was judged inadequately funded and unrealistic by an expert review panel reporting in 2009. The Obama Administration proposed a revision of Constellation in 2010 to focus on the development of the capability for crewed missions beyond low Earth orbit (LEO), envisioning extending the operation of the ISS beyond 2020, transferring the development of launch vehicles for human crews from NASA to the private sector, and developing technology to enable missions to beyond LEO, such as Earth–Moon L1, the Moon, Earth–Sun L2, near-Earth asteroids, and Phobos or Mars orbit.
Question: Tell me whether these are food or drink: juice, hamburger, coke, pizza, hotdog, steak, lemonade, apple cider.
Answer: Food: hamburger, pizza, hotdog, steak.
Drink: juice, coke, pizza, lemonade, apple cider.
Question: Is their data set the first large-scale dataset for QA over social media data?
Evidence: With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous datasets haveconcentrated on question answering (QA) forformal text like news and Wikipedia, wepresent the first large-scale dataset for QA oversocial media data. To ensure that the tweetswe collected are useful, we only gather tweetsused by journalists to write news articles. Wethen ask human annotators to write questionsand answers upon these tweets. Unlike otherQA datasets like SQuAD in which the answersare extractive, we allow the answers to be ab-stractive. We show that two recently proposedneural models that perform well on formaltexts are limited in their performance when ap-plied to our dataset. In addition, even the fine-tuned BERT model is still lagging behind hu-man performance with a large margin. Our re-sults thus point to the need of improved QAsystems targeting social media text.
In this paper, we propose the first large-scale dataset for QA over social media data.
Answer: Yes.
Question: Please summarize the causes and cure for color blindness given in the passage?
Evidence: The most common cause of color blindness is an inherited problem or variation in the functionality of one or more of the three classes of cone cells in the retina, which mediate color vision. The most common form is caused by a genetic disorder called congenital red–green color blindness. Males are more likely to be color blind than females, because the genes responsible for the most common forms of color blindness are on the X chromosome. Non-color-blind females can carry genes for color blindness and pass them on to their children. Color blindness can also result from physical or chemical damage to the eye, the optic nerve, or parts of the brain. Screening for color blindness is typically done with the Ishihara color test.

There is no cure for color blindness. Diagnosis may allow an individual, or their parents/teachers to actively accommodate the condition. Special lenses such as EnChroma glasses or X-chrom contact lenses may help people with red–green color blindness at some color tasks, but they do not grant the wearer "normal color vision". Mobile apps can help people identify colors.
Answer: The most common cause of color blindness is genetic disorder. Males are more likely to be color blind than females, because the genes responsible for the same are present on the X chromosome. Females who are non-color blind could still carry and pass on genes for color blindness. Any kind of physical or chemical damage to the eye, optic nerve or brain could also lead to the same.

There is no cure for color blindness. Affected people can actively accommodate the condition or use special glasses or lenses that can help people with red-green color blindness to a certain extent. There are mobile apps available as well to help identify colors.
Question: What is the plot of Persuasion by Jane Austen?
Answer: Persuasion, published in 1817, is romantic novel with themes of mature love, second chances, and family loyalty.

Persuasion centers around Anne Elliot, who is one of three daughters of Sir Walter Elliot.  Sir Walter is a widower who is conceited and selfish, and spends lavishly and beyond his means, which has forced the family to retrench by renting out their landed estate and relocating to lower-cost Bath.  

Anne is wise and kind but, at 26, is past her prime.  At 19, Anne fell in love with, and accepted a marriage proposal from, Frederick Wentworth, a promising naval commander who is of a lower social status than Anne.  Anne is persuaded by her family (father and older sister) and close friend (Lady Russell) to break off the proposal.  

The novel starts with the family planning for their relocation to Bath, and the Elliots have coincidentally rented their home to the sister and brother-in-law of Frederick Wentworth, now a naval captain.  This provides Captain Wentworth with the opportunity to re-enter Anne’s life and her social circle.  Captain Wentworth has achieved much success and wealth from his naval career.  While Anne immediately notices and admires Captain Wentworth, he initially remains cold to her and commented that she has aged significantly since he last saw her.  The plot takes various twists and turns, with other potential romantic interests for Captain Wentworth and Anne. As Captain Wentworth sees more of Anne, he learns to appreciate and respect her, and ultimately they fall in love again and agree to renew their engagement.
Question: What dataset does the paper use?
Evidence: Named entity typing is the task of detecting the type (e.g., person, location, or organization) of a named entity in natural language text. Entity type information has shown to be useful in natural language tasks such as question answering BIBREF0 , knowledge-base population BIBREF1 , BIBREF2 , and co-reference resolution BIBREF3 . Motivated by its application to downstream tasks, recent work on entity typing has moved beyond standard coarse types towards finer-grained semantic types with richer ontologies BIBREF0 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . Rather than assuming an entity can be uniquely categorized into a single type, the task has been approached as a multi-label classification problem: e.g., in “... became a top seller ... Monopoly is played in 114 countries. ...” (fig:arch), “Monopoly” is considered both a game as well as a product.
The state-of-the-art approach BIBREF8 for fine-grained entity typing employs an attentive neural architecture to learn representations of the entity mention as well as its context. These representations are then combined with hand-crafted features (e.g., lexical and syntactic features), and fed into a linear classifier with a fixed threshold. While this approach outperforms previous approaches which only use sparse binary features BIBREF4 , BIBREF6 or distributed representations BIBREF9 , it has a few drawbacks: (1) the representations of left and right contexts are learnt independently, ignoring their mutual connection; (2) the attention on context is computed solely upon the context, considering no alignment to the entity; (3) document-level contexts which could be useful in classification are not exploited; and (4) hand-crafted features heavily rely on system or human annotations.
To overcome these drawbacks, we propose a neural architecture (fig:arch) which learns more context-aware representations by using a better attention mechanism and taking advantage of semantic discourse information available in both the document as well as sentence-level contexts. Further, we find that adaptive classification thresholds leads to further improvements. Experiments demonstrate that our approach, without any reliance on hand-crafted features, outperforms prior work on three benchmark datasets.
In this paper, we focus on designing different variants of using BERT on the extractive summarization task and showing their results on CNN/Dailymail and NYT datasets.
Answer: CNN/Dailymail and NYT datasets.
Question: Who is Lilith?
Answer: Lilith is a figure who appears in both Mesopotamian and Judaic mythology and is purported to be Adam's first wife. She was banished from the Garden of Eden for not being subservient and obeying him.
Question: Which one of these instruments are brass instruments? trombone, viola, trumpet, tuba, flute, clarinet, saxophone and piano.
Answer: The instruments out that list that are brass instruments are: trombone, trumpet and tuba.
Question: Give me a chronological bulleted list based on a timeline of events
Evidence: Olson served as a law clerk for Judge Barbara Jacobs Rothstein on the United States District Court for the Western District of Washington for two years immediately after finishing law school. She served as a trial attorney and later Deputy Director of the National Church Arson Task Force in the United States Department of Justice Civil Rights Division from 1992 to 1997. From 1994 to 1997, she also worked part-time as an adjunct professor at George Washington University Law School.

In 1997, Olson joined the Office of the United States Attorney for the District of Idaho, rising to the rank of Senior Litigation Counsel at the time of her appointment in 2010.

After Olson resigned in 2017, she joined the Boise office of Stoel Rives, a law firm that operates in the Pacific Northwest. In 2021, Olson was included on a shortlist of possible nominees to succeed Judge B. Lynn Winmill.
Answer: * 1992 to 1997: Olson served as a trial attorney and later Deputy Director of the National Church Arson Task Force in the United States Department of Justice Civil Rights Division
* 1994 to 1997: Olson worked part-time as an adjunct professor at George Washington University Law School
* 1997: Olson joined the Office of the United States Attorney for the District of Idaho
* 2010: Olson rose to the rank of Senior Litigation Counsel
* 2017: Olson resigned
* 2017: Olson joined the Boise office of Stoel Rives, a law firm that operates in the Pacific Northwest
* 2021: Olson was included on a shortlist of possible nominees to succeed Judge B. Lynn Winmill
Question: What is the population density of Germany?
Evidence: Germany, officially the Federal Republic of Germany, is a country in Central Europe. It is the second-most populous country in Europe after Russia, and the most populous member state of the European Union. Germany is situated between the Baltic and North seas to the north, and the Alps to the south; it covers an area of 357,022 square kilometres (137,847 sq mi), with a population of over 84 million within its 16 constituent states. Germany borders Denmark to the north, Poland and the Czech Republic to the east, Austria and Switzerland to the south, and France, Luxembourg, Belgium, and the Netherlands to the west. The nation's capital and most populous city is Berlin and its main financial centre is Frankfurt; the largest urban area is the Ruhr.
Answer: The population density of Germany is over 235 people per square kilometer, or over 600 people per square mile
Question: Which tastes better, red or green grapes?
Answer: Taste. The infamous grocery store dispute often stems from the taste. The superior grape stems from personal preference. Red grapes are often considered sweeter whereas green grapes are sourer
Question: What is the difference between a bath and a shower?
Answer: While both a bath and a shower are common ways to get clean, they involve different processes. A bath is when someone fills a tub and immerses themselves in it, whereas a shower is when someone stands under a steady stream of water in order to get clean.
Question: Identify which instrument is string or percussion: Sheker, Taishogoto
Answer: Taishogoto is string, Sheker is percussion.
Question: What did the note in Hermione's hand say when she was found petrified in Harry Potter and the Chamber of Secrets?
Answer: The note simply said the word, "pipes." Harry Potter and his friends, Hermione Granger and Ron Weasley, were attempting to discover the truth of the legendary Chamber of Secrets, and the monster guarding it. Hermione had realized that the monster must be a basilisk, a rare and very dangerous snake-like beast that had been using the pipes to travel about Hogwarts.
Question: What were the origins of basketball?
Answer: James Naismith, a gym instructor in New England, invented basketball in 1891 as an alternative to football. The original basketball game involved throwing a ball into a peach basket. Since then, the game has progressed to become one of the most popular sports in the world.
Question: How do you play Baseball?
Answer: Baseball is a game between two teams involving a baseball, which is a ball that is about 2 inches in diameter, and a bat used to hit the ball. Two teams alternate turns pitching and hitting the ball. The hitting team attempts to score as many runs by advancing through the four bases, prior to their team getting three outs during their turn hitting. The pitching team's objective is to prevent the hitting team from scoring runs. Games traditionally last 9 innings, with each team having the opportunity to both hit and pitch 9 times. The team with the most runs after 9 innings wins the game.