DataHammer/scimrc huggingface dataset:
Question: From the passage identify the subtypes of Hepatovirus A. Display the results in comma separated format.
Evidence: Hepatovirus A is a species of virus in the order Picornavirales, family Picornaviridae, genus Hepatovirus. Humans and other vertebrates serve as natural hosts.

Nine members of Hepatovirus are recognized. These species infect bats, rodents, hedgehogs, and shrews. Phylogenetic analysis suggests a rodent origin for Hepatitis A.[citation needed]

A member virus of hepatovirus B (Phopivirus) has been isolated from a seal. This virus shared a common ancestor with Hepatovirus A about 1800 years ago.[citation needed]

Another hepatovirus – Marmota himalayana hepatovirus – has been isolated from the woodchuck Marmota himalayana. This virus appears to have had a common ancestor with the primate-infecting species around 1000 years ago.[citation needed]

One serotype and seven different genetic groups (four human and three simian) have been described. The human genotypes are numbered I–III. Six subtypes have been described (IA, IB, IIA, IIB, IIIA, IIIB). The simian genotypes have been numbered IV–VI. A single isolate of genotype VII isolated from a human has also been described. Genotype III has been isolated from both humans and owl monkeys. Most human isolates are of genotype I. Of the type I isolates subtype IA accounts for the majority.

The mutation rate in the genome has been estimated to be 1.73–9.76 × 10−4 nucleotide substitutions per site per year. The human strains appear to have diverged from the simian about 3600 years ago. The mean age of genotypes III and IIIA strains has been estimated to be 592 and 202 years, respectively.
Answer: IA, IB, IIA, IIB, IIIA, IIIB
Question: Are the 4 BAE attack modes almost always stronger than previous baselines on 7 text classification datasets?
Evidence: Recent studies have shown the vulnerability of ML models to adversarial attacks, small perturbations which lead to misclassification of inputs. Adversarial example generation in NLP BIBREF0 is more challenging than in common computer vision tasks BIBREF1, BIBREF2, BIBREF3 due to two main reasons: the discrete nature of input space and ensuring semantic coherence with the original sentence. A major bottleneck in applying gradient based BIBREF4 or generator model BIBREF5 based approaches to generate adversarial examples in NLP is the backward propagation of the perturbations from the continuous embedding space to the discrete token space.
Recent works for attacking text models rely on introducing errors at the character level in words BIBREF6, BIBREF7 or adding and deleting words BIBREF8, BIBREF9, BIBREF10, etc. for creating adversarial examples. These techniques often result in adversarial examples which are unnatural looking and lack grammatical correctness, and thus can be easily identified by humans.
TextFooler BIBREF11 is a black-box attack, that uses rule based synonym replacement from a fixed word embedding space to generate adversarial examples. These adversarial examples do not account for the overall semantics of the sentence, and consider only the token level similarity using word embeddings. This can lead to out-of-context and unnaturally complex replacements (see Table ), which can be easily identifiable by humans.
The recent advent of powerful language models BIBREF12, BIBREF13 in NLP has paved the way for using them in various downstream applications. In this paper, we present a simple yet novel technique: BAE (BERT-based Adversarial Examples), which uses a language model (LM) for token replacement to best fit the overall context. We perturb an input sentence by either replacing a token or inserting a new token in the sentence, by means of masking a part of the input and using a LM to fill in the mask (See Figure FIGREF1). BAE relies on the powerful BERT masked LM for ensuring grammatical correctness of the adversarial examples. Our attack beats the previous baselines by a large margin and confirms the inherent vulnerabilities of modern text classification models to adversarial attacks. Moreover, BAE produces more richer and natural looking adversarial examples as it uses the semantics learned by a LM.
To the best of our knowledge, we are the first to use a LM for adversarial example generation. We summarize our major contributions as follows:

We propose BAE, a novel strategy for generating natural looking adversarial examples using a masked language model.
We introduce 4 BAE attack modes, all of which are almost always stronger than previous baselines on 7 text classification datasets.
We show that, surprisingly, just a few replace/insert operations can reduce the accuracy of even a powerful BERT-based classifier by over $80\%$ on some datasets.

We introduce 4 BAE attack modes, all of which are almost always stronger than previous baselines on 7 text classification datasets.
Answer: Yes, they are.
Question: What is the evaluation metric?
Evidence: We evaluate our CEQE model on the WMT2018 Quality Estimation Shared Task for word-level English-German, German-English, English-Czech, and English-Latvian QE. Words in all languages are lowercased. The evaluation metric is the multiplication of F1-scores for the “OK” and “BAD” classes against the true labels. F1-score is the harmonic mean of precision and recall. In Table TABREF15 , our model achieves the best performance on three out of six test sets in the WMT 2018 word-level QE shared task.
We evaluate our CEQE model on the WMT2018 Quality Estimation Shared Task3 for wordlevel English-German, German-English, EnglishCzech, and English-Latvian QE. Words in all languages are lowercased. The evaluation metric is the multiplication of F1-scores for the “OK” and “BAD” classes against the true labels.
Answer: The multiplication of F1-scores for the “OK” and “BAD” classes against the true labels.
Question: How is moral bias measured?
Evidence: Analogous to word embeddings, sentence embeddings, e.g. the Universal Sentence Encoder BIBREF8 and Sentence-BERT BIBREF6, allow one to calculate the cosine similarity of various different sentences, as for instance the similarity of a question and the corresponding answer. The more appropriate a specific answer is to a given question, the stronger is its cosine similarity expected to be. When considering two opposite answers, it is therefore possible to determine a bias value:
where $\vec{q}$ is the vector representation of the question and $\vec{a}$ and $\vec{b}$ the representations of the two answers/choices. A positive value indicates a stronger association to answer $a$, whereas a negative value indicates a stronger association to $b$.
Analogous to word embeddings, sentence embeddings, e.g. the Universal Sentence Encoder BIBREF8 and Sentence-BERT BIBREF6, allow one to calculate the cosine similarity of various different sentences, as for instance the similarity of a question and the corresponding answer. The more appropriate a specific answer is to a given question, the stronger is its cosine similarity expected to be. When considering two opposite answers, it is therefore possible to determine a bias value:

where $\vec{q}$ is the vector representation of the question and $\vec{a}$ and $\vec{b}$ the representations of the two answers/choices.
Answer: Answer with content missing: (formula 1) bias(q, a, b) = cos(a, q) − cos(b, q)
Bias is calculated as substraction of cosine similarities of question and some answer for two opposite answers.
Question: What baseline models do they compare against?
Evidence: FLOAT SELECTED: Table 2: Experimental Results of Models
FLOAT SELECTED: Table 2: Experimental Results of Models
Answer: SLQA, Rusalka, HMA Model (single), TriAN (single), jiangnan (ensemble), MITRE (ensemble), TriAN (ensemble), HMA Model (ensemble)
Question: What is the wettest state in the US?
Answer: Hawaii – 63.70 inches of rain per year
Question: What is One Piece?
Evidence: The series focuses on Monkey D. Luffy, a young man made of rubber, who, inspired by his childhood idol, the powerful pirate Red-Haired Shanks, sets off on a journey from the East Blue Sea to find the mythical treasure, the One Piece, and proclaim himself the King of the Pirates. In an effort to organize his own crew, the Straw Hat Pirates,[Jp 5] Luffy rescues and befriends a pirate hunter and swordsman named Roronoa Zoro, and they head off in search of the titular treasure. They are joined in their journey by Nami, a money-obsessed thief and navigator; Usopp, a sniper and compulsive liar; and Sanji, an amorous but chivalrous cook. They acquire a ship, the Going Merry,[Jp 6] and engage in confrontations with notorious pirates of the East Blue. As Luffy and his crew set out on their adventures, others join the crew later in the series, including Tony Tony Chopper, an anthropomorphized reindeer doctor; Nico Robin, an archaeologist and former Baroque Works assassin; Franky, a cyborg shipwright; Brook, a skeleton musician and swordsman; and Jimbei, a whale shark-type fish-man and former member of the Seven Warlords of the Sea who becomes their helmsman. Once the Going Merry is damaged beyond repair, Franky builds the Straw Hat Pirates a new ship, the Thousand Sunny,[Jp 7] Together, they encounter other pirates, bounty hunters, criminal organizations, revolutionaries, secret agents, different types of scientists, and soldiers of the corrupt World Government, and various other friends and foes, as they sail the seas in pursuit of their dreams.
Answer: One Piece is a manga and an anime about pirates. The main protagonist is Monkey D. Luffy who became a rubber man after eating a Rubber Devil Fruit. Devil Fruit are primarily how people get powers in this world. Luffy's goal is to find the One Piece, a fabled treasure that would allow him to become the King of Pirates. His crew includes a swordsman Roronoa Zoro, a navigator Nami, a sniper Usopp, a chef Sanji, a doctor Tony Chopper, an archaeologist Nico Robin, a cyborg shipwright Franky, a skeleton musician Brook, and a fish-man Jimbei. Together they travel between islands and encounter pirates, criminal organizations, World Government officials, and many friends and enemies as they seek to fulfill their dreams.
Question: Which of the following people were Prime Ministers of the United Kingdom? Margaret Thatcher, Winston Churchill, Queen Elizabeth II, Benjamin Disraeli, Thomas Cromwell
Answer: Margaret Thatcher, Winston Churchill, and Benjamin Disraeli were Prime Ministers of the United Kingdom.
Question: Which places are near Lightwater.
Evidence: Lightwater is a village in the Surrey Heath district of Surrey, England, about 27 miles (43 km) southwest of central London. Immediately surrounding towns and villages include Bagshot, Deepcut, Windlesham, Camberley, and West End, Woking. It is bounded to the north-west by the M3 motorway and to the north-east by the "Lightwater Bypass" (A322). To the south-east it is bounded by the "Red Road" (B311), while to the south-west there are vehicle-testing tracks owned by the Ministry of Defence.

The village is adjacent to junction 3 of the M3 motorway. The nearest railway station is at Bagshot, on the line between Ascot and Aldershot. Lightwater is part of the civil parish of Windlesham, which has a population of 17,000 and also includes the neighbouring village of Bagshot.
Answer: Lightwater is near Bagshot, Windlesham, Camberley, Deepcut and West End, Woking. It is about 27 miles from London.
Question: When did World War II finish?
Answer: World War II, also knowns as WW2 or WWII, was a global conflict that started in 1939 and ended in 1945.
Question: are these seas or oceans?
atlantic
indian
caspian
red
arabian
pacific
baltic
yellow
southern
Answer: seas: caspian, red, arabian, baltic, yellow, southern
oceans: atlantic, indian, pacific
Question: What is a sonnet?
Answer: In the English language, a sonnet is a poem comprised of 14 lines.  The most commonly known sonnets written by William Shakespeare contain 12 lines of iambic pentameter followed by a couplet.  The couplet usually rhymes.
Question: How many neurons are in the softmax layer?
Evidence: We use the CNN structure as described in Section SECREF5 for emotional feature extraction. As a dataset for extracting emotion-related features, we use the corpus developed by BIBREF35 . This dataset consists of blog posts labeled by their corresponding emotion categories. As emotion taxonomy, the authors used six basic emotions, i.e., Anger, Disgust, Surprise, Sadness, Joy and Fear. In particular, the blog posts were split into sentences and each sentence was labeled. The dataset contains 5,205 sentences labeled by one of the emotion labels. After employing this model on the sarcasm dataset, we obtained a 150-dimensional feature vector from the fully-connected layer. As the aim of training is to classify each sentence into one of the six emotion classes, we used six neurons in the softmax layer.
The final softmax determines whether a sentence is positive, negative or neutral. Thus, we have three neurons in the softmax layer.
Answer: Three. Positive, negative, or neutral.
Question: Give me the list of British artists born before 1700
Answer: These are the British artists born before 1700:

- Hans Holbein the Younger (1497/8–1543) – German artist and printmaker who became court painter in England
- Marcus Gheeraerts the Elder (c. 1520 – c. 1590) – Flemish printmaker and painter for the English court of the mid-16th century
- George Gower (1540–1596) – English portrait painter
- Nicholas Hilliard (1547–1619) – English goldsmith, limner, portrait miniature painter
- Rowland Lockey (c. 1565 – 1616) – English goldsmith, portrait miniaturist, painter
- Isaac Oliver (c. 1565 – 1617) – French-born English portrait miniature painter
- Anthony van Dyck (1599–1641) – Flemish Baroque painter, watercolourist and etcher who became court painter in England
- Wenceslaus Hollar (1607–1677) – Czech etcher
- Samuel Cooper (c. 1608 – 1672) – English miniature painter
- John Michael Wright (1617–1694) – British baroque portrait painter
- Peter Lely (1618–1680) – Dutch painter and portrait artist in England
- Francis Barlow (c. 1626 – 1704) – English painter, etcher, and illustrator
- David Loggan (1635–1692) – English baroque painter, born in Danzig
- Godfrey Kneller (1646/9–1723) – portrait painter in England
- Edward Pierce (1630–1695)
- Francis Place (1647–1728) – English potter and engraver
- James Thornhill (1675–1734) – English painter of historical subjects
- Jonathan Richardson (1665–1745) – English portrait painter
- Peter Monamy (1681–1749) – English marine painter
- John Wootton (1682–1764) – English painter of sporting subjects, battle scenes and landscapes
- Pieter Andreas Rysbrack (1685 or 1690–1748) – Flemish painter working in London
- John Michael Rysbrack (1694–1770) – Flemish sculptor working in London
- John Vanderbank (1694–1739) – English portrait painter and book illustrator
- William Hogarth (1697–1764) – English painter, printmaker, pictorial satirist, social critic and editorial cartoonist
Question: Classify these as protein, fats or carbohydrate sources: steak, yogurt, cheese, bananas, bread, pasta
Answer: Steak: protein, yogurt: protein, cheese: fats, bananas: carb, bread: carb, pasta: carb
Question: Extract the venues of each Phish concert referenced in this paragraph and list in bulleted format.
Evidence: On October 1, 2008, the band announced on their website that they had officially reunited, and would play their first shows in five years in March 2009 at the Hampton Coliseum in Hampton, Virginia. The three reunion concerts were held on March 6, 7, and 8, 2009, with "Fluffhead" being the first song the band played onstage at the first show. Approximately 14,000 people attended the concerts over the course of three days, and the band made the shows available for free download on their LivePhish website for a limited time, in order to accommodate fans who were unable to attend. When the band decided to reunite, the members agreed to limit their touring schedule, and they have typically performed about 50 concerts a year since. Following the reunion weekend, Phish embarked on a summer tour which began in May with a concert at Fenway Park in Boston. The Fenway show was followed by a 25-date tour which included performances at the 2009 edition of the Bonnaroo Music Festival in Tennessee and a four date stand at Red Rocks Amphitheatre in Colorado. At Bonnaroo, Phish was joined by Bruce Springsteen on guitar for three songs. Phish's fourteenth studio album, Joy, produced by Steve Lillywhite, was released September 8, 2009. In October, the band held Festival 8, their first multi-day festival event since Coventry in 2004, at the Empire Polo Club in Indio, California. In March 2010, Anastasio inducted Genesis, one of his favorite bands, into the Rock and Roll Hall of Fame at the museum's annual ceremony in New York City. In addition to Anastasio's speech, Phish performed the Genesis songs "Watcher of the Skies" and "No Reply at All" at the event. Phish toured in the summer and fall of 2010, and their concerts at Alpine Valley Music Theatre in East Troy, Wisconsin and the Utica Memorial Auditorium in Utica, New York were issued as CD/DVD sets in 2010 and 2011 respectively.
Answer: -Hampton Coliseum
-Fenway Park
-Red Rocks Ampitheatre
-Bonnaroo Music Festival
-Empire Polo Club
-New York City (venue for the Rock and Roll Hall of Fame ceremony is not specified)
-Alpine Valley Music Theatre
-Utica Memorial Auditorium
Question: What was the White Brigade
Evidence: The White Brigade (Dutch: Witte Brigade, French: Brigade blanche) was a Belgian resistance group, was founded on 23 July 1940 in Antwerp by Marcel Louette, who was nicknamed "Fidelio". The group was originally known as "De Geuzengroep" and changed its name again after the Liberation of Belgium to Witte Brigade-Fidelio as the term "white brigade" had emerged as a generic term to describe the resistance.

The name was chosen in opposition to the "Black Brigade", a collaborator group led by SS-Untersturmführer Reimond Tollenaere, who was responsible for the propaganda of pro-German Flemish National League. The Witte Brigade was based in Antwerp but had smaller branches in Gent, Lier, Aalst, Brussels, Waasland, Wallonia and in the coastal region.
Answer: The White Brigade (Dutch: Witte Brigade, French: Brigade blanche) was a Belgian resistance group, was founded on 23 July 1940 in Antwerp by Marcel Louette, who was nicknamed "Fidelio". The group was originally known as "De Geuzengroep" and changed its name again after the Liberation of Belgium to Witte Brigade-Fidelio as the term "white brigade" had emerged as a generic term to describe the resistance.
Question: How many letters there are in the hebrew language
Answer: The Hebrew alphabet consists of 22 letters, all consonants: Alef is the first letter of the Hebrew alphabet and Tav is the last. The Hebrew alphabet is often called the "alef-bet," because of its first two letters in the alphabet.
Question: How is it that some high net-worth individuals are able to avoid paying taxes completely?
Answer: The Internal Revenue Service (IRS) taxes regular income and investment income differently. For one type of common investment income i.e long term capital gains, the tax rates are 0%, 15% and 20% based on the annual income of the individual. The income used for calculating the above rates doesn't include income from the long term capital gains itself. For example, if an individual makes less than $41,675 for the year 2022 through regular income, his or her capital gains tax rate would be 0% even if the long term capital gains itself is more than a million dollars. Assuming many of these high net-worth individuals don't have any regular income and all their income is through long term capital gains, they end up paying no taxes at all.
Question: What are interesting plot twists for a novel?
Answer: Twists could include showing that a character who was thought dead is actually alive, or maybe revealing that a trusted character is actually a villain. Thrillers can use natural disasters or freak accidents to put characters in peril. Mystery novels can introduce new evidence that redirects suspicion or dramatically increases the scope of an investigation. New romantic relationships between characters can also add interesting twists. Novels can also use unreliable narrators to show that information has been withheld from the reader.
Question: By how much does their method outperform the multi-head attention model?
Evidence: FLOAT SELECTED: Table 2: Experimental results.
FLOAT SELECTED: Table 2: Experimental results.
Answer: Their average improvement in Character Error Rate over the best MHA model was 0.33 percent points.
Question: Who is Ronald Peter Nash
Evidence: Ronald Peter Nash CMG (born 18 September 1946) is a British diplomat who served as Ambassador to Afghanistan from 2002 to 2003. He was appointed Companion of the Order of St Michael and St George (CMG) in the 2004 New Year Honours.
Answer: Ronald Peter Nash CMG (born 18 September 1946) is a British diplomat who served as Ambassador to Afghanistan from 2002 to 2003. 

He was appointed Companion of the Order of St Michael and St George (CMG) in the 2004 New Year Honours.
Question: Which are three assumptions in current approaches for defining faithfulness?
Evidence: Defining Faithfulness ::: Assumption 1 (The Model Assumption).
Two models will make the same predictions if and only if they use the same reasoning process.
Defining Faithfulness ::: Assumption 2 (The Prediction Assumption).
On similar inputs, the model makes similar decisions if and only if its reasoning is similar.
Defining Faithfulness ::: Assumption 3 (The Linearity Assumption).
Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.
Defining Faithfulness ::: Assumption 1 (The Model Assumption).
Two models will make the same predictions if and only if they use the same reasoning process.
Defining Faithfulness ::: Assumption 2 (The Prediction Assumption).
On similar inputs, the model makes similar decisions if and only if its reasoning is similar.
Defining Faithfulness ::: Assumption 3 (The Linearity Assumption).
Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.
Answer: The answers are shown as follows:
* Two models will make the same predictions if and only if they use the same reasoning process.
* On similar inputs, the model makes similar decisions if and only if its reasoning is similar.
* Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.
Question: Let's imagine I create a fake currency called Yarns with two types of coins: a super-yarn (worth 10 yarns) and a mini-yarn (worth 0.5 yarn). How much money do I have in total if I have 2 super-yarns and three mini-yarns?
Answer: Given that a super-yarn is worth 10 yarns and a mini-yarn is worth 0.5 yarn, if you have 2 super-yarns and 3 mini-yarns you have 21.5 yarns in total.
Question: Give me the all-time hit movies of Rajnikanth.
Answer: Baasha
Padaiyappa
Annamalai
Chandramukhi
Thalapathi
Enthiran
Question: What's the difference between a turnover and a turnover on downs in the sport of American Football?
Answer: A turnover is when the offensive team, the team with possession of the football, loses possession of the football during play. This generally occurs in two ways: the defense intercepting a pass or recovering a fumble. A turnover on downs occurs only on the fourth and last down of a set of downs; the offense attempts to gain the yardage required to be given a new set of downs but fails to do so by means other than the defense's takeaway, such as an incomplete pass, or the ball carrier being tackled short of the line to gain.
Question: Give me a bulleted list of 5 outdoor activities I could do this weekend.
Answer: Here are 5 outdoor activities you could do this weekend:
1. Hike a long and steep trail through the mountains.
2. Surf in the Pacific ocean.
3. Ski at the resorts in Lake Tahoe.
4. Drink craft beers at local breweries.
5. Go for a long walk on the beach.