DataHammer/scimrc huggingface dataset:
Question: Does the SQUAD provide the mapping of the answer contexts to Wikipedia?
Evidence: All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively. SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times. On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts. Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes.
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate. SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes. It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries. InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes. InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively. In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close.
Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on.
Fig. FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a. Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models.
SQUAD provides no mapping of the answer contexts to Wikipedia, whereas WIKIQA and SELQA provide mappings; however, their data do not come from the same version of Wikipedia.
Answer: No, it doesn't.
Question: Is the hessian matrix mentioned in the discussion of SGD referring to  kernel matrix?
Evidence: A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function. Next we give the formal definition of kernel machines discussed in this paper. Given a training data set {X, y} of n training instances where {X ∈ R n×d , y ∈ R n } = {(x 1 , y 1 ), (x 2 , y 2 ), ..., (x n , y n )}, and (x i , y i ) denotes the instance x i ∈ R d with its label y i , the objective of the kernel machine training is to find an optimal ω * which minimizes the structural risk as follows.
min
where λ denotes the regularization constant and f (ω, x i ) = ω, φ(x i ) . The variable ω is defined on the reproducing kernel Hilbert space (RKHS) and •, • is the inner product on the RKHS. The function φ(•) maps the instances from their original data space to a higher dimensional feature space induced by the kernel function. Assume the loss l(•, •) is an affine function of ω. The representer theorem shows that a minimizer of the optimization problem ()
where k(x i , x j ) denotes a positive definite kernel function and k(x i , x j ) = φ(x i ), φ(x j ) . By substituting the expressions of f (ω, x i ) and ω into the Equation (1), we have the objective with respect to α below.
where α = [α 1 . . . α n ] T is an n-dimension vector, each dimension of which corresponds to the contribution of a training instance to the kernel machine.
Then, we can derive that the Hessian matrix H = [H ij ] n×n of Problem () is equal to the kernel matrix. The element in the i-th row and j-th column of the matrix H is H ij = k(x i , x j ). The derivations of the first and second derivatives can be found in the supplementary material. For clarity, we use kernel matrix H to denote the Hessian matrix of the kernel machine in the rest of the paper.
For clarity, we use kernel matrix H to denote the Hessian matrix of the kernel machine in the rest of the paper.
Answer: We have replaced the Hessian matrix with kernel matrix.
Question: Do the authors provide evidence that 'most' street gang members use Twitter to intimidate others?
Evidence: Street gang members have established online presences coinciding with their physical occupation of neighborhoods. The National Gang Threat Assessment Report confirms that at least tens of thousands of gang members are using social networking websites such as Twitter and video sharing websites such as YouTube in their daily life BIBREF0 . They are very active online; the 2007 National Assessment Center's survey of gang members found that 25% of individuals in gangs use the Internet for at least 4 hours a week BIBREF4 . Gang members typically use social networking sites and social media to develop online respect for their street gang BIBREF5 and to post intimidating, threatening images or videos BIBREF6 . This “Cyber-” or “Internet banging” BIBREF7 behavior is precipitated by the fact that an increasing number of young members of the society are joining gangs BIBREF8 , and these young members have become enamored with technology and with the notion of sharing information quickly and publicly through social media. Stronger police surveillance in the physical spaces where gangs congregate further encourages gang members to seek out virtual spaces such as social media to express their affiliation, to sell drugs, and to celebrate their illegal activities BIBREF9 .
The National Gang Threat Assessment Report confirms that at least tens of thousands of gang members are using social networking websites such as Twitter and video sharing websites such as YouTube in their daily life BIBREF0 . They are very active online; the 2007 National Assessment Center's survey of gang members found that 25% of individuals in gangs use the Internet for at least 4 hours a week BIBREF4 .
Answer: No.
Question: How large is the improvement over the baseline?
Evidence: Table shows how unsupervised decompositions affect QA. Our RoBERTa baseline performs quite well on HotpotQA (77.0 F1), despite processing each paragraph separately, which prohibits inter-paragraph reasoning. The result is in line with prior work which found that a version of our baseline QA model using BERT BIBREF26 does well on HotpotQA by exploiting single-hop reasoning shortcuts BIBREF21. We achieve significant gains over our strong baseline by leveraging decompositions from our best decomposition model, trained with USeq2Seq on FastText pseudo-decompositions; we find a 3.1 F1 gain on the original dev set, 11 F1 gain on the multi-hop dev set, and 10 F1 gain on the out-of-domain dev set. Unsupervised decompositions even match the performance of using (within our pipeline) supervised and heuristic decompositions from DecompRC (i.e., 80.1 vs. 79.8 F1 on the original dev set).
We achieve significant gains over our strong baseline by leveraging decompositions from our best decomposition model, trained with USeq2Seq on FastText pseudo-decompositions; we find a 3.1 F1 gain on the original dev set, 11 F1 gain on the multi-hop dev set, and 10 F1 gain on the out-of-domain dev set. 
Answer: The answers are shown as follows:
* 3.1 F1 gain on the original dev set
* 11 F1 gain on the multi-hop dev set
* 10 F1 gain on the out-of-domain dev set.
Question: What is an excavator?
Answer: An excavator is a heavy equipment machine that is used for moving large amount of earth at one time.  They have a boom and a huge bucket attached to them and are essentially a huge shovel used to prepare the ground for huge construction projects.  John Deere is one company that produces excavators.
Question: From the passage provided, extract when hallmarking was first observed in England.
Evidence: Hallmarking dates back to the 14th century when Edward I of England passed a law requiring any item made of silver, which was offered for sale to be at least of equal quality as that of the coin of the realm (silver currency). The four wardens of the Goldsmiths’ Company were tasked with visiting workshops in the City of London to assay (test) silver articles. If these articles were found to be below standard they were originally forfeit to the king, but if they passed, each article received the king's mark of authentication which was the mark of a leopard's head. By 1478, there were several hundred workshops and merchants manufacturing silver articles in the City of London. It was not possible for the wardens to visit them all so the merchants were ordered to bring their items to Goldsmiths’ Hall for testing and marking and a permanent assay office was established in the building. This is the origin of the term hallmark – struck with the king's mark at Goldsmiths’ Hall.

In 1544 the Goldsmith's Company adopted the king's mark as their town mark and the mark of the leopard's head is now internationally recognized as the mark of this assay office.
Answer: Hallmarking was first observed in England in the 14th century.
Question: What is email?
Answer: Email is form of electronic communication that is distributed between computers over closed and open networks.  The internet is the most common network that is used for email communication.  Email services that are very popular are gmail, outook, protonmail, and yahoo mail.  Major fortune 500 companies all use email communication in one form or another to help their companies and people all be more productive and deliver results faster.
Question: Write down some key points for the poem Garshasp-nama based on the given text.
Evidence: The Garshasp-nama is an epic poem by Asadi Tusi (died 1072/73). It has been described as one of the best epic poems in Persian literature, comparable to Shahnameh, by Ferdowsi, and the most important work of Asadi Tusi. Asadi Tusi completed the poem in 1066 and dedicated it to a certain Abu Dulaf, the ruler of Nakhjavan (nothing is known about him). The poem has also been translated to other languages such as French and German. It seems that Asadi Tusi wrote this poem based on a written source. Like the Shahnameh, it contains few Arabic loan-words and consists of some 9,000 verses. The main hero of this epic poem is Garshasp, the son of Etret, and grandson of Sām. The poem begins with the story of Jamshid and Zahhak. Jamshid is overthrown by Zahhak and flees to Zabolistan. In Zabolistan, Jamshid falls in love with an unnamed daughter of Kurang, the king of Zabolistan, and she bore a child for Jamshid, named Tur (not to be confused with Tur, the son of Freydun). Jamshid flees again to China. Garshasp is actually the grandson of Tur's grandson.
Answer: 1. The Garshasp-nama is an epic poem by Asadi Tusi (died 1072/73).
2. It has been described as one of the best epic poems in Persian literature, comparable to Shahnameh, by Ferdowsi, and the most important work of Asadi Tusi.
3. Asadi Tusi completed the poem in 1066 and dedicated it to a certain Abu Dulaf, the ruler of Nakhjavan (nothing is known about him).
4. The poem has also been translated to other languages such as French and German. 
5. It seems that Asadi Tusi wrote this poem based on a written source.
6. Like the Shahnameh, it contains few Arabic loan-words and consists of some 9,000 verses.
7. The main hero of this epic poem is Garshasp, the son of Etret, and grandson of Sām.
8. The poem begins with the story of Jamshid and Zahhak.
9.  Jamshid is overthrown by Zahhak and flees to Zabolistan. In Zabolistan, Jamshid falls in love with an unnamed daughter of Kurang, the king of Zabolistan, and she bore a child for Jamshid, named Tur (not to be confused with Tur, the son of Freydun).
10. Jamshid flees again to China.
11. Garshasp is actually the grandson of Tur's grandson.
Question: What are the pros and cons of working from home rather than going into an office downtown?
Answer: There are both advantages and disadvantages from working from home, and they apply differently to different people depending on the person's personality, family situation, location, and job description. Some key advantages are the ability to develop closer personal relationships with team members, have the opportunity for more spontaneous casual conversations, and experience more productive meetings because of the ability to read people's body language and to see presentation materials more clearly. Many people will experience better mental health by going into the office and establishing a sense of greater connection with team members. In addition, certain employees will take advantage of remote work to use work time to engage in personal activities rather than real work, to the detriment of the employer. Some disadvantages of going into the office include the time inefficiency from commuting, the cost of commuting, the time inefficiency that occurs from experiencing all the social chatting that occurs in the office, and the hassle of having to schedule conference rooms. Commuting also contributes to global warming through the greater use of fossil fuels. On balance, whether remote work makes sense is a complex question, and the answer will be unique for every employee and employer depending on the circumstances.
Question: What did the authors use to train on SNLI, MNLI, and an NLI-version2 of FEVER in round 2?
Evidence: For the second round, we used a more powerful RoBERTa model BIBREF25 trained on SNLI, MNLI, an NLI-version of FEVER BIBREF26, and the training data from the previous round (A1). After a hyperparameter search, we selected the model with the best performance on the A1 development set. Then, using the hyperparameters selected from this search, we created a final set of models by training several models with different random seeds. During annotation, we constructed an ensemble by randomly picking a model from the model set as the adversary each turn. This helps us avoid annotators exploiting vulnerabilities in one single model. A new non-overlapping set of contexts was again constructed from Wikipedia via HotpotQA using the same method as Round 1.
For the second round, we used a more powerful RoBERTa model (Liu et al., 2019b) trained on SNLI, MNLI, an NLI-version2 of FEVER (Thorne et al., 2018), and the training data from the previous round (A1).
Answer: A more powerful RoBERTa model.
Question: Given a reference text about Run Towards the Danger, tell me how many essays are part of the collection.
Evidence: Run Towards the Danger is a 2022 Canadian essay collection by Sarah Polley, a former child star, director, and screenwriter.

The six essays in the collection examine aspects of Polley's career on stage, screen, and on film detailing her roles in a Stratford Festival production of Alice Through the Looking Glass, as well as her breakout roles in The Adventures of Baron Munchausen and the TV series Road to Avonlea. The book also revealed for the first time that Polley had been a victim of Jian Ghomeshi who sexually and physically assaulted her when she was 16 and he was 28.
Answer: Six essays are part of the Run Towards the Danger essay collection.
Question: What are two strong baseline methods authors refer to?
Evidence: FLOAT SELECTED: Table 7: SRL results on the Chinese test set. We choose the best settings for each configuration of our model.
FLOAT SELECTED: Table 7: SRL results on the Chinese test set. We choose the best settings for each configuration of our model.
Answer: Marcheggiani and Titov (2017) and Cai et al. (2018)
Question: Can you summarize the book Annihilation?
Answer: Annihilation is a science fiction book taking place in the southern US inside a mysterious Area X. A team is assembled to explore the domain of Area X, led by a character only known as the psychologist. The main character is known as the biologist. These expeditions are managed by a mysterious government organization known as the Southern Reach.

The expedition centers on two notable landmarks within Area X, the topological anomaly, which the biologists calls a tower but is really a tunnel, and a lighthouse. The expedition encounters an entity within the topological anomaly that "writes" living words of non-sense on the wall of the anomaly in fungus. 

Early in the expedition, the biologist is contaminated by the fungus of the words in the anomaly, which grants her immunity to hypnotic suggestion, which the psychologist uses to control the other members of the expedition. The plot follows the biologist pursuing the psychologist in search of answers, leading a confrontation at the lighthouse. The psychologist jumps from the lighthouse, presumably forced to by the entity behind Area X, but lives long enough to confront the biologist. During the climax of that encounter, the psychologist shouts "Annihilation!" multiple times, and it is revealed that this is a hypnotic trigger word designed to induce suicide in the other expedition members.

After the psychologist succumbs to her injuries at the base of the lighthouse, the biologist embarks on a journey north to an island that is possibly related to the disappearance of her husband, who was a member of the previous expedition.
Question: Who was the mascot of 13th South Asian Games (SAG)
Answer: A pair of blackbucks
Question: Extract the names of the cities and rank them in alphabetical order?
Evidence: The United Kingdom is a constitutional monarchy and parliamentary democracy. Its capital and largest city is London, the capital of England, a global city and financial centre with a population of over 14 million people. Edinburgh, Cardiff and Belfast are the national capitals of Scotland, Wales and Northern Ireland respectively.
Answer: The cities listed in alphabetical order are Belfast, Cardiff, Edinburgh and London.
Question: Is it expected to have speaker role, semantic slot and dialog domain annotations in real world datasets?
Evidence: Moreover, we can easily extend SPNet to other summarization tasks. We plan to apply semantic slot scaffold to news summarization. Specifically, we can annotate the critical entities such as person names or location names to ensure that they are captured correctly in the generated summary. We also plan to collect a human-human dialog dataset with more diverse human-written summaries.
We plan to apply semantic slot scaffold to news summarization. Specifically, we can annotate the critical entities such as person names or location names to ensure that they are captured correctly in the generated summary. We also plan to collect a human-human dialog dataset with more diverse human-written summaries.
Answer: Not at the moment, but summaries can be additionaly extended with this annotations.
Question: How is plastic made?
Answer: Plastic is made from oil, natural gas and even plant oils during refining of these oils into other products like gasoline.  Ethane and propane are created when treated with heat during a refinery process called cracking.  This turns the Ethane and propane into ethylene and propylene which are used with other chemical ingredients to create polymers that are the base of what plastic is made out of.
Question: Which seventh season episode of Game of Thrones did Alan Taylor direct?
Answer: Alan Taylor directed "Beyond the Wall"
Question: Where was the 2017 European Judo Championships held
Answer: The men's 90 kg competition at the 2017 European Judo Championships in Warsaw was held on 22 April at the Torwar Hall.
Question: How can you open a can without using your hands?
Answer: 1) You could try to use both feet to open the can if you have very strong feet.
2) You could ask a friend to open the can with their hands.
3) You could use your elbow to push the can off of a very high surface.
4) You could drive over the can with a car, though you would have to use only your arms to drive.
Question: Given this paragraph about the Ain Elzarga Wetland, why is this location one of the most important wetlands in the region?
Evidence: Ain Elzarga Wetland is a Ramsar-protected wetland of Libya. It was established in 2000 and covers an area of 33 hectares (82 acres). Ramsar says of it: "A small natural sebkha or depression with at least one natural connection to the sea, wet all year round but with increasing water levels and salinity during summer. The sebkha, with mudflats and salt marsh community, is surrounded by dunes from east to west and rocky hills to the south and east. The site is one of the most important wetlands in the area of the El Kouf National Park for migratory waterbirds. The birdwatching and ecotourism potential is considerable but undeveloped. Unsustainable hunting and destruction of vegetation, especially during summer, are considered threats.
Answer: The Ain Elzarga Wetland is an important wetland due to being the home for migratory waterbirds.
Question: List the name of the games in Prince of Persia series along with their publication year. List the results in comma separated format.
Evidence: Prince of Persia is a video game franchise created by Jordan Mechner. It is built around a series of action-adventure games focused on various incarnations of the eponymous Prince, set in ancient and medieval Persia.

The first two games in the series, Prince of Persia (1989) and Prince of Persia 2: The Shadow and the Flame (1993), were published by Broderbund. Prince of Persia 3D (1999), named for being the first installment to use 3D computer graphics, was developed by Red Orb Entertainment and published by The Learning Company on PC; The Dreamcast version was developed by Avalanche Software and published by Mattel Interactive. Ubisoft bought the rights to the franchise in 2001 and rebooted it with Prince of Persia: The Sands of Time (2003). Ubisoft has since developed and published four additional entries in the series: Prince of Persia: Warrior Within (2004), Prince of Persia: The Two Thrones (2006), Prince of Persia (2008), and Prince of Persia: The Forgotten Sands (2010), as well as a number of spin-offs and games for mobile devices.

Outside of the games, the franchise also includes a film adaptation based on The Sands of Time, written in part by Mechner, and released by Walt Disney Pictures in 2010; a graphic novel; and the Lego Prince of Persia toyline. Ubisoft's Assassin's Creed franchise is considered to be the spiritual successor to the series.
Answer: Prince of Persia (1989), Prince of Persia 2: The Shadow and the Flame (1993), Prince of Persia 3D (1999), Prince of Persia: The Sands of Time (2003), Prince of Persia: Warrior Within (2004), Prince of Persia: The Two Thrones (2006), Prince of Persia (2008),  Prince of Persia: The Forgotten Sands (2010)
Question: What is GeneBass?
Answer: GeneBass is a CRISPR-Cas9 screening platform for the high-throughput functional analysis of genes in mammalian cells. The GeneBass platform allows researchers to systematically activate or repress genes of interest and study their effects on cellular processes such as cell proliferation, differentiation, and survival. The system uses a pooled lentiviral library of guide RNAs targeting every gene in the human or mouse genome, which can be introduced into cells using viral transduction. The guide RNAs are designed to target specific regions of the genome adjacent to the transcription start sites of each gene, allowing for specific and efficient gene activation or repression. GeneBass experiments typically involve the transduction of large numbers of cells, followed by selection or sorting of cells based on specific phenotypes of interest. High-throughput sequencing of the guide RNA library before and after selection can be used to identify genes that are essential for specific cellular processes or that are involved in disease pathways.