DataHammer/scimrc huggingface dataset:
Question: From the passage identify the symbols of Demeter. Display the results in a comma separated format.
Evidence: Demeter (Δημήτηρ, Dēmḗtēr)Goddess of grain, agriculture, harvest, growth, and nourishment. Demeter, whose Roman counterpart is Ceres, is a daughter of Cronus and Rhea, and was swallowed and then regurgitated by her father. She is a sister of Zeus, by whom she bore Persephone, who is also known as Kore, i.e. "the girl." One of the central myths associated with Demeter involves Hades' abduction of Persephone and Demeter's lengthy search for her. Demeter is one of the main deities of the Eleusinian Mysteries, in which the rites seemed to center around Demeter's search for and reunion with her daughter, which symbolized both the rebirth of crops in spring and the rebirth of the initiates after death. She is depicted as a mature woman, often crowned and holding sheafs of wheat and a torch. Her symbols are the cornucopia, wheat-ears, the winged serpent, and the lotus staff. Her sacred animals include pigs and snakes.
Answer: cornucopia, wheat-ears, the winged serpent, the lotus staff
Question: Classify the below based on the products they sell.
Adidas, nike, Pepsi, Sprite, Fanta, FILA
Answer: Adidas, nike, FILA - Shoe
Pepsi, Sprite, Fanta - Beverages
Question: How do authors initialize their sequential CWRs h?
Evidence: Traditional language models are estimated to maximize the likelihood of each word $x_i$ given the words that precede it, $p(x_i \mid x_{<i})$. Given a corpus that is annotated with shallow syntax, we propose to condition on both the preceding words and their annotations.
We associate with each word $x_i$ three additional variables (denoted $c_i$): the indices of the beginning and end of the last completed chunk before $x_i$, and its label. For example, in Fig. FIGREF8, $c_4=\langle 3, 3, \text{VP}\rangle $ for $x_4=\text{the}$. Chunks, $c$ are only used as conditioning context via $p(x_i \mid x_{<i}, c_{\leqslant i})$; they are not predicted. Because the $c$ labels depend on the entire sentence through the CRF chunker, conditioning each word's probability on any $c_i$ means that our model is, strictly speaking, not a language model, and it can no longer be meaningfully evaluated using perplexity.
A right-to-left model is constructed analogously, conditioning on $c_{\geqslant i}$ alongside $x_{>i}$. Following BIBREF2, we use a joint objective maximizing data likelihood objectives in both directions, with shared softmax parameters.
To reduce cost, we initialize our sequential CWRs h, using pretrained embeddings from ELMo-transformer.
Answer: Using pretrained embeddings from ELMo-transformer.
Question: I am trying to book a flight from Singapore to Sydney, what shall I do if the flight is too expensive?
Answer: You will have the option to choose from local Asian low-cost airlines such as Scoot, Jetstar, or AirAsia which would provide cheaper flights options.
Question: How to add two cells in Excel?
Answer: You can use the =cell1 + cell2 operation where cell1 is the first cell and cell2 is the 2nd cell
Question: Through their analysis, what are the reasons that their method achieves more remarkable performance boosts on PHEME than on RumourEval?
Evidence: Although the sifted multi-task learning method outperforms previous state-of-the-art methods on two datasets (From Table TABREF27), we observe that the proposed method achieves more remarkable performance boosts on PHEME than on RumourEval. There are two reasons for our analysis according to Table TABREF24 and Table TABREF27. One is that the number of training examples in RumourEval (including 5,568 tweets) is relatively limited as compared with PHEME (including 105,354 tweets), which is not enough to train deep neural networks. Another is that PHEME includes more threads (6,425 threads) than RumourEval (325 threads) so that PHEME can offer more rich credibility features to our proposed method.
Although the sifted multi-task learning method outperforms previous state-of-the-art methods on two datasets (From Table 2), we observe that the proposed method achieves more remarkable performance boosts on PHEME than on RumourEval. There are two reasons for our analysis according to Table 1 and Table 2. One is that the number of training examples in RumourEval (including 5,568 tweets) is relatively limited as compared with PHEME (including 105,354 tweets), which is not enough to train deep neural networks. Another is that PHEME includes more threads (6,425 threads) than RumourEval (325 threads) so that PHEME can offer more rich credibility features to our proposed method.
Answer: The number of training examples in RumourEval is relatively limited as compared with PHEME , which is not enough to train deep neural networks, and PHEME includes more threads than RumourEval so that PHEME can offer more rich credibility features to their method.
Question: From the passage provided, extract the per capita coffee consumption in kg for Belgium.
Evidence: Nordic countries are the highest coffee-consuming nations when measured per capita, with consumption in Finland as the world's highest.

Finland – 26.45 lb (12.00 kg)
Norway – 21.82 lb (9.90 kg)
Iceland – 19.84 lb (9.00 kg)
Denmark – 19.18 lb (8.70 kg)
Netherlands – 18.52 lb (8.40 kg)
Sweden – 18.00 lb (8.16 kg)
Switzerland – 17.42 lb (7.90 kg)
Belgium – 15.00 lb (6.80 kg)
Luxembourg – 14.33 lb (6.50 kg)
Canada – 14.33 lb (6.50 kg)
Answer: Belgium consumes 6.80 kg of coffee per capita.
Question: Classify the following by how they are affected by a strong permanent magnet as one of "attract", "repel", "nothing": iron nail, paper clips, a tin can, a compass, a plastic cup, a glass window, a leather wallet, a brick, a magnet of opposite polarity, pyrolytic carbon sheet.
Answer: Items that are strongly or weakly attracted are called "ferromagnetic" and "paramagnetic", respectively. These include: iron nail, paper clips, a tin can, a compass.
Items that are repelled by a magnet are either "paramagnetic" or themselves permanent magnets of the opposite polarity, including: a magnet of opposite polarity, pyrolytic carbon sheet. Items that are not affected by a permanent magnet are called "nonmagnetic" and include: a plastic cup, a glass window, a leather wallet, a brick.
Question: Why do many people spend so much time on their smartphone everyday?
Answer: There are lots of reasons and one of them is that lot of people are now using smartphones for both work and entertainment. Applications have been developed to help improve work efficiency as well as providing easy access to all kinds of entertaining content.
Question: How long are the questions on average?
Evidence: For all experiments, we admitted only experienced MTurk workers who are based in the US. One HIT consisted of writing one text for the text collection, formulating 15 questions for the question collection, or finding 15 pairs of answers for the answer collection. We paid $0.50 per HIT for the text and question collection, and $0.60 per HIT for the answer collection.
More than 2,100 texts were paired with 15 questions each, resulting in a total number of approx. 32,000 annotated questions. For 13% of the questions, the workers did not agree on one of the 4 categories with a 3 out of 5 majority, so we did not include these questions in our dataset.
The distribution of category labels on the remaining 87% is shown in Table TABREF10 . 14,074 (52%) questions could be answered. Out of the answerable questions, 10,160 could be answered from the text directly (text-based) and 3,914 questions required the use of commonsense knowledge (script-based). After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%). This ratio was manually verified based on a random sample of questions.
We split the dataset into training (9,731 questions on 1,470 texts), development (1,411 questions on 219 texts), and test set (2,797 questions on 430 texts). Each text appears only in one of the three sets. The complete set of texts for 5 scenarios was held out for the test set. The average text, question, and answer length is 196.0 words, 7.8 words, and 3.6 words, respectively. On average, there are 6.7 questions per text.
Figure FIGREF21 shows the distribution of question types in the dataset, which we identified using simple heuristics based on the first words of a question: Yes/no questions were identified as questions starting with an auxiliary or modal verb, all other question types were determined based on the question word.
We found that 29% of all questions are yes/no questions. Questions about details of a situation (such as what/ which and who) form the second most frequent question category. Temporal questions (when and how long/often) form approx. 11% of all questions. We leave a more detailed analysis of question types for future work.
The average text, question, and answer length is 196.0 words, 7.8 words, and 3.6 words, respectively.
Answer: 7.8 words.
Question: Who went to space first?
Answer: Yuri Gagarin was the first human to go into space. However, animals were sent before that to test out.
Question: Given a reference text about the Tax Cuts and Jobs Act, provide a bulleted list of the main elements of the act.
Evidence: The Act to provide for reconciliation pursuant to titles II and V of the concurrent resolution on the budget for fiscal year 2018, Pub. L. 115–97 (text) (PDF), is a congressional revenue act of the United States originally introduced in Congress as the Tax Cuts and Jobs Act (TCJA), that amended the Internal Revenue Code of 1986. Major elements of the changes include reducing tax rates for businesses and individuals, increasing the standard deduction and family tax credits, eliminating personal exemptions and making it less beneficial to itemize deductions, limiting deductions for state and local income taxes and property taxes, further limiting the mortgage interest deduction, reducing the alternative minimum tax for individuals and eliminating it for corporations, doubling the estate tax exemption, and set the penalty enforcing individual mandate of the Affordable Care Act (ACA) at $0.

The Act is based on tax reform advocated by congressional Republicans and the Trump administration. The nonpartisan Congressional Budget Office (CBO) reported that under the Act individuals and pass-through entities like partnerships and S corporations would receive about $1.125 trillion in net benefits (i.e. net tax cuts offset by reduced healthcare subsidies) over 10 years, while corporations would receive around $320 billion in benefits. The CBO estimated that implementing the Act would add an estimated $2.289 trillion to the national debt over ten years, or about $1.891 trillion after taking into account macroeconomic feedback effects, in addition to the $9.8 trillion increase forecast under the current policy baseline and existing $20 trillion national debt.[8
Answer: The main elements of the Tax Cuts and Jobs Act are:
- Reducing tax rates for businesses and individuals
- Increasing the standard deduction and family tax credits
- Eliminating personal exemptions
- Limiting deductions for state and local income and property taxes
- Limiting the mortgage interest deduction
- Reducing the alternative minimum tax
- Doubling the estate tax exemption
- Setting the penalty enforcing individual mandate of the Affordable Care Act at $0
Question: What model do the authors use in this paper?
Evidence: To recognize entailment between two questions INLINEFORM0 (premise) and INLINEFORM1 (hypothesis), we adapted the neural network proposed by Bowman et al. BIBREF13 . Our DL model, presented in Figure FIGREF20 , consists of three 600d ReLU layers, with a bottom layer taking the concatenated sentence representations as input and a top layer feeding a softmax classifier. The sentence embedding model sums the Recurrent neural network (RNN) embeddings of its words. The word embeddings are first initialized with pretrained GloVe vectors. This adaptation provided the best performance in previous experiments with RQE data.
GloVe is an unsupervised learning algorithm to generate vector representations for words BIBREF42 . Training is performed on aggregated word co-occurrence statistics from a large corpus, and the resulting representations show interesting linear substructures of the word vector space. We use the pretrained common crawl version with 840B tokens and 300d vectors, which are not updated during training.
Our DL model, presented in Figure 1, consists of three 600d ReLU layers, with a bottom layer taking the concatenated sentence representations as input and a top layer feeding a softmax classifier. The sentence embedding model sums the Recurrent neural network (RNN) embeddings of its words. The word embeddings are first initialized with pretrained GloVe vectors.
Answer: The DL model consists of three 600d ReLU layers, with a bottom layer taking the concatenated sentence representations as input and a top layer feeding a softmax classifier. The sentence embedding model sums the Recurrent neural network (RNN) embeddings of its words. The word embeddings are first initialized with pretrained GloVe vectors.
Question: What are some ways you can stop your toddler from crying?
Answer: Give your toddler a hug to comfort him/her; try to reconcile the fight between the siblings if that is the source of conflict; if all fails - cry louder than the toddler
Question: When was the "No, They Can't" book released?
Evidence: No, They Can't: Why Government Fails – But Individuals Succeed is a 2012 book by John Stossel, the American consumer reporter, investigative journalist, author and libertarian columnist. It was published on April 10, 2012, and focuses on what Stossel sees as the failures of government intervention.
Answer: The book "No, They Can't" was published on April 10, 2012.
Question: What came first the chicken or the egg?
Answer: At first glance, it may seem that a chicken must have come from an egg first. "Egg" is a word, that is used to describe the resulting product of procreation for many species. Based on the theory of evolution a non chicken egg at some point led to the birth of a chicken. It is important to note that an egg does not instantly turn into a chicken and there is a vast and potentially infinite spectrum of transitionary states. Somewhere in the infinite spectrum of this evolutionary transition a chicken and egg existed for a moment. One can argue that a chicken and its egg were created exactly at the same instant in time. We could further argue that the chicken and egg of yesterday is not the same chicken and egg of today.
Question: How does discrete latent variable has an explicit semantic meaning to improve the CVAE on short-text conversation?
Evidence: We find that BIBREF27 zhao2018unsupervised make use of a set of discrete variables that define high-level attributes of a response. Although they interpret meanings of the learned discrete latent variables by clustering data according to certain classes (e.g. dialog acts), such latent variables still have no exact meanings. In our model, we connect each latent variable with a word in the vocabulary, thus each latent variable has an exact semantic meaning. Besides, they focus on multi-turn dialogue generation and presented an unsupervised discrete sentence representation learning method learned from the context while our concentration is primarily on single-turn dialogue generation with no context information.
We find that BIBREF27 zhao2018unsupervised make use of a set of discrete variables that define high-level attributes of a response. Although they interpret meanings of the learned discrete latent variables by clustering data according to certain classes (e.g. dialog acts), such latent variables still have no exact meanings. In our model, we connect each latent variable with a word in the vocabulary, thus each latent variable has an exact semantic meaning. Besides, they focus on multi-turn dialogue generation and presented an unsupervised discrete sentence representation learning method learned from the context while our concentration is primarily on single-turn dialogue generation with no context information.
Answer: The answers are shown as follows:
* we connect each latent variable with a word in the vocabulary, thus each latent variable has an exact semantic meaning.
Question: Which year was quantum computer demonstrated to be possible?
Evidence: Over the years, experimentalists have constructed small-scale quantum computers using trapped ions and superconductors. In 1998, a two-qubit quantum computer demonstrated the feasibility of the technology, and subsequent experiments have increased the number of qubits and reduced error rates. In 2019, Google AI and NASA announced that they had achieved quantum supremacy with a 54-qubit machine, performing a computation that is impossible for any classical computer. However, the validity of this claim is still being actively researched.
Answer: 1998
Question: why peoples eating popcorn in the cinema?
Answer: People often eat popcorn in the cinema because it has become a popular snack food that is associated with movie-going. Popcorn is easy to eat with your hands and can be shared among a group, making it a convenient snack to enjoy while watching a film. In addition, the aroma of freshly popped popcorn is often enticing and can help to create a festive atmosphere in the cinema. Popcorn also has a long shelf life and can be stored in large quantities, making it an economical choice for cinemas. Overall, the tradition of eating popcorn in the cinema has become deeply ingrained in popular culture and is enjoyed by many movie-goers around the world.
Question: What is used to demonstrate the validity?
Evidence: The English and Dutch corpora were independently annotated for cyberbullying by trained linguists. All were Dutch native speakers and English second-language speakers. To demonstrate the validity of our guidelines, inter-annotator agreement scores were calculated using Kappa on a subset of each corpus. Inter-rater agreement for Dutch (2 raters) is calculated using Cohen's Kappa BIBREF53 . Fleiss' Kappa BIBREF54 is used for the English corpus ( INLINEFORM0 2 raters). Kappa scores for the identification of cyberbullying are INLINEFORM1 = 0.69 (Dutch) and INLINEFORM2 = 0.59 (English).
As shown in Table TABREF35 , inter-annotator agreement for the identification of the more fine-grained categories for English varies from fair to substantial BIBREF55 , except for defamation, which appears to be more difficult to recognise. No encouragements to the harasser were present in this subset of the corpus. For Dutch, the inter-annotator agreement is fair to substantial, except for curse and defamation. Analysis revealed that one of both annotators often annotated the latter as an insult, and in some cases even did not consider it as cyberbullying-related.
In short, the inter-rater reliability study shows that the annotation of cyberbullying is not trivial and that more fine-grained categories like defamation, curse and encouragements are sometimes hard to recognise. It appears that defamations were sometimes hard to distinguish from insults, whereas curses and exclusions were sometimes considered insults or threats. The analysis further reveals that encouragements to the harasser are subject to interpretation. Some are straightforward (e.g. `I agree we should send her hate'), whereas others are subject to the annotator's judgement and interpretation (e.g. `hahaha', `LOL').
To demonstrate the validity of our guidelines, inter-annotator agreement scores were calculated using Kappa on a subset of 9 each corpus.
Answer: Kappa is used to demonstrate validity.
Question: How was the Grammy Award named?
Answer: The Grammy was named after the Gramophone, which was used to play music before records, tape, discs, or digital.
Question: How was the KGR10 corpus created?
Evidence: KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocław CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish.
KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocław CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish.
Answer: The answers are shown as follows:
* most relevant content of the website, including all subsites
Question: What is mitosis
Answer: A type of cell division that results in two daughter cells each having the same number and kind of chromosomes as the "parent" chromosome.
Question: What are the top three cloud providers.
Answer: - AWS
- Microsoft Azure
- Google Cloud Platform (GCP)