DataHammer/scimrc huggingface dataset:
Question: Why is sitting the new smoking?
Answer: Sitting is not good for your body, as it was created to move. Sedentary lifestyles have many health risk factors associated. It can decrease your bone density to live a sedentary lifestyle, leading to more injuries. Sitting all day will also overtime impact your flexibility and ability to be limber. This too, impacts your likelihood of becoming injured.
Question: What ways are studied in the experiment related to fertility?
Evidence: We experiment with three ways of setting the fertility of the source words: constant, guided, and predicted. With constant, we set the fertilities of all source words to a fixed integer value INLINEFORM0 . With guided, we train a word aligner based on IBM Model 2 (we used fast_align in our experiments, BIBREF17 ) and, for each word in the vocabulary, we set the fertilities to the maximal observed value in the training data (or 1 if no alignment was observed). With the predicted strategy, we train a separate fertility predictor model using a bi-LSTM tagger. At training time, we provide as supervision the fertility estimated by fast_align. Since our model works with fertility upper bounds and the word aligner may miss some word pairs, we found it beneficial to add a constant to this number (1 in our experiments). At test time, we use the expected fertilities according to our model.
We experiment with three ways of setting the fertility of the source words: constant, guided, and predicted.
Answer: They experiment with three ways of setting the fertility of the source words: constant, guided, and predicted.
Question: what is meetup group
Answer: Meetup events are real-life gatherings where members and organizers get together to connect, discuss, and practice activities related to their shared interests.
Question: What is the Foresight discipline?
Answer: Foresight is also known as strategic foresight and futures studies.  It is an emerging multi-disciplinary practice that analyzes change and its implications in the context of the future. 

It enables societies, organizations, and individuals to effectively explore the breadth of potential future scenarios in order to better adapt in the present.  Its purpose is to inform clear thinking, appropriate action, and effective decision making based on the identified trends that will have significant impact on their domain.

Some of the numerous methodologies for decision support that are incorporated in strategic foresight include scenario planning, systems analysis, back-casting, design thinking, trends analysis, horizon scanning, and systems thinking.
Question: Categorize the following animals into sea or land animals: shark, dolphin, bear, rat, salmon, moose, cockroach, manatee
Answer: Shark: Sea Animal
Dolphin: Sea Animal
Bear: Land Animal
Rat: Land Animal
Salmon: Sea Animal
Moose: Land Animal
Cockroach: Land Animal
Manatee: Sea Animal
Question: Given this paragraph about Aronia, what is another name for the plant and where were they first cultivated?
Evidence: Aronia is a genus of deciduous shrubs, the chokeberries, in the family Rosaceae native to eastern North America and most commonly found in wet woods and swamps.The genus Aronia is considered to have 3 species.The most common and widely used is Aronia melanocarpa (black chokeberry) which emerged from Eastern North America. The lesser known Aronia arbutifolia (red chokeberry) and the hybrid form of the abovementioned species called Aronia prunifolia (purple chokeberry) were first cultivated in Central and Eastern North America. In the eighteenth century, the first shrubs of the best-known species Aronia melanocarpa reached Europe where they were first cultivated in Scandinavia and Russia.
Answer: Aronia is also known as chokeberries and they were first cultivated in Scandinavia and Russia.
Question: What datasets are used to train the paraphrasing model?
Evidence: The paraphrasing model is used to measure the semantic relevance between the original question and the question generated from the QG model. We use bidirectional RNN with gated recurrent unit to represent two questions, and compose them with element-wise multiplication. The results are followed by a $softmax$ layer, whose output length is 2. The model is trained by minimizing the cross-entropy error, in which the supervision is provided in the training data.
We collect two datasets to train the paraphrasing model. The first dataset is from Quora dataset, which is built for detecting whether or not a pair of questions are semantically equivalent. 345,989 positive question pairs and 255,027 negative pairs are included in the first dataset. The second dataset includes web queries from query logs, which are obtained by clustering the web queries that click the same web page. In this way we obtain 6,118,023 positive query pairs. We implement a heuristic rule to get 6,118,023 negative instances for the query dataset. For each pair of query text, we clamp the first query and retrieve a query that is mostly similar to the second query. To improve the efficiency of this process, we randomly sample 10,000 queries and define the “similarity” as the number of co-occurred words between two questions. During training, we initialize the values of word embeddings with $300d$ Glove vectors, which is learned on Wikipedia texts. We use a held-out data consisting of 20K query pairs to check the performance of the paraphrasing model. The accuracy of the paraphrasing model on the held-out dataset is 87.36%.
We collect two datasets to train the paraphrasing model. The first dataset is from Quora dataset[https://data.quora.com/ First-Quora-Dataset-Release-Question-Pairs], which is built for detecting whether or not a pair of questions are semantically equivalent. 345,989 positive question pairs and 255,027 negative pairs are included in the first dataset. The second dataset includes web queries from query logs, which are obtained by clustering the web queries that click the same web page.
Answer: They collect two datasets to train the paraphrasing model. The first dataset is from Quora datase,the second dataset includes web queries from query logs, which are obtained by clustering the web queries that click the same web page.
Question: What are some of the best movies for kids?
Answer: Some movies that most children will enjoy include Disney Movies like Bambi, The Jungle Book and Peter Pan, Pixar movies like Toy Story, Cars and the Incredibles and Studio Ghibli movies like Spirited Away, My Neighbor Totoro and Ponyo.
Question: Classify each of the following as a programming language or not: Lisp, Basic, Esperanto, Prolog, Sympathy, Pascal, Tessellation, Kumquats, Algol, Java, C++, Corkscrew, Envy, Brainfuck
Answer: From the list, these are programming languages: Lisp, Basic, Prolog, Pascal, Algol, Java, C++, Envy, Brainfuck.
Question: What are the six target languages?
Evidence: Given the initial distributional or specialised collection of target language vectors INLINEFORM0 , we apply an off-the-shelf clustering algorithm on top of these vectors in order to group verbs into classes. Following prior work BIBREF56 , BIBREF57 , BIBREF17 , we employ the MNCut spectral clustering algorithm BIBREF58 , which has wide applicability in similar NLP tasks which involve high-dimensional feature spaces BIBREF59 , BIBREF60 , BIBREF18 . Again, following prior work BIBREF17 , BIBREF61 , we estimate the number of clusters INLINEFORM1 using the self-tuning method of Zelnik:2004nips. This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.
Results and Discussion
This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.

Results and Discussion
Answer: Answer with content missing: (3 Experimental Setup) We experiment with six target languages: French (FR), Brazilian Portuguese (PT), Italian (IT), Polish (PL), Croatian (HR), and Finnish (FI).
Question: From the given text, write some important points about guitar's vibrato system
Evidence: A vibrato system on a guitar is a mechanical device used to temporarily change the pitch of the strings. They add vibrato to the sound by changing the tension of the strings, typically at the bridge or tailpiece of an electric guitar using a controlling lever, which is alternately referred to as a whammy bar, vibrato bar, or incorrectly as a tremolo arm. The lever enables the player to quickly and temporarily vary the tension and sometimes length of the strings, changing the pitch to create a vibrato, portamento, or pitch bend effect. Instruments without a vibrato have other bridge and tailpiece systems.
Answer: 1. A vibrato system is a mechanical component that alters the pitch of the strings on a guitar.
2. They modify the string tension at the bridge or tailpiece of an electric guitar using a regulating lever, sometimes known as a whammy bar, vibrato bar, or wrongly as a tremolo arm, to add vibrato to the music.
3. With the help of the lever, the musician can quickly and momentarily alter the tension and occasionally the length of the strings, which alters the pitch and produces effects such as vibrato, portamento, and pitch bend.
4. Vibrato-free instruments need different bridge and tailpiece systems.
Question: Tell me whether these are Indian states or not: Karnataka, Kerala, Maharashtra, Chennai, Haryana, Delhi
Answer: The Indian states are: Karnataka, Kerala, Maharashtra, Haryana
Chennai is an Indian city which belongs to the state Tamil Nadu
Delhi is Indian union territory

India is a federal union comprising 28 states and 8 union territories, with a total of 36 entities. 

states: Andhra Pradesh, Arunachal Pradesh, Assam, Bihar, Chhattisgarh, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Manipur, Meghalaya, Mizoram, Nagaland, Odisha, Punjab, Rajasthan, Sikkim, Tamil Nadu, Telangana, Tripura, Uttar Pradesh, Uttarakhand, West Bengal

union territories: Andaman and Nicobar Islands, Chandigarh, Dadra and Nagar Haveli and Daman and Diu, Delhi, Jammu and Kashmir, Ladakh	, Lakshadweep, Puducherry
Question: Does parallel universe really exist?
Answer: When we talk about the possibility of multiple universes, we are referring to the idea that there could be many separate and distinct “realities” or “worlds” coexisting alongside our own. These universes may have different physical laws, properties, and dimensions, and could exist in parallel to our own universe. So basically, there’s this idea that there could be lots of different universes happening at the same time, and there are couple of evidences that make people think it might be true.

There are couple of evidences for parallel universe, such as Hawking radiation, which says that anything going into a black hole might end up in another universe. or the gravity waves act oddly and deviate objects from their orbits, possibly due to the influence of parallel universes.

This idea of multiple universes has been proposed in various fields of science, including physics, astronomy, and even psychology. For example, Stephen Hawking was the leading theoretical physicist who advocated multiverse theory. I believe it remains a highly theoretical concept till today, and it is an area of active research and exploration, as scientists continue to investigate the possibility of parallel universes and their potential implications for our understanding of the universe and our place in it.
Question: Name the moon rocket that NASA is going to launch thru Space Launch System vehicle
Answer: Artemis I
Question: When is India's independence day?
Answer: India celebrates her independence day on the 15th day of August every year. The prime minister hoists the national flag at red fort and gives a speech to the nation.
Question: What is abstract class?
Answer: A generic class that is used only as a template for derived classes, and
that cannot be instantiated.
Question: Who is the winner of International Booker Prize 2022 ?
Answer: Geetanjali Shree
Question: Given a reference text about a singer, tell me when they debuted, what their first release was, and what their most popular song is.
Evidence: Lee Ji-eun (Korean: 이지은; born May 16, 1993), also known by her stage name IU (Korean: 아이유), is a South Korean singer-songwriter, record producer, and actress. She signed with LOEN Entertainment (now Kakao Entertainment) in 2007 as a trainee and debuted as a singer at the age of fifteen with her first extended play (EP) Lost and Found (2008). Although her follow-up albums, Growing Up and IU...IM, brought mainstream success, it was only after the release of "Good Day" (Korean: 좋은 날), the lead single from her 2010 album Real, that she achieved national stardom. "Good Day" went on to spend five consecutive weeks at the top of South Korea's Gaon Digital Chart, and in 2019, it was ranked number one on Billboard's "100 Greatest K-Pop Songs of the 2010s" list.

Lost and Found is the debut extended play (EP) by South Korean singer-songwriter IU. It was released by LOEN Entertainment on September 23, 2008. IU collaborated with lyricist and producer Choi Gap-won, who had previously produced songs including "Amnesia" by Gummy, "Incurable Disease" by Wheesung, and "Toc Toc Toc" by Lee Hyori. Lee Jong-hoon of Soul-Shop and the singer Mario additionally worked on the title song, "Lost Child".
Answer: The singer Lee Ji-eun, or better known as IU, debuted on September 23, 2008 with her first extended play "Lost and Found". Her most popular song is "Good Day" from her 2010 album "Real" which was ranked number one on Billboard's "100 Greatest K-Pop Songs of the 2010s" list.
Question: What datasets do they use for the tasks?
Evidence: To compare fairly, we train and evaluate these systems on the VLSP corpora. In particular, we conduct experiments on Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task. All of these corpora are converted to CoNLL format. The corpus of POS tagging task consists of two columns namely word, and POS tag. For chunking task, there are three columns namely word, POS tag, and chunk in the corpus. The corpus of NER task consists of four columns. The order of these columns are word, POS tag, chunk, and named entity. While NER corpus has been separated into training and testing parts, the POS tagging and chunking data sets are not previously divided. For this reason, we use INLINEFORM0 of these data sets as a training set, and the remaining as a testing set. Because our system adopts early stopping method, we use INLINEFORM1 of these data sets from the training set as a development set when training NNVLP system. Table TABREF24 and Table TABREF25 shows the statistics of each corpus.
To compare fairly, we train and evaluate these systems on the VLSP corpora. In particular, we conduct experiments on Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task. All of these corpora are converted to CoNLL format. The corpus of POS tagging task consists of two columns namely word, and POS tag. For chunking task, there are three columns namely word, POS tag, and chunk in the corpus. The corpus of NER task consists of four columns. The order of these columns are word, POS tag, chunk, and named entity. While NER corpus has been separated into training and testing parts, the POS tagging and chunking data sets are not previously divided. For this reason, we use INLINEFORM0 of these data sets as a training set, and the remaining as a testing set. Because our system adopts early stopping method, we use INLINEFORM1 of these data sets from the training set as a development set when training NNVLP system. Table TABREF24 and Table TABREF25 shows the statistics of each corpus.
Answer: The answers are shown as follows:
*  Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task
Question: Does their system have any limitations on human study?
Evidence: After adding more data we have the performance on the CBT validation and test datasets soaring. However is there still potential for much further growth beyond the results we have observed?
We decided to explore the remaining space for improvement on the CBT by testing humans on a random subset of 50 named entity and 50 common noun validation questions that the psr ensemble could not answer correctly. These questions were answered by 10 non-native English speakers from our research laboratory, each on a disjoint subset of questions.. Participants had unlimited time to answer the questions and were told that these questions were not correctly answered by a machine, providing additional motivation to prove they are better than computers. The results of the human study are summarized in Table TABREF28 . They show that a majority of questions that our system could not answer so far are in fact answerable. This suggests that 1) the original human baselines might have been underestimated, however, it might also be the case that there are some examples that can be answered by machines and not by humans; 2) there is still space for improvement.
A system that would answer correctly every time when either our ensemble or human answered correctly would achieve accuracy over 92% percent on both validation and test NE datasets and over 96% on both CN datasets. Hence it still makes sense to use CBT dataset to study further improvements of text-comprehension systems.
The results of the human study are summarized in Table 3. They show that a majority of questions that our system could not answer so far are in fact answerable. This suggests that 1) the original human baselines might have been underestimated, however, it might also be the case that there are some examples that can be answered by machines and not by humans; 2) there is still space for improvement.
Answer: A majority of questions that their system could not answer so far are in fact answerable.
Question: Whether highway layers bring significant improvements to the proposed model?
Evidence: The architecture of our proposed model is as shown in Fig. FIGREF3 . It looks similar to the character-aware neural language model proposed by BIBREF1 , but we represent a word by the sequence of radical embeddings instead of character embeddings. Besides, unlike the former model, there are no highway layers in the proposed model, because we find that highway layers do not bring significant improvements to our proposed model (see Section SECREF31 ).
Besides, unlike the former model, there are no highway layers in the proposed model, because we find that highway layers do not bring significant improvements to our proposed model (see Section 5.3).
Answer: No.
Question: How many vowels are in the word dinosaur?
Answer: The letters a, e, I, o and u are vowels. As such there are 4 vowels in dinosaur
Question: Who services the Saint-Germain-les-Belles station?
Evidence: Saint-Germain-les-Belles is a railway station in Saint-Germain-les-Belles, Nouvelle-Aquitaine, France. The station is located on the Orléans–Montauban railway line. The station is served by TER (local) services operated by SNCF.
Answer: Saint-Germain-les-Belles station is serviced by the TER services operated by SNCF.
Question: Identify the vegetable: apple, orange, potatoes
Answer: Trick question, none of them are vegetables. Based on research from the early 2010s, potatoes doesn't have enough nutrimental benefits, to be classified as a vegetable.
Question: Who is the primary male protagonist on the TV show "The X-Files"?
Answer: Special Agent Fox William Mulder
Question: What are the best Japanese RPGs to try out?
Answer: JRPGs have been around since the late 80s and hence there are a lot of options to try out. If you're for something recent, games like Octopath Traveler (both the original and the sequel), the Xenoblade Chronicles series, or the latest Dragon Quest or Final Fantasy are all good bets. If you'd like to try a classic, try something like Final Fantasy 7, arguably the most famous JRPG in history, or Chrono Trigger, a cult classic that continues to have a large following today.
Question: Who is Charles Brigstocke?
Evidence: Charles Reginald Brigstocke CB (22 July 1876 – 7 April 1951) was a British civil servant.
Answer: Brigstocke was born in Carmarthen, Wales, and educated at Llandovery School. He entered the Civil Service in 1894. In 1901 he was called to the bar by the Middle Temple. Between 1903 and 1918 he worked at the Admiralty. In 1918 he transferred to the Air Ministry and was appointed Assistant Secretary in 1919, serving with the air delegation at the Versailles Conference. For these services he was appointed Companion of the Order of the Bath (CB) in the 1920 New Year Honours.
Question: How do you change a bicycle tire?
Answer: Start by taking off the wheel. For this, open the quick release for the brakes or detach the brake cable. Then, unscrew the axle to take off the wheel. Following, you have to deflate the tire and be able to pull out the tube from underneath the tire. Pry the tire off the rim with tire levers. Once the tire comes off, you can replace the tube and work it back underneath the tire. Remember only to inflate the tube completely once both tube and tire are fitted on the rim. To mount the wheel again, follow the above-described steps in reverse order.
Question: How many times Lewis Hamilton won the F1 Championship ?
Answer: 7 times
Question: Why micro-averaged F-1 is used in the study?
Evidence: NER involves identifying both entity boundaries and entity types. With “exact-match evaluation”, a named entity is considered correctly recognized only if both the boundaries and type match the ground truth BIBREF8 , BIBREF17 , BIBREF18 . Precision, Recall, and F-1 scores are computed on the number of true positives (TP), false positives (FP), and false negatives (FN). Their formal definitions are as follows:
True Positive (TP): entities that are recognized by NER and match the ground truth.
False Positive (FP): entities that are recognized by NER but do not match the ground truth.
False Negative (FN): entities annotated in the ground which that are not recognized by NER.
Precision measures the ability of a NER system to present only correct entities, and Recall measures the ability of a NER system to recognize all entities in a corpus. DISPLAYFORM0 
The F-1 score is the harmonic mean of precision and recall, and the balanced F-1 score is the variant which is most commonly used. This is defined as: DISPLAYFORM0 
Since most NER systems involve multiple entity types, it is often required to assess the performance across all entity classes. Two measures are commonly used for this purpose: the macro-averaged F-1 score and the micro-averaged F-1 score. The macro-averaged F-1 score computes the F-1 score independently for each entity type, then takes the average (hence treating all entity types equally). The micro-averaged F-1 score aggregates the contributions of entities from all classes to compute the average (treating all entities equally). We use the micro-averaged F-1 in our study since this accounts for label imbalances in the evaluation data and therefore a more meaningful statistic.
We use the micro-averaged F-1 in our study since this accounts for label imbalances in the evaluation data and therefore a more meaningful statistic.
Answer: Since this accounts for label imbalances in the evaluation data and therefore a more meaningful statistic.