DataHammer/scimrc huggingface dataset:
Question: What the the effects of houseplants?
Evidence: Houseplants do not have an appreciable effect on the concentration of volatile organic compounds (VOCs) in normal home environments: it would require between 10 and 1000 houseplants per square meter to achieve the same level of VOC removal as occurs from passive exchange between indoor and outdoor air. 

The idea that houseplants have an appreciable effect on indoor air quality is largely based on the Clean Air Study conducted by NASA in the 1980s. The study tested plants in sealed chambers and with highly elevated levels of volatile organic compounds (specifically benzene, trichloroethylene and formaldehyde). It concluded that to the extent that houseplants improve air quality, the mechanism of action is microorganisms within the potting soil, rather than the plants themselves.

Houseplants do have a statistically significant effect on the concentrations of both carbon monoxide and carbon dioxide in normal indoor environments, reducing carbon dioxide levels by 10-25% and carbon monoxide levels by up to 90%.  The effect has been investigated by NASA for use in spacecraft.

Plants also appear to increase the number and diversity of bacteria in homes, while reducing fungal diversity. They tend to increase humidity, reduce temperature swings, and reduce noise.

There are also many claimed psychological and physiological benefits to having houseplants. A critical review of the experimental literature concluded "The reviewed studies suggest that indoor plants can provide psychological benefits such as stress-reduction and increased pain tolerance. However, they also showed substantial heterogeneity in methods and results. We therefore have strong reservations about general claims that indoor plants cause beneficial psychological changes. It appears that benefits are contingent on features of the context in which the indoor plants are encountered and on characteristics of the people encountering them."
Answer: There are also many claimed psychological and physiological benefits to having houseplants. Houseplants do have a statistically significant effect on the concentrations of both carbon monoxide and carbon dioxide in normal indoor environments. 
Plants also appear to increase the number and diversity of bacteria in homes, while reducing fungal diversity.They tend to increase humidity, reduce temperature swings, and reduce noise. There are also many claimed psychological and physiological benefits to having houseplants. Indoor plants can provide psychological benefits such as stress-reduction and increased pain tolerance.
Question: Name some books by Thomas Pynchon
Answer: The most popular books by Thomas Pynchon include V., Gravity's Rainbow, The Crying of Lot 49 or Inherent Vice.
Question: How was performance of previously proposed rules at very large scale?
Evidence: Basic Features. We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.
We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.
Answer: The answers are shown as follows:
*  close to random,
Question: Which are the main reasons for doing a PhD?
Answer: After completing the Master's degree, some students are really interested in contributing to specific field of Science. Curiosity, challenges, and the feeling that they are able to contribute their ideas and knowledge to a much broader ecosystem, are the main reasons that motivate students towards the PhD journey.
Question: What method do the authors propose?
Evidence: In the past few years, there has been much interest in applying neural network based deep learning techniques to solve all kinds of natural language processing (NLP) tasks. From low level tasks such as language modeling, POS tagging, named entity recognition, and semantic role labeling BIBREF0 , BIBREF1 , to high level tasks such as machine translation, information retrieval, semantic analysis BIBREF2 , BIBREF3 , BIBREF4 and sentence relation modeling tasks such as paraphrase identification and question answering BIBREF5 , BIBREF6 , BIBREF7 . Deep representation learning has demonstrated its importance for these tasks. All the tasks get performance improvement via learning either word level representations or sentence level representations.
In this work, we brought deep representation learning technologies to the clinical domain. Specifically, we focus on clinical information extraction, using clinical notes and pathology reports from the Mayo Clinic. Our system will identify event expressions consisting of the following components:
The input of our system consists of raw clinical notes or pathology reports like below:
And output annotations over the text that capture the key information such as event mentions and attributes. Table TABREF7 illustrates the output of clinical information extraction in details.
To solve this task, the major challenge is how to precisely identify the spans (character offsets) of the event expressions from raw clinical notes. Traditional machine learning approaches usually build a supervised classifier with features generated by the Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) . For example, BluLab system BIBREF8 extracted morphological(lemma), lexical(token), and syntactic(part-of-speech) features encoded from cTAKES. Although using the domain specific information extraction tools can improve the performance, learning how to use it well for clinical domain feature engineering is still very time-consuming. In short, a simple and effective method that only leverage basic NLP modules and achieves high extraction performance is desired to save costs.
To address this challenge, we propose a deep neural networks based method, especially convolution neural network BIBREF0 , to learn hidden feature representations directly from raw clinical notes. More specifically, one method first extract a window of surrounding words for the candidate word. Then, we attach each word with their part-of-speech tag and shape information as extra features. Then our system deploys a temporal convolution neural network to learn hidden feature representations. Finally, our system uses Multilayer Perceptron (MLP) to predict event spans. Note that we use the same model to predict event attributes.
To address this challenge, we propose a deep neural networks based method, especially convolution neural network, to learn hidden feature representations directly from raw clinical notes.
Answer: They propose a deep neural network based method, especially convolution neural network, to learn hidden feature representations directly from raw clinical notes.
Question: Give me some cuisines I should try
Answer: Here are some cuisines you should try - Indian, Mediterranean, Italian, French and Chinese
Question: Identify which instrument is string or percussion: Bak, Fiddle
Answer: Bak is percussion, Fiddle is string.
Question: Classify the following literals as strings or ints: "a", "", "0", 0, 0xasdf, "0xasdf"
Answer: In many programming languages, string literals are surrounded by double quotes. That means that "a", "", "0", and "0xasdf" are strings, while 0 and 0xasdf are int literals.
Question: What is the function of the error rate?
Evidence: The dataset statistics can be found in Table TABREF7. The number of examples we collected increases per round, starting with approximately 19k examples for Round 1, to around 47k examples for Round 2, to over 103k examples for Round 3. We collected more data for later rounds not only because that data is likely to be more interesting, but also simply because the base model is better and so annotation took longer to collect good, verified correct examples of model vulnerabilities.
For each round, we report the model error rate, both on verified and unverified examples. The unverified model error rate captures the percentage of examples where the model disagreed with the writer's target label, but where we are not (yet) sure if the example is correct. The verified model error rate is the percentage of model errors from example pairs that other annotators were able to confirm the correct label for. Note that this error rate represents a straightforward way to evaluate model quality: the lower the model error rate—assuming constant annotator quality and context-difficulty—the better the model.
We observe that model error rates decrease as we progress through rounds. In Round 3, where we included a more diverse range of contexts from various domains, the overall error rate went slightly up compared to the preceding round, but for Wikipedia contexts the error rate decreased substantially. While for the first round roughly 1 in every 5 examples were verified model errors, this quickly dropped over consecutive rounds, and the overall model error rate is less than 1 in 10. On the one hand, this is impressive, and shows how far we have come with just three rounds. On the other hand, it shows that we still have a long way to go if even untrained annotators can fool ensembles of state-of-the-art models with relative ease.
Table TABREF7 also reports the average number of “tries”, i.e., attempts made for each context until a model error was found (or the number of possible tries is exceeded), and the average time this took (in seconds). Again, these metrics represent a useful way to evaluate model quality. We observe that the average tries and average time per verified error both go up as we progress through the rounds. The numbers clearly demonstrate that the rounds are getting increasingly more difficult.
Note that error rate is a useful way to evaluate model quality: the lower the model error rate—assuming constant annotator quality and context-difficulty—the better the model.
Answer: The error rate is a useful way to evaluate model quality: the lower the model error rate—assuming constant annotator quality and context-difficulty—the better the model.
Question: Should high school students be allowed to drink coffee?
Answer: Coffee contains caffeine that helps boost energy levels in human bodies and there is no clear evidence showing that drinking moderate amounts of coffee would harm human productivity or health. For high school students to not doze off at classes, it could be a good idea to permit a limited amount of coffee intake.
Question: How many Silver Star awards did James Frederick Moriarty receive?
Evidence: James Frederick Moriarty (May 8, 1896 – January 30, 1981) was a highly decorated officer in the United States Marine Corps with the rank of brigadier general. A veteran of World War I, he distinguished himself several times as a company officer with the 6th Machine Gun Battalion and received four awards of the Silver Star.
Answer: James Frederick Moriarty received four awards of the Silver Star
Question: What is the plot of the movie above suspicion
Evidence: Above Suspicion is a 2019 American crime thriller film directed by Phillip Noyce, from a screenplay by Chris Gerolmo, based upon Joe Sharkey's non-fiction book of the same name revolving around the murder of Susan Smith. It stars Emilia Clarke, Jack Huston, Sophie Lowe, Austin Hébert, Karl Glusman, Chris Mulkey, Omar Miller, Kevin Dunn, Thora Birch and Johnny Knoxville.

It was released in the United States on May 7, 2021, by Lionsgate.

Plot
An ambitious rookie agent is assigned to an FBI field office in the run-down town of Pikeville, Kentucky. The married man recruits a local drug-addicted woman as his informant and gets involved with her, thereby setting in motion a downward spiral of jealousy, betrayal and violence.
Answer: Above Suspicion is a 2019 American crime thriller film directed by Phillip Noyce, from a screenplay by Chris Gerolmo, based upon Joe Sharkey's non-fiction book of the same name revolving around the murder of Susan Smith. It stars Emilia Clarke, Jack Huston, Sophie Lowe, Austin Hébert, Karl Glusman, Chris Mulkey, Omar Miller, Kevin Dunn, Thora Birch and Johnny Knoxville.

It was released in the United States on May 7, 2021, by Lionsgate.

An ambitious rookie agent is assigned to an FBI field office in the run-down town of Pikeville, Kentucky. The married man recruits a local drug-addicted woman as his informant and gets involved with her, thereby setting in motion a downward spiral of jealousy, betrayal and violence.
Question: What is the Make Something Edmonton Initiative?
Evidence: Make Something Edmonton is a community building initiative that resulted from the creation of a task force on City image and reputation in July 2012 in Edmonton, Alberta. The task force is co-chaired by entrepreneur Chris LaBossiere and Rapid Fire Theatre artistic director Amy Shostak. The creative concept behind the initiative was articulated by local writer and entrepreneur Todd Babiak.

The central idea behind the initiative is that "Edmonton is an unusually good city to create something from nothing, to launch a new idea, to build, to get ‘er done, to make something."
Answer: The Make Something Edmonton is a community building initiative that resulted from the creation of a task force on City image and reputation in July 2012 in Edmonton, Alberta. The task force is co-chaired by entrepreneur Chris LaBossiere and Rapid Fire Theatre artistic director Amy Shostak. The creative concept behind the initiative was articulated by local writer and entrepreneur Todd Babiak.
Question: What is the best way to recover from a shoulder SLAP tear?
Answer: Non surgical interventions include physical therapy to strengthen the surrounding muscles and increase range of motion as well as steroid and PRP shots. It is recommended to try non surgical interventions first, and if the problem persists then meet with a surgeon to discuss alternatives.
Question: What could the n-ary format with several secondary arguments extracted from OpenIE facilitate the team in the research?
Evidence: We utilize an off-the-shelf toolbox of OpenIE to the derive structured answer-relevant relations from sentences as to the point contexts. Relations extracted by OpenIE can be represented either in a triple format or in an n-ary format with several secondary arguments, and we employ the latter to keep the extractions as informative as possible and avoid extracting too many similar relations in different granularities from one sentence. We join all arguments in the extracted n-ary relation into a sequence as our to the point context. Figure FIGREF5 shows n-ary relations extracted from OpenIE. As we can see, OpenIE extracts multiple relations for complex sentences. Here we select the most informative relation according to three criteria in the order of descending importance: (1) having the maximal number of overlapped tokens between the answer and the relation; (2) being assigned the highest confidence score by OpenIE; (3) containing maximum non-stop words. As shown in Figure FIGREF5, our criteria can select answer-relevant relations (waved in Figure FIGREF5), which is especially useful for sentences with extraneous information. In rare cases, OpenIE cannot extract any relation, we treat the sentence itself as the to the point context.
Table TABREF8 shows some statistics to verify the intuition that the extracted relations can serve as more to the point context. We find that the tokens in relations are 61% more likely to be used in the target question than the tokens in sentences, and thus they are more to the point. On the other hand, on average the sentences contain one more question token than the relations (1.86 v.s. 2.87). Therefore, it is still necessary to take the original sentence into account to generate a more accurate question.
Relations extracted by OpenIE can be represented either in a triple format or in an n-ary format with several secondary arguments, and we employ the latter to keep the extractions as informative as possible and avoid extracting too many similar relations in different granularities from one sentence.
Answer: It can keep the extractions as informative as possible and avoid extracting too many similar relations in different granularities from one sentence.
Question: Classify the eight planets of Earth's solar system as rocky or gaseous, according to their primary composition.
Answer: Mercury - rocky
Veuns - rocky
Earth - rocky
Mars - rocky
Jupiter - gaseous
Saturn - gaseous
Uranus - gaseous
Neptune - gaseous
Question: What are the 7 continents in the world
Answer: Asia
Africa
Antarctica
Europe
North America
South America
Australia
Question: Write a tribute to my high school swim coach.
Answer: I’ve known Coach for most of my life, growing up in town and spending time with her family as a kid. But it wasn’t until I went to high school and joined the swim team that Coach's role in my life grew and she became more than a parent or even a coach, but an advocate and role model.

Coach left a lasting, positive impact on so many of us. Pushing us not only to be better swimmers, but to be humble leaders and stronger, more confident adults. 

Even though I would never be a star swimmer or D1 athlete, I never felt discouraged by Coach or anyone on her coaching staff. Quite the opposite: I threw myself into swimming. We all pushed ourselves to be the best we could be, we cheered as loud as we could on the sidelines, and no one passed up an opportunity to support the team or attend a team outing. Because Coach didn’t just build teams of champion swimmers, she built a community. I’m still close to many of my teammates — some remain my closest friends. That’s because swimming is more than a sport at our school, it’s a family. And Coach is and (in my mind) always will be the matriarch.
Question: From the passage provided, extract the official language of Tamil Nadu.
Evidence: Tamil Nadu (/ˌtæmɪl ˈnɑːduː/; Tamil: [ˈtamiɻ ˈnaːɽɯ] (listen), abbr. TN) is the southern-most state of India. The tenth largest Indian state by area and the sixth largest by population, Tamil Nadu is the home of the Tamil people, whose Tamil language—one of the longest surviving classical languages in the world—is widely spoken in the state and serves as its official language. The capital and largest city is Chennai.
Answer: Tamil is the official language of Tamil Nadu.
Question: Identify which instrument is string or percussion: Xylorimba, Cimbalom
Answer: Cimbalom is string, Xylorimba is percussion.
Question: What is forward slip in aviation?
Answer: It is a technique you use to lose altitude quickly without gaining airspeed.  It is not efficient aerodynamically as it causes significant drag.  The plane will appear as if it is flying sideways.  A forward slip is executed by applying aileron input with opposite rudder.  Typically full opposite rudder is needed to compensate for the aileron input.
Question: Who invented punched cards used in early computing 1880s
Answer: Herman Hollerith
Question: What does this interdisciplinary project collect and analyse?
Evidence: Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.
This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences.
Answer: This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences.
Question: What are some plants you can put in your outdoor garden to keep the bugs away?
Answer: Some plants known to be bug deterrents are as follows: thyme, lavender, allium, petunia, basil, marigold, lemongrass, chrysanthemum, and mosquito plant.
Question: Name some fruits that have red skin.
Answer: Strawberries, Cherries, Dragonfruit and Apples all have red skin.