DataHammer/scimrc huggingface dataset:
Question: How much renewable energy is produced and consumed in the United States?
Evidence: According to preliminary data from the US Energy Information Administration, renewable energy accounted for about 12.6% of total primary energy consumption and about 19.8% of the domestically produced electricity in the United States in 2020.
Answer: Since 2019, wind power has been the largest producer of renewable electricity in the country. Wind power generated 337.9 terawatt-hours of electricity in 2020, which accounted for 8.4% of the nation's total electricity generation and 43.2% of the total renewable electricity generation.  By October 2021, the United States nameplate generating capacity for wind power was 129,256 megawatts (MW).  Texas remained firmly established as the leader in wind power deployment, followed by Iowa and Oklahoma as of year end 2020.

Hydroelectric power is the second-largest producer of renewable electricity in the country, generating around 7.3% of the nation's total electricity in 2020 as well as 36.4% of the total renewable electricity generation.  The United States is the fourth largest producer of hydroelectricity in the world after China, Canada and Brazil.

Solar power provides a growing share of electricity in the country, with over 50 GW of installed capacity generating about 1.3% of the country's total electricity supply in 2017, up from 0.9% the previous year. As of 2016, more than 260,000 people worked in the solar industry and 43 states deployed net metering, where energy utilities bought back excess power generated by solar arrays.  Large photovoltaic power plants in the United States include Mount Signal Solar (600 MW) and Solar Star (579 MW). Since the United States pioneered solar thermal power technology in the 1980s with Solar One, several more such power stations have been built. The largest of these solar thermal power stations are the Ivanpah Solar Power Facility (392 MW), southwest of Las Vegas, and the SEGS group of plants in the Mojave Desert, with a total generating capacity of 354 MW.

Other renewable energy sources include geothermal, with The Geysers in Northern California the largest geothermal complex in the world.

The development of renewable energy and energy efficiency marked "a new era of energy exploration" in the United States, according to former President Barack Obama.  In a joint address to the Congress on February 24, 2009, President Obama called for doubling renewable energy within the following three years. Renewable energy reached a major milestone in the first quarter of 2011, when it contributed 11.7% of total national energy production (660 TWh), surpassing energy production from nuclear power (620 TWh) for the first time since 1997.  In his 2012 State of the Union address, President Barack Obama restated his commitment to renewable energy and mentioned the long-standing Interior Department commitment to permit 10,000 MW of renewable energy projects on public land in 2012.
Question: Is Blind a reliable source for information?
Answer: Blind is an app where the tech community can anonymously post opinions and polls.  Because of the anonymity of the users, it can be reliable because people can post opinions unfiltered without the fear of repercussions (although there is an anti bullying policy that is enforced by banning users if they violate the policy).  Ironically, the anonymity is also what can lead to unreliable information as many opinions are users trolling each other and giving exaggerated and false responses.
Question: When was the newest country created?
Answer: South Sudan became a country in July 2011.
Question: What is the data selection paper in machine translation
Evidence: We observe that merely adding more tasks cannot provide much improvement on the target task. Thus, we propose two MTL training algorithms to improve the performance. The first method simply adopts a sampling scheme, which randomly selects training data from the auxiliary tasks controlled by a ratio hyperparameter; The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 . It learns the sample weights from the auxiliary tasks automatically through language models. Prior to this work, many studies have used upstream datasets to augment the performance of MRC models, including word embedding BIBREF5 , language models (ELMo) BIBREF8 and machine translation BIBREF1 . These methods aim to obtain a robust semantic encoding of both passages and questions. Our MTL method is orthogonal to these methods: rather than enriching semantic embedding with external knowledge, we leverage existing MRC datasets across different domains, which help make the whole comprehension process more robust and universal. Our experiments show that MTL can bring further performance boost when combined with contextual representations from pre-trained language models, e.g., ELMo BIBREF8 .
We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 . We use $(Q^{k},P^{k},A^{k})$ to represent a data point from the $k$ -th task for $1\le k\le K$ , with $k=1$ being the target task. Since the passage styles are hard to evaluate, we only evaluate data points based on $Q^{k}$ and $A^k$ . Note that only data from auxiliary task ( $2\le k\le K$ ) is re-weighted; target task data always have weight 1.
The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 .
We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 .
Answer: The answers are shown as follows:
* BIBREF7
* BIBREF26
Question: why do human beings have a tendency to think they are always right?
Answer: As a first attempt to answer the question of why human beings have a tendency to think they are always right, we could point to cognitive biases, and in particular to one called "confirmation bias". This bias conditions human beings to use any new evidence to confirm their pre-existing beliefs, and to reject any new evidence that contradicts these beliefs. People may also experience the so-called illusory superiority, another bias that conditions them to overestimate their skills and make them believe they are superior to the rest. However these biases, as concrete as well-proven as they are, are only part of the explanation. The book "Being Wrong: Adventures in the Margin of Error" by Kathryn Schulz is an exploration on why humans have the tendency to disregard errors and think they are always right. Shulz describes how people start with the assumption that they are right in everything they believe, and when they realise they have made a mistake, instead of challenging this assumption, they actually feel relief and think: "I was right except for this particular error I was making, but now that I have realised it I can certainly say that I am 100% right in everything".
Question: which weighs more, a pound of bricks or a ton of feathers?
Answer: A ton is more than a pound, so a ton of feathers weighs more than a pound of bricks.
Question: Based on this paragraph about dogs, what is a companion dog?
Evidence: A companion dog is a dog that does not work, providing only companionship as a pet, rather than usefulness by doing specific tasks. Many of the toy dog breeds are used only for the pleasure of their company, not as workers. Any dog can be a companion dog, and many working types such as retrievers are enjoyed primarily for their friendly nature as a family pet, as are mixed breed dogs. The American Kennel Club also offers a Companion dog title for judged dog obedience competitions.
Answer: A companion dog is a dog that provides only companionship as a pet, rather than usefulness by doing specific tasks.
Question: What’s the difference between Delta Live Tables and Workflows?
Answer: These are some of the main differences between Delta Live Tables and Workflows:

- Workflows is complementary to Delta Live Tables
- Workflows is a control flow that allows end-to-end orchestration of tasks that might include one or more DLT Pipelines.
- A workflow consists of one or more tasks, which could be a Spark job, a DLT Pipeline, an ML job, some random python code, etc. 
- DLT Pipelines is a data flow. You declare your data transformations, and Live Tables takes care of actually running your pipeline for you.
Question: What are some of the highest quality, full flavored cigars I can buy in the united states?
Answer: Due to the US embargo banning importation of products from Cuba in the mid 20th century, the storied Cuban cigar has not been able to be legally purchased by US consumers.With that said, there are numerous highly regarded cigar makers in countries like Nicargua, the Dominican Republic, and Honduras whose cigars rival the best Cuban cigars, many of them full flavored. Here are a few options to consider:

Arturo Fuente Opus X - this is a highly complex, highly sought after, full-flavored cigar from the Dominican Republic that retails for ~$25 for a single, making it an expensive choice for a regular cigar smoker. With that said, it is near the top of the list of favorites for most cigar aficianados.

Padron Serie 1926 - this is a creamy, chocolatey cigar from Nicaragua that retails for around $17, making it a slightly more affordable option, though still a premium selection. These are produced in higher volume than the Opus, which makes them easier to fine.

Ashton VSG - this is a Dominican cigar, with VSG standing for "Virgin Sun Grown." The flavor profile is very unique due to the sun grown wrapper. These are about $13 for a single at retail depending on size.

There are dozens of reputable cigar makers making thousands of cigars, but those above are longstanding staples of many avid cigar smokers in the US.
Question: Name the spyware used by Israel to hack Whatsapp?
Answer: Pegasus
Question: What is their baseline model?
Evidence: We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. For McM, the choices of number of convolutional filters, number of hidden units in first dense layer, number of hidden units in second dense layer, and recurrent units for LSTM are made empirically. Rest of the hyperparameters were selected by performing grid search using $20\%$ stratified validation set from training set on McM$_\textsubscript {R}$. Available choices and final selected parameters are mentioned in Table TABREF18. These choices remained same for all experiments and the validation set was merged back into training set.
We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification.
Answer: The answers are shown as follows:
* the model proposed in BIBREF3
Question: Do they measure how well they perform on longer sequences specifically?
Evidence: We further explore the effect of INLINEFORM0 -gram length in our model (i.e., the filter size for the covolutional layers used by the attention parsing module). In Figure FIGREF39 we plot the AUC scores for link prediction on the Cora dataset against varying INLINEFORM1 -gram length. The performance peaked around length 20, then starts to drop, indicating a moderate attention span is more preferable. Similar results are observed on other datasets (results not shown). Experimental details on the ablation study can be found in the SM.
We further explore the effect of INLINEFORM0 -gram length in our model (i.e., the filter size for the covolutional layers used by the attention parsing module). In Figure FIGREF39 we plot the AUC scores for link prediction on the Cora dataset against varying INLINEFORM1 -gram length. 
Answer: Yes.
Question: how many speeches are in the dataset?
Evidence: The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive. Over the course of this period a variety of topics are discussed, with many debated throughout (such as nuclear disarmament). Although the linguistic style has changed over this period, the context of these speeches constrains the variability to the formal domain. Before training the model, the dataset is split into 283,593 paragraphs, cleaned by removing paragraph deliminators and other excess noise, and tokenized using the spaCy tokenizer BIBREF4 .
The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive.
Answer: The answers are shown as follows:
* 7,507
Question: classify each of the following as living or non-living: dog, table, cat, tree, pen, book.
Answer: living: dog, cat, tree
non-living: table, pen, book
Question: On which dataset(s) do they compute their word embeddings?
Evidence: For a fair comparison, we trained each model on the same corpus of 10 million sentences gathered from Wikipedia. We removed stopwords and words appearing fewer than 2,000 times (130 million tokens total) to reduce noise and uninformative words. Our word2vec and NNSE baselines were trained using the recommended hyperparameters from their original publications, and all optimizers were using using the default settings. Hyperparameters are always consistent across evaluations.
For a fair comparison, we trained each model on the same corpus of 10 million sentences gathered from Wikipedia.
Answer: The answers are shown as follows:
* 10 million sentences gathered from Wikipedia
Question: What is the method used by Hale et al.?
Evidence: This work is most closely related to the paper from which we get the ERP data: BIBREF0 . In that work, the authors relate the surprisal of a word, i.e. the (negative log) probability of the word appearing in its context, to each of the ERP signals we consider here. The authors do not directly train a model to predict ERPs. Instead, models of the probability distribution of each word in context are used to compute a surprisal for each word, which is input into a mixed effects regression along with word frequency, word length, word position in the sentence, and sentence position in the experiment. The effect of the surprisal is assessed using a likelihood-ratio test. In BIBREF7 , the authors take an approach similar to BIBREF0 . The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance" that counts the number of parser actions in the RNNG language model. The authors find that surprisal (as predicted by the RNNG) and distance are both significant factors in a mixed effects regression which predicts the P600, while the surprisal as computed by an LSTM is not. Unlike BIBREF0 and BIBREF7 , we do not use a linking function (e.g. surprisal) to relate a language model to ERPs. We thus lose the interpretability provided by the linking function, but we are able to predict a significant proportion of the variance for all of the ERP components, where prior work could not. We interpret our results through characterization of the ERPs in terms of how they relate to each other and to eye-tracking data rather than through a linking function. The authors in BIBREF8 also use a recurrent neural network to predict neural activity directly. In that work the authors predict magnetoencephalography (MEG) activity, a close cousin to EEG, recorded while participants read a chapter of Harry Potter and the Sorcerer’s Stone BIBREF9 . Their approach to characterization of processing at each MEG sensor location is to determine whether it is best predicted by the context vector of the recurrent network (prior to the current word being processed), the embedding of the current word, or the probability of the current word given the context. In future work we also intend to add these types of studies to the ERP predictions.
In Hale et al. (2018), the authors take an approach similar to Frank et al. (2015). The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance” that counts the number of parser actions in the RNNG language model.
Answer: The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance” that counts the number of parser actions in the RNNG language model.
Question: Which characters belong to DC or Marvel Universe? She-Hulk, Wonder Woman
Answer: She-Hulk is Marvel, Wonder Woman is DC
Question: Extract the years that Ayrton Senna da Silva won the Formula One World Championship, and put them separated by comma.
Evidence: Ayrton Senna da Silva (21 March 1960 – 1 May 1994) was a Brazilian racing driver who won the Formula One World Drivers' Championship in 1988, 1990, and 1991. Senna is one of three Formula One drivers from Brazil to win the World Championship and won 41 Grands Prix and 65 pole positions, with the latter being the record until 2006.
Answer: 1988, 1990, 1991
Question: What is the area of Texas in square feet?
Answer: 7.5 Trillion
Question: Write a few sentences explaining the criteria you used to choose your favorite fast food restaurant.
Answer: The most important piece of criteria I use is taste of the food and drinks. Although there are other important factors like price, and variety of menu options ultimately the tastiness of the food is the most important factor.
Question: Is there any example where geometric property is visible for context similarity between words?
Evidence: The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. However considering the vocabulary was reduced to $V^* = 52$ words, those results lacked of consistency. Therefore for both languages we decided to re-train the RNNs using the same architecture, but with a larger vocabulary of the $V=300$ most relevant words (still in the RF sense) and on all the available data (i.e. everything is used as training) to compensate for the increased size of the vocabulary. We then calculated the distance of a few prominent words to the others. The analysis of the average cosine distance over $B=10$ runs for three major words is given by tables TABREF38 and TABREF39, and three other examples are given in the appendix tables TABREF57 and TABREF58. The first row corresponds to the reference word vector $\overrightarrow{w_1}$ used to calculate the distance from (thus the distance is always zero), while the following ones are the 9 closest to it. The two last rows correspond to words we deemed important to check the distance with (an antagonistic one or relevant one not in the top 9 for instance).
The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones. 
Answer: Yes.
Question: In what city was the first season of MTV's The Real World filmed?
Answer: The first season of MTV's The Real World was filmed in New York.
Question: Identify which animal species is alive or extinct: Megalania, Sea Turtle
Answer: Sea Turtle is alive, Megalania is extinct.
Question: Is the harmonic distribution a discrete distribution?
Evidence: In probability theory and statistics, the harmonic distribution is a continuous probability distribution. It was discovered by Étienne Halphen, who had become interested in the statistical modeling of natural events. His practical experience in data analysis motivated him to pioneer a new system of distributions that provided sufficient flexibility to fit a large variety of data sets. Halphen restricted his search to distributions whose parameters could be estimated using simple statistical approaches.
Answer: No, it is a continuous distribution.
Question: Where is the Bulgan Volcanic Field located?
Evidence: Khanuy-Gol (also known as Bulgan Volcanic Field, Chanuj Gol Hanui Gol or Hanuy Gol) is a volcanic field in Mongolia. Khanuy-Gol is located in the northern Hangai range, north of the settlement of Bulgan.

Topographic map
It covers a surface area of 3,500 square kilometres (1,400 sq mi) 150 kilometres (93 mi) southwest of Ulanbator. Among the 10 cones with heights of 30–190 metres (98–623 ft) that make up the field are Baga Togo Uul/Bogo-Togo-Ula (meaning "Little Togo Mountain", 28 metres (92 ft) high, 48°55.79′N 102°46.22′E to 48°55.34′N 102°45.75′E), Ikh Togo Uul/Ikha-Togo-Ula (meaning "Great Togo mountain", 219 metres (719 ft) high, 48°59.53′N 102°44.48′E), Togo, Urun Dush, and Uran Uul. This cone is the highest of the field, with a height of 280 metres (920 ft). Baga Togo Uul has one maar-like vent among its three vents, these vents are named Javalach, Togo and Tulga. Both Togo cones have a vegetation cover.
Answer: The Bulgan Volcanic Field is located in Mongolia, in the northern Hangai range.
Question: Where was the first publicly funded civil park?
Answer: The first publicly funded civil park is generally accepted to be Birkenhead Park, in England, United Kingdom.
Question: What did the authors select to explore robustness under domain transfer for round 3?
Evidence: For the third round, we selected a more diverse set of contexts, in order to explore robustness under domain transfer. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. We trained an even stronger RoBERTa model by adding the training set from the second round (A2) to the training data.
For the third round, we selected a more diverse set of contexts, in order to explore robustness under domain transfer.
Answer: A more diverse set of contexts.
Question: Tell me whether these are animals or apache projects or both:
Kudu, Impala, Alligator, Ant, Camel, Flink, Spark, Flume, Tomcat, Dog, Elephant
Answer: Animals: Alligator, Dog, Elephant
Apache Projects: Flink, Spark, Flume
Both animals and Apache projects: Kudu, Impala, Ant, Camel, Tomcat
Question: Given the current banking crisis we are experiencing, is this a redux of the 2008 crisis?
Answer: While there are some similarities between 2023 and 2008, I do not believe this is a similar crisis.  The biggest difference is that in 2008, nearly every bank had widespread exposure to mortgage backed securities.  In the 2023 crisis, it is only a few banks that had overexposure to treasuries without any hedge against rising interest rates.   Additionally, in 2008, while two different banks collapsed, there was much more widespread fear amongst the banks that caused a credit crunch.  Banks were unwilling to lend each other money for fear of liquidity problems, which in turned led to the credit markets freezing and causing secondary problems of companies being unable to attain lines of credit to meet payroll.   In 2023, there was also a failure of 2 banks, but the failure stemmed from a traditional bank run where depositors, en mass, were attempting to withdraw their funds at the same time, causing a liquidity problem with the affected banks.