DataHammer/scimrc huggingface dataset:
Question: What is a bank run?
Answer: A bank run is a sudden and drastic event that occurs when all the depositors in a bank rush to withdraw their deposits at the same time or within a very short timeframe. As more depositors withdraw their money, the rest of the depositor's fears of a bank's insolvency increase, therefore driving more depositors to withdraw their money. The only thing that can stop a bank run is everyone stopping from withdrawing their money, however this is a good example of game theory since the benefits of stopping individual's withdrawals benefits the whole system but is against the individual's benefit while withdrawing the individual's money benefits the individual but affects the whole system. Banks typically don't have the entirety of their depositors' money available for withdrawal. During a bank run they have to increase their cash positions, sometimes selling assets at a loss, increasing the likelihood of the bank's collapse.
Question: How is presence of three target styles detected?
Evidence: We randomly sampled 50 news abstracts from the test set and asked three native-speaker annotators for evaluation to score the generated headlines. Specifically, we conduct two tasks to evaluate on four criteria: (1) relevance, (2) attractiveness, (3) language fluency, and (4) style strength. For the first task, the human raters are asked to evaluate these outputs on the first three aspects, relevance, attractiveness, and language fluency on a Likert scale from 1 to 10 (integer values). For relevance, human annotators are asked to evaluate how semantically relevant the headline is to the news body. For attractiveness, annotators are asked how attractive the headlines are. For fluency, we ask the annotators to evaluate how fluent and readable the text is. After the collection of human evaluation results, we averaged the scores as the final score. In addition, we have another independent human evaluation task about the style strength – we present the generated headlines from TitleStylist and baselines to the human judges and let them choose the one that most conforms to the target style such as humor. Then we define the style strength score as the proportion of choices.
In addition, we have another independent human evaluation task about the style strength – we present the generated headlines from TitleStylist and baselines to the human judges and let them choose the one that most conforms to the target style such as humor. Then we define the style strength score as the proportion of choices.
Answer: The answers are shown as follows:
* human evaluation task about the style strength
Question: Classify each of the following animals as exoskeleton or endoskeleton: grasshopper, fish, spider, scorpion, ant, monkey
Answer: Grasshopper: Exoskeleton
Fish: Endoskeleton
Spider: Exoskeleton
Scorpion: Exoskeleton
Ant: Exoskeleton
Monkey: Endoskeleton
Question: what is the source of the song lyrics?
Evidence: In order to obtain a large number of Brazilian music lyrics, we created a crawler to navigate into the Vagalume website, extracting, for each musical genre, all the songs by all the listed authors. The implementation of a crawler was necessary because, although the Vagalume site provides an API, it is only for consultation and does not allow obtaining large amounts of data. The crawler was implemented using Scrapy, an open-source and collaborative Python library to extract data from websites.
In order to obtain a large number of Brazilian music lyrics, we created a crawler to navigate into the Vagalume website, extracting, for each musical genre, all the songs by all the listed authors.
Answer: The answers are shown as follows:
* Vagalume website
Question: What is the general framework for data exploration by semantic queries?
Evidence: Given the theoretical results, here we design a general framework for scholarly data exploration by using semantic queries on knowledge graph embedding space. Figure FIGREF19 shows the architecture of the proposed framework. There are three main components, namely data processing, task processing, and query processing.
FLOAT SELECTED: Fig. 1. Architecture of the semantic query framework. Eclipse denotes operation, parallelogram denotes resulting data.
Figure FIGREF19 shows the architecture of the proposed framework. There are three main components, namely data processing, task processing, and query processing.
FLOAT SELECTED: Fig. 1. Architecture of the semantic query framework. Eclipse denotes operation, parallelogram denotes resulting data.
Answer: The answers are shown as follows:
* three main components, namely data processing, task processing, and query processing
Question: Why is the sky blue?
Answer: Earth's atmosphere scatters short-wavelength light more efficiently than that of longer wavelengths. Because its wavelengths are shorter, blue light is more strongly scattered than the longer-wavelength lights, red or green. Hence, the result is that when looking at the sky away from the direct incident sunlight, the human eye perceives the sky to be blue.
Question: How accurate is the aspect based sentiment classifier trained only using the XR loss?
Evidence: FLOAT SELECTED: Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p < 0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from (He et al., 2018a). Numbers for Semisupervised are from (He et al., 2018b).
FLOAT SELECTED: Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p < 0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from (He et al., 2018a). Numbers for Semisupervised are from (He et al., 2018b).
Answer: BiLSTM-XR-Dev Estimation accuracy is 83.31 for SemEval-15 and 87.68 for SemEval-16.
BiLSTM-XR accuracy is 83.31 for SemEval-15 and 88.12 for SemEval-16.
Question: Why do cats make purring sounds?
Answer: Cats make purring sounds as a way to communicate emotions which may include relaxation, happiness, and sometimes even excitement. However, the exact reason why cats make the purring sounds has not been fully understood.
Question: Identify the bird from the list: Canada Dry, Canada goose, Goosebumps
Answer: Canada goose
Question: Give me a summary of how the citric acid cycle works
Evidence: The citric acid cycle (CAC)—also known as the Krebs cycle, Szent-Györgyi-Krebs cycle or the TCA cycle (tricarboxylic acid cycle)—is a series of chemical reactions to release stored energy through the oxidation of acetyl-CoA derived from carbohydrates, fats, and proteins. The Krebs cycle is used by organisms that respire (as opposed to organisms that ferment) to generate energy, either by anaerobic respiration or aerobic respiration. In addition, the cycle provides precursors of certain amino acids, as well as the reducing agent NADH, that are used in numerous other reactions. Its central importance to many biochemical pathways suggests that it was one of the earliest components of metabolism and may have originated abiogenically. Even though it is branded as a 'cycle', it is not necessary for metabolites to follow only one specific route; at least three alternative segments of the citric acid cycle have been recognized.

The name of this metabolic pathway is derived from the citric acid (a tricarboxylic acid, often called citrate, as the ionized form predominates at biological pH) that is consumed and then regenerated by this sequence of reactions to complete the cycle. The cycle consumes acetate (in the form of acetyl-CoA) and water, reduces NAD+ to NADH, releasing carbon dioxide. The NADH generated by the citric acid cycle is fed into the oxidative phosphorylation (electron transport) pathway. The net result of these two closely linked pathways is the oxidation of nutrients to produce usable chemical energy in the form of ATP.

In eukaryotic cells, the citric acid cycle occurs in the matrix of the mitochondrion. In prokaryotic cells, such as bacteria, which lack mitochondria, the citric acid cycle reaction sequence is performed in the cytosol with the proton gradient for ATP production being across the cell's surface (plasma membrane) rather than the inner membrane of the mitochondrion.

For each pyruvate molecule (from glycolysis), the overall yield of energy-containing compounds from the citric acid cycle is three NADH, one FADH2, and one GTP.
Answer: The citric acid cycle is used by organisms to generate energy. This is achieved through a series of chemical reactions that occur in mitochondria to release stored energy through the oxidation of acetyl-CoA derived from fats, proteins, and carbohydrates.
Question: Did they build the linguistic and the temporal models using the same framework?
Evidence: Finally, we created a combined temporal-linguistic model. Since both the linguistic and the temporal models were built using the same framework, it was fairly simple to combine the two models. The combined model was created by merging the linguistic and temporal corpora and vocabularies. (Recall that we treated temporal features as words). We then experimented with the same four methods as in the last two sections to create our combined models.
Table 4 shows the performance of each of these models. Across the board, the combined models outperformed their corresponding linguistic and temporal models, though the difference with the linguistic models were not as great. These results suggest that at some level the temporal and the linguistic "styles" of users provide non-overlapping cues about the identity of said users. Also, note that as with the linguistic and temporal models, our combined confusion model outperformed the other combined models.
Another way to evaluate the performance of the different combined models is through the rank-statistics plot. This is shown in Figure 2 . The figure shows the distribution of the ranks of the $5,612$ users for different combined models. The x-axis is the rank percentile (divided into bins of $5\%$ ), the y-axis is the percentage of the users that fall in each bin. For example, for the confusion model, $69\%$ (3880) of the $5,612$ users were correctly linked between Twitter and Facebook when looking at the top $5\%$ (281) of the predictions by the model. From the figure, you can clearly see that the confusion model is superior to the other models, with TF-IDF a close second. You can also see from the figure that the rank plot for the random baseline is a horizontal line, with each rank percentile bin having $5\%$ of the users ( $5\%$ because the rank percentiles were divided into bins of $5\%$ ).
Since both the linguistic and the temporal models were built using the same framework, it was fairly simple to combine the two models.
Answer: Yes.
Question: Which is a species of fish? Tailor or Sailor
Answer: Tailor
Question: Which TV series was narrated by Walter Winchell
Answer: The Untouchables
Question: Who are Industrial Light and Magic?
Evidence: Industrial Light & Magic (ILM) is an American motion picture visual effects company that was founded on May 26, 1975 by George Lucas. It is a division of the film production company Lucasfilm, which Lucas founded, and was created when he began production on the original Star Wars, now the fourth episode of the Skywalker Saga.

ILM originated in Van Nuys, California, then later moved to San Rafael in 1978, and since 2005 it has been based at the Letterman Digital Arts Center in the Presidio of San Francisco. In 2012, The Walt Disney Company acquired ILM as part of its purchase of Lucasfilm.
Answer: Industrial Light and Magic are one of the biggest and most innovative VFX (Visual Effects) and CGI (Computer generated imagery) firms. The company was started by George Lucas.and was acquired by Disney. They have contributed Visual special effects for over 300 films including the Star Wars series, Indiana Jones series and the Jurassic Park series.
Question: What are the words of House Mooton?
Answer: "Wisdom and Strength"
Question: What kind of espresso machine is best for home use?
Answer: Home espresso machines are a growing segment of the small kitchen appliance market. A good home espresso machine provides the ability to make high quality espresso in a machine that is typically smaller and less expensive than their commercial counterparts. 

There are three common espresso machine designs for home use: manual, semi-automatic, and fully automatic. As the name implies -- manual machines require direct "manual" control by the user, who typically applies pressure through a lever to deliver the desired brew pressure. Semi-automatic machines require the user to prepare the coffee into the portafilter, but then rely on an automated pump and to deliver the desired brew pressure for espresso. Fully automatic machines automate the entire process end-to-end: grinding the beans, loading the ground beans into the portafilter, and then pumping water through the portafilter at predefined pressure levels to brew espresso. 

Manual machines typically have a lower cost, smaller footprint, and provide the user with more direct control over the brewing process. This allows skilled users to make higher-quality espresso than they would with a more automated machine. In contrast, fully automatic machines are often the easiest to use, but are unable to make espresso as consistently great as a skilled barista could with a manual machines. Fully automatic machines are inherently more complex, more expensive to purchase, and more likely to have longer-term reliability issues. 

Regardless of the type of espresso machine, two factors influence how well they will perform: their ability to manage brew temperature and brew pressure. The best machines provide the ability to vary brew temperature and pressure in consistent, repeatable ways.The type of coffee bean and desired result will impact what temperature and pressure profile the user wants. The best machines offer flexibility along both dimensions in consistent, repeatable ways.
Question: How big is seed lexicon used for training?
Evidence: We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16.
We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. 
Answer: 30 words
Question: What is Freeride snowboarding?
Answer: Freestyle snowboarding is any riding that includes performing tricks. In freestyle, the rider utilizes natural and man-made features such as rails, jumps, boxes, and innumerable others to perform tricks. It is a popular all-inclusive concept that distinguishes the creative aspects of snowboarding, in contrast to a style like alpine snowboarding.
Question: Are these baseball teams or football teams: Arizona Diamondbacks, Seattle Mariners, Pittsburg Pirates, and Houston Astros?
Answer: They are baseball teams.
Question: What are the series of simple models?
Evidence: Besides the Baseline and Oracle, where only ASR 1-best hypothesis is considered, we also perform experiments to utilize ASR $n$-best hypotheses during evaluation. The models evaluating with $n$-bests and a BM (pre-trained on transcription) are called Direct Models (in Fig. FIGREF7):
Besides the Baseline and Oracle, where only ASR 1-best hypothesis is considered, we also perform experiments to utilize ASR $n$-best hypotheses during evaluation.
Answer: The answers are shown as follows:
* perform experiments to utilize ASR $n$-best hypotheses during evaluation
Question: What is Cuneiform?
Answer: Cuneiform is a logo-syllabic script that was used to write several languages of the Ancient Middle East. The script was in active use from the early Bronze Age until the beginning of the Common Era. It is named for the characteristic wedge-shaped impressions (Latin: cuneus) which form its signs. Cuneiform was originally developed to write the Sumerian language of southern Mesopotamia (modern Iraq). Cuneiform is the earliest known writing system.
Question: what datasets were used?
Evidence: In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 .
In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise. The 20-Newsgroups consists of twenty thousand messages taken from twenty newsgroups, and is divided in six super-classes, which are, in turn, partitioned in several sub-classes. For this first set of experiments, only the four most populated super-classes were used: “computers", “science", “politics" and “recreative". The preprocessing of the documents consisted of stemming and stop-words removal. After that, 75% of the documents were randomly selected for training and the remaining 25% for testing.
In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 .
In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise. 
Answer: The answers are shown as follows:
* Reuters-21578 BIBREF30
*  LabelMe BIBREF31
* 20-Newsgroups benchmark corpus BIBREF29
Question: Who are the most influential Asian celebrities?
Answer: Michelle Yeoh, having won the Oscar for best actress in 2023 and making history as the first Asian lead actress winner. Bruce Lee and Jackie Chan are also iconic Asian celebrities who are very well-known for their martial arts and action films.
Question: Provide a table of the various recruiting lists that Calvin Johnson was named to and who named him to the list and do it in CSV format
Evidence: Johnson was rated among the nation's top 10 wide receivers and top 100 players by virtually every recruiting analyst. He was tabbed the No. 4 wide receiver and No. 15 player in the nation by TheInsiders.com, and named to the Super Southern 100 by the Atlanta Journal-Constitution, the Rivals 100 by Rivals.com, TheInsiders.com Hot 100, the SuperPrep All-America 275, and the Prep Star Top 100 Dream Team.

Johnson was also rated as the best player in Georgia, No. 12 in the Southeast and No. 37 in the nation by Rivals.com, the No. 7 wide receiver in the nation by SuperPrep, and first-team all-state selection (Class AAAA) by the Atlanta Journal-Constitution. Finally, he was tabbed to the AJC's preseason Super 11. By the time he was a junior, he was ranked as within the top 10 wide receivers and the top 100 players in the nation by almost every writer.
Answer: Sure, here is text in a CSV format of the various recruiting lists that Calvin Johnson was named to and who named him to the list:

List name,List owner
Super Southern 100,Atlanta Journal-Constitution
the Rivals 100,Rivals.com
TheInsiders.com Hot 100,TheInsiders.com
the SuperPrep All-America 275,SuperPrep
Prep Star Top 100 Dream Team,Prep Star
Question: Tell me about toys that can help children learn to write.
Answer: Toys that help children learn to write typically involve increasing their hand strength and gross motor skills: toys such as play-doh and blocks increase hand strength. Also, practicing writing letters in sand prepares them for the motion of writing.
Question: What are all the different names for COVID-19 and which one is the most recommended?
Evidence: During the initial outbreak in Wuhan, the virus and disease were commonly referred to as "coronavirus" and "Wuhan coronavirus", with the disease sometimes called "Wuhan pneumonia". In the past, many diseases have been named after geographical locations, such as the Spanish flu, Middle East respiratory syndrome, and Zika virus. In January 2020, the World Health Organization (WHO) recommended 2019-nCoV and 2019-nCoV acute respiratory disease as interim names for the virus and disease per 2015 guidance and international guidelines against using geographical locations or groups of people in disease and virus names to prevent social stigma. The official names COVID‑19 and SARS-CoV-2 were issued by the WHO on 11 February 2020 with COVID-19 being shorthand for "coronavirus disease 2019". The WHO additionally uses "the COVID‑19 virus" and "the virus responsible for COVID‑19" in public communications.
Answer: Here are a list of names for COVID-19 that were mentioned: coronavirus, Wuhan coronavirus, Wuhan pneumonia, 2019-nCoV, 2019-nCoV acute respiratory disease, COVID‑19, SARS-CoV-2, the COVID‑19 virus, the virus responsible for COVID‑19.

The official names COVID‑19 and SARS-CoV-2 were issued by the WHO on 11 February 2020, but they also use "the COVID‑19 virus" and "the virus responsible for COVID‑19" in public communications.

Based on this information, the most recommended name is COVID-19.
Question: How better are state-of-the-art results than this model? 
Evidence: Table TABREF32 depicts models producing 100 words summaries, all depending on hand-crafted features. We use as baselines FreqSum BIBREF22 ; TsSum BIBREF23 ; traditional graph-based approaches such as Cont. LexRank BIBREF9 ; Centroid BIBREF24 ; CLASSY04 BIBREF25 ; its improved version CLASSY11 BIBREF26 and the greedy model GreedyKL BIBREF27. All of these models are significantly underperforming compared to SemSentSum. In addition, we include state-of-the-art models : RegSum BIBREF0 and GCN+PADG BIBREF3. We outperform both in terms of ROUGE-1. For ROUGE-2 scores we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features and a much smaller and simpler model. Finally, RegSum achieves a similar ROUGE-2 score but computes sentence saliences based on word scores, incorporating a rich set of word-level and domain-specific features. Nonetheless, our model is competitive and does not depend on hand-crafted features due to its full data-driven nature and thus, it is not limited to a single domain.
In addition, we include state-of-the-art models : RegSum BIBREF0 and GCN+PADG BIBREF3. We outperform both in terms of ROUGE-1. For ROUGE-2 scores we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features and a much smaller and simpler model. Finally, RegSum achieves a similar ROUGE-2 score but computes sentence saliences based on word scores, incorporating a rich set of word-level and domain-specific features.
Answer: The answers are shown as follows:
* we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features
*  RegSum achieves a similar ROUGE-2 score
Question: Toronto Maple Leafs is a team in the NHL. Who was their captain and star player in the early 2000s?
Answer: Mats Sundin