DataHammer/scimrc huggingface dataset:
Question: Based on the provided paragraph give a bullet list of the names of the countries (regions) which took part in WWI and their reasons?
Evidence: World War I or the First World War (28 July 1914 – 11 November 1918), often abbreviated as WWI, was one of the deadliest global conflicts in history. The first decade of the 20th century saw increasing diplomatic tension between the European great powers. This reached breaking point on 28 June 1914, when a Bosnian Serb named Gavrilo Princip assassinated Archduke Franz Ferdinand, heir to the Austro-Hungarian throne. Austria-Hungary held Serbia responsible, and declared war on 28 July. Russia came to Serbia's defense, and by 4 August, defensive alliances had drawn in Germany, France, and Britain. German strategy in 1914 was to first defeat France, then attack Russia. However, this failed, and by the end of 1914, the Western Front consisted of a continuous line of trenches stretching from the English Channel to Switzerland. The Eastern Front was more fluid, but neither side could gain a decisive advantage, despite a series of costly offensives. Attempts by both sides to bypass the stalemate caused fighting to expand into the Middle East, the Alps, the Balkans, and overseas colonies, bringing Bulgaria, Romania, Greece, and others into the war.
Answer: The countries and regions that took part in World War I (WWI) and their reasons are as follows:

- Austria-Hungary: because their heir to the Austro-Hungarian throne, Archduke Franz Ferdinand, was assassinated by a Bosnian Serb named Gavrilo Princip.
 
- Serbia: because Austria-Hungary declared war against them.

- Russia: because they wanted to defend Serbia.

- Germany, France, and Britain: because they were allies with the countries already engaging in the war.

- Middle East, the Alps, the Balkans, and overseas colonies, bringing Bulgaria, Romania, Greece, and others: because the Eastern Front of the war was more fluid, but neither side could gain a decisive advantage. Both sides had attempts to overcome the stalemate leading to the war expanding into these regions and countries.
Question: Identify which systems in this list are part of the Hadoop ecosystem : MapReduce, MySQL, Google apps, Hive, Pig
Answer: MapReduce, Hive and Pig are part of Hadoop ecosystem. Google apps and MySQL are other projects that don't belong to Hadoop ecosystem.
Question: how to find a good date?
Answer: According to Pew Research, 70% of people say their dating life isn’t going well. The three top reasons provided that dating doesn’t work out include:
1)    People live too far away from each other
2)    Significant debt or poor financial situation
3)    different political views
A lot of people nowadays met through online dating apps so they can connect to potential dates they might not be able to met in personal life.  Some prefer someone who complements him while others like dates share similarities. For people looking for fast-paced effective dating experiences, you can try 8-minute first date meeting where you chat with people within eight minutes and decide whether or not they should go out on another date. Experts claims that successful relationships are those where a partner validates/affirms you, inspires you, and challenges you in a good way!
Question: Classify each of the following as a desert or mountain range: Alps, Sahara, Rockies, Appalachian, Arabian, Gobi
Answer: Alps: Mountain Range
Sahara: Desert
Rockies: Mountain Range
Appalachian: Mountain Range
Arabian: Desert
Gobi: Desert
Question: Which countries and languages do the political speeches and manifestos come from?
Evidence: Ideally one would choose for each topic a sample of reports from the entire political spectrum in order to form an unbiased opinion. But ordering media content with respect to the political spectrum at scale requires automated prediction of political bias. The aim of this study is to provide empirical evidence indicating that leveraging open data sources of german texts, automated political bias prediction is possible with above chance accuracy. These experimental results confirm and extend previous findings BIBREF0 , BIBREF1 ; a novel contribution of this work is a proof of concept which applies this technology to sort news article recommendations according to their political bias.
The aim of this study is to provide empirical evidence indicating that leveraging open data sources of german texts, automated political bias prediction is possible with above chance accuracy.
Answer: The answers are shown as follows:
* german
Question: How do they solve the problem of recoverable span in translating the English and Chinese datasets into more languages, with Google Translate?
Evidence: We have training and testing sets in three different languages: English, Chinese and Korean. The English dataset is SQuAD BIBREF2. The Chinese dataset is DRCD BIBREF14, a Chinese RC dataset with 30,000+ examples in the training set and 10,000+ examples in the development set. The Korean dataset is KorQuAD BIBREF15, a Korean RC dataset with 60,000+ examples in the training set and 10,000+ examples in the development set, created in exactly the same procedure as SQuAD. We always use the development sets of SQuAD, DRCD and KorQuAD for testing since the testing sets of the corpora have not been released yet.
Next, to construct a diverse cross-lingual RC dataset with compromised quality, we translated the English and Chinese datasets into more languages, with Google Translate. An obvious issue with this method is that some examples might no longer have a recoverable span. To solve the problem, we use fuzzy matching to find the most possible answer, which calculates minimal edit distance between translated answer and all possible spans. If the minimal edit distance is larger than min(10, lengths of translated answer - 1), we drop the examples during training, and treat them as noise when testing. In this way, we can recover more than 95% of examples. The following generated datasets are recovered with same setting.
The pre-trained multi-BERT is the official released one. This multi-lingual version of BERT were pre-trained on corpus in 104 languages. Data in different languages were simply mixed in batches while pre-training, without additional effort to align between languages. When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged.
Next, to construct a diverse cross-lingual RC dataset with compromised quality, we translated the English and Chinese datasets into more languages, with Google Translate1. An obvious issue with this method is that some examples might no longer have a recoverable span. To solve the problem, we use fuzzy matching2 to find the most possible answer, which calculates minimal edit distance between translated answer and all possible spans. If the minimal edit distance is larger than min(10, lengths of translated answer - 1), we drop the examples during training, and treat them as noise when testing. In this way, we can recover more than 95% of examples.
Answer: They use fuzzy matching2 to find the most possible answer, which calculates minimal edit distance between translated answer and all possible spans. If the minimal edit distance is larger than min(10, lengths of translated answer - 1), they drop the examples during training, and treat them as noise when testing. In this way, they can recover more than 95% of examples.
Question: What is difference in peformance between proposed model and state-of-the art on other question types?
Evidence: FLOAT SELECTED: Table 2. Performance of different models on DROP’s development set in terms of Exact Match (EM) and F1.
FLOAT SELECTED: Table 2. Performance of different models on DROP’s development set in terms of Exact Match (EM) and F1.
Answer: For single-span questions, the proposed LARGE-SQUAD improve performance of the MTMSNlarge baseline for 2.1 EM and 1.55 F1.
For number type question,  MTMSNlarge baseline  have improvement over LARGE-SQUAD for 3,11  EM and  2,98 F1. 
For date question,  LARGE-SQUAD have improvements in 2,02 EM but MTMSNlarge have improvement of 4,39 F1.
Question: who is called God Of The Offside ?
Answer: Sourav Ganguly who was Indian skipper was famously called as The God of the offside. Because he was second to none when he caressed the ball through the covers or cut it fiercely past point.It was known that this nickname was given by Rahul Dravid.
Question: Who is Satoshi Nakamoto?
Evidence: "Satoshi Nakamoto is the name used by the presumed inventors of Bitcoin. It is probably a pseudonym or nom de plume for the creator.

The anonymous programmer, Satoshi Nakamoto allegedly developed bitcoin, authored the bitcoin white paper, and created how it was to be used. As part of the implementation, Nakamoto devised the first blockchain database, this has been confirmed. They were active in developing bitcoin until December 2010. Many people have claimed, or have been claimed, to be Nakamoto. Nakamoto's BTC wallet worth approx. 1 million BTC (48 billion USD) or 5% of all BTC mined to this day, was activated in December 2021 after 10 years. Before this it was last activated 17 May 2010."
Answer: Satoshi Nakamoto is the name used by the presumed inventors of Bitcoin. It is probably a pseudonym or nom de plume for the creator.
Question: Name five popular large dog breeds
Answer: Golden Retrievers, Labrador Retriever, Poodle, Great Dane, and Boxers
Question: From the information below, extract the date the Battle of Entebbe took place.
Evidence: The Battle of Entebbe was a battle of the Uganda–Tanzania War that took place on 7 April 1979 on the Entebbe peninsula in Uganda between Tanzanian units and Ugandan and Libyan units. The Tanzanians occupied the area, killed hundreds of Libyans, and ended the Libyan airlift in support of the Ugandan Government.
Answer: The Battle of Entebbe took place on April 7, 1979.
Question: What datasets are used?
Evidence: To this end, various datasets have been created to benchmark progress in the field. In hate speech detection, recently BIBREF5 compiled and released a dataset of over 24,000 tweets labeled as containing hate speech, offensive language, or neither. The TRAC shared task on Aggression Identification, a dataset of over 15,000 Facebook comments labeled with varying levels of aggression, was released as part of a competition BIBREF14. In order to benchmark toxic comment detection, The Wikipedia Toxic Comments dataset (which we study in this work) was collected and extracted from Wikipedia Talk pages and featured in a Kaggle competition BIBREF12, BIBREF15. Each of these benchmarks examine only single-turn utterances, outside of the context in which the language appeared. In this work we recommend that future systems should move beyond classification of singular utterances and use contextual information to help identify offensive language.
In order to benchmark toxic comment detection, The Wikipedia Toxic Comments dataset (which we study in this work) was collected and extracted from Wikipedia Talk pages and featured in a Kaggle competition BIBREF12, BIBREF15. 
Answer: The answers are shown as follows:
* The Wikipedia Toxic Comments dataset
Question: Which US astronauts that landed on the moon were Scouts?
Answer: Since NASA's formation in 1958, 181 of the 320 pilots and scientists selected by NASA were Scouts.

Of the 24 people to travel to the Moon on the Apollo missions, 20 were Scouts, including 11 of the 12 Moonwalkers.

All three of the Apollo 13 astronauts, who had to abort their mission and return to Earth were also Scouts.
Question: On what dataset is Aristo system trained?
Evidence: Several methods make use of the Aristo Corpus, comprising a large Web-crawled corpus ($5 \times 10^{10}$ tokens (280GB)) originally from the University of Waterloo, combined with targeted science content from Wikipedia, SimpleWikipedia, and several smaller online science texts (BID25).
The Regents exam questions are taken verbatim from the New York Regents Examination board, using the 4th Grade Science, 8th Grade Science, and 12th Grade Living Environment examinations. The questions are partitioned into train/dev/test by exam, i.e., each exam is either in train, dev, or test but not split up between them. The ARC dataset is a larger corpus of science questions drawn from public resources across the country, spanning grades 3 to 9, and also includes the Regents 4th and 8th questions (using the same train/dev/test split). Further details of the datasets are described in (BID13). The datasets are publicly available. Dataset sizes are shown in Table TABREF34. All but 39 of the 9366 questions are 4-way multiple choice, the remaining 39 ($<$0.5%) being 3- or 5-way. A random score over the entire dataset is 25.02%.
FLOAT SELECTED: Table 3: Dataset partition sizes (number of questions).
Several methods make use of the Aristo Corpus, comprising a large Web-crawled corpus ($5 \times 10^{10}$ tokens (280GB)) originally from the University of Waterloo, combined with targeted science content from Wikipedia, SimpleWikipedia, and several smaller online science texts (BID25).
The Regents exam questions are taken verbatim from the New York Regents Examination board, using the 4th Grade Science, 8th Grade Science, and 12th Grade Living Environment examinations. The questions are partitioned into train/dev/test by exam, i.e., each exam is either in train, dev, or test but not split up between them. The ARC dataset is a larger corpus of science questions drawn from public resources across the country, spanning grades 3 to 9, and also includes the Regents 4th and 8th questions (using the same train/dev/test split). Further details of the datasets are described in (BID13). The datasets are publicly available. Dataset sizes are shown in Table TABREF34. All but 39 of the 9366 questions are 4-way multiple choice, the remaining 39 ($<$0.5%) being 3- or 5-way. A random score over the entire dataset is 25.02%.
FLOAT SELECTED: Table 3: Dataset partition sizes (number of questions).
Answer: Aristo Corpus
Regents 4th
Regents 8th
Regents `12th
ARC-Easy
ARC-challenge
Question: Why is job training such as upskilling and reskilling so important?
Answer: The degree of importance of job training differs based on the government as job training is much more important in a capitalist economy versus a communist economy.  Assuming a capitalist economy, job training is important because of market dynamics, supply and demand.  In this case, demand are the available jobs and supply are the skilled workers who provide work in those jobs.  As technology advances, new innovations are made and cyclical market segments ebb and flow, the demand for highly skilled workers changes.  Upskilling is important as it helps workers to maintain valuable skills in their given field and the goal is to keep up with the technology advances in their respective field.  Reskilling is most often more closely linked to cyclical market forces where the demand for jobs in one field go down and another go up; reskilling is acquiring skills in the field with growing demand for jobs when a worker possesses skills in a field with declining demand for jobs or their skills.
Question: Please give me a summary of the meaning of a derivative.
Evidence: In mathematics, the derivative of a function of a real variable measures the sensitivity to change of the function value (output value) with respect to a change in its argument (input value). Derivatives are a fundamental tool of calculus. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances.

The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point. The tangent line is the best linear approximation of the function near that input value. For this reason, the derivative is often described as the "instantaneous rate of change", the ratio of the instantaneous change in the dependent variable to that of the independent variable.

Derivatives can be generalized to functions of several real variables. In this generalization, the derivative is reinterpreted as a linear transformation whose graph is (after an appropriate translation) the best linear approximation to the graph of the original function. The Jacobian matrix is the matrix that represents this linear transformation with respect to the basis given by the choice of independent and dependent variables. It can be calculated in terms of the partial derivatives with respect to the independent variables. For a real-valued function of several variables, the Jacobian matrix reduces to the gradient vector.

The process of finding a derivative is called differentiation. The reverse process is called antidifferentiation. The fundamental theorem of calculus relates antidifferentiation with integration. Differentiation and integration constitute the two fundamental operations in single-variable calculus.
Answer: A derivative is a function that measures the sensitivity to change a functions output based on changes to the input. For example, how quickly the position of an object moves over a defined time period (i.e., the derivative of the position with respect to time) is the object's velocity.
Question: What do the authors suggest in this paper?
Evidence: There have been many advances in machine learning methods which help machines understand human behavior better than ever. One of the most important aspects of human behavior is emotion. If machines could detect human emotional expressions, it could be used to improve on verity of applications such as marketing BIBREF0 , human-computer interactions BIBREF1 , political science BIBREF2 etc.
Emotion in humans is complex and hard to distinguish. There have been many emotional models in psychology which tried to classify and point out basic human emotions such as Ekman's 6 basic emotions BIBREF3 , Plutchik's wheel of emotions BIBREF4 , or Parrott's three-level categorization of emotions BIBREF5 . These varieties show that emotions are hard to define, distinguish, and categorize even for human experts.
By adding the complexity of language and the fact that emotion expressions are very complex and context dependant BIBREF6 , BIBREF7 , BIBREF8 , we can see why detecting emotions in textual data is a challenging task. This difficulty can be seen when human annotators try to assign emotional labels to the text, but using various techniques the annotation task can be accomplished with desirable agreement among the annotators BIBREF9 .
Based on that, we suggest focusing on methodologies that increase the quality of these latent representations both contextually and emotionally, can improve the performance of these models.
Answer: Suggest focusing on methodologies that increase the quality of these latent representations both contextually and emotionally, can improve the performance of these models.
Question: What are some classes you need to take in university to become an aerospace engineer?
Answer: Some classes you need to take in university to become an aerospace engineer include thermodynamics, aerodynamics, fluid dynamics, and mechanics of materials.
Question: What are the author's future work plans?
Evidence: We have proposed a novel RvNN architecture to fully utilize linguistic priors. A newly introduced tag-level tree-LSTM demonstrates that it can effectively control the composition function of the corresponding word-level tree-LSTM. In addition, the proper contextualization of the input word vectors results in significant performance improvements on several sentence-level tasks. For future work, we plan to explore a new way of exploiting dependency trees effectively, similar to the case of constituency trees.
For future work, we plan to explore a new way of exploiting dependency trees effectively, similar to the case of constituency trees.
Answer: For future work, they plan to explore a new way of exploiting dependency trees effectively, similar to the case of constituency trees.
Question: What previous approaches are considered?
Evidence: Our baseline system (Baseline_1850K) is taken from BIBREF13 . It consists of a DNN trained to predict subword targets within the keywords. The input to the DNN consists of a sequence with INLINEFORM0 frames of left and INLINEFORM1 frames of right context; each with a stride of INLINEFORM2 . The topology consists of a 1-D convolutional layer with 92 filters (of shape 8x8 and stride 8x8), followed by 3 fully-connected layers with 512 nodes and a rectified linear unit activation each. A final softmax output predicts the 7 subword targets, obtained from the same forced alignment process described in SECREF5 . This results in the baseline DNN containing 1.7M parameters, and performing 1.8M multiply-accumulate operations per inference (every 30ms of streaming audio). A keyword spotting score between 0 and 1 is computed by first smoothing the posterior values, averaging them over a sliding window of the previous 100 frames with respect to the current INLINEFORM3 ; the score is then defined as the largest product of the smoothed posteriors in the sliding window as originally proposed in BIBREF6 .
Our baseline system (Baseline_1850K) is taken from BIBREF13 . It consists of a DNN trained to predict subword targets within the keywords. The input to the DNN consists of a sequence with INLINEFORM0 frames of left and INLINEFORM1 frames of right context; each with a stride of INLINEFORM2 . The topology consists of a 1-D convolutional layer with 92 filters (of shape 8x8 and stride 8x8), followed by 3 fully-connected layers with 512 nodes and a rectified linear unit activation each. A final softmax output predicts the 7 subword targets, obtained from the same forced alignment process described in SECREF5 . This results in the baseline DNN containing 1.7M parameters, and performing 1.8M multiply-accumulate operations per inference (every 30ms of streaming audio). A keyword spotting score between 0 and 1 is computed by first smoothing the posterior values, averaging them over a sliding window of the previous 100 frames with respect to the current INLINEFORM3 ; the score is then defined as the largest product of the smoothed posteriors in the sliding window as originally proposed in BIBREF6 .
Answer: The answers are shown as follows:
* Our baseline system (Baseline_1850K) is taken from BIBREF13 .
Question: Who has been an announcer on the gameshow "The Price is Right"?
Answer: Rich Fields, George Gray, Johnny Olson, Rod Roddy
Question: When did India get Independence?
Answer: India got Independence from the British Empire on 15th August, 1947
Question: Given these paragraphs about the Tesla bot, what is its alias?
Evidence: Optimus, also known as Tesla Bot, is a conceptual general-purpose robotic humanoid under development by Tesla, Inc. It was announced at the company's Artificial Intelligence (AI) Day event on August 19, 2021. CEO Elon Musk claimed during the event that Tesla would likely build a prototype by 2022. Musk is on record having said that he thinks Optimus "has the potential to be more significant than the vehicle business over time."

Tesla Bot is planned to measure 5 ft 8 in (173 cm) tall and weigh 125 lb (57 kg). According to the presentation made during the first AI Day event, a Tesla Bot will be "controlled by the same AI system Tesla is developing for the advanced driver-assistance system used in its cars" and have a carrying capacity of 45 lb (20 kg). Proposed tasks for the product are ones that are "dangerous, repetitive and boring", such as providing manufacturing assistance.

https://en.wikipedia.org/wiki/Optimus_(robot)
Answer: Optimus