DataHammer/scimrc huggingface dataset:
Question: From this paragraph, find out who built the Panama Canal
Evidence: The Panama Canal is an artificial 82 km (51 mi) waterway in Panama that connects the Atlantic Ocean with the Pacific Ocean and divides North and South America. The canal cuts across the Isthmus of Panama and is a conduit for maritime trade. One of the largest and most difficult engineering projects ever undertaken, the Panama Canal shortcut greatly reduces the time for ships to travel between the Atlantic and Pacific oceans, enabling them to avoid the lengthy, hazardous Cape Horn route around the southernmost tip of South America via the Drake Passage or Strait of Magellan.

Colombia, France, and later the United States controlled the territory surrounding the canal during construction. France began work on the canal in 1881, but stopped because of lack of investors' confidence due to engineering problems and a high worker mortality rate. The United States took over the project on May 4, 1904, and opened the canal on August 15, 1914. The US continued to control the canal and surrounding Panama Canal Zone until the 1977 Torrijos–Carter Treaties provided for its handover to Panama. After a period of joint American–Panamanian control, the canal was taken over by the Panamanian government in 1999. It is now managed and operated by the government-owned Panama Canal Authority.

Canal locks at each end lift ships up to Gatun Lake, an artificial lake 26 meters (85 ft) above sea level, created to reduce the amount of excavation work required for the canal, and then lower the ships at the other end. The original locks are 33.5 meters (110 ft) wide. A third, wider lane of locks was constructed between September 2007 and May 2016. The expanded waterway began commercial operation on June 26, 2016. The new locks allow transit of larger, New Panamax ships.

Annual traffic has risen from about 1,000 ships in 1914, when the canal opened, to 14,702 vessels in 2008, for a total of 333.7 million Panama Canal/Universal Measurement System (PC/UMS) tons. By 2012, more than 815,000 vessels had passed through the canal. In 2017 it took ships an average of 11.38 hours to pass between the canal's two locks. The American Society of Civil Engineers has ranked the Panama Canal one of the seven wonders of the modern world.
Answer: France began work on the Panama canal in 1881, but stopped because of lack of investors' confidence due to engineering problems and a high worker mortality rate. The United States took over the project on May 4, 1904, and opened the canal on August 15, 1914.
Question: Who is the 6th player in Women's NCAA D-I Basketball to score 1,000 points in a single season?
Answer: If you watched any of the Women's March Madness 2023 games, especially Iowa, then you witnessed greatness in the making by Caitlin Clark. This 1,000 point feat in a single season, happened midway through the game against the powerhouse (undefeated at the time) South Carolina Gamecocks on March 31, 2023. It's also worth noting that in the same game, Caitlin dropped 41 points and her three-point range is Steph Curry like.
Question: Identify which animal species is alive or extinct: Quagga, Beluga
Answer: Beluga is alive, Quagga is extinct.
Question: Why has pickleball become so popular?
Answer: Pickleball has indeed become very popular.  Promoters of the game have been calling it the "fastest growing sport" for some time now.

There are a few reasons many people cite for why the sport is so popular:

- It is a quick game to learn.  The rules are pretty simple, and more importantly, the skill needed to hit functional shots in the game is not very difficult to learn.  Compare it to tennis, where it can take multiple lessons from a professional teacher just to get to a point where you can hit a few good shots.  With pickleball, almost anyone of any age or fitness level can pick up a paddle and be playing a game in just minutes.

- Pickleball culture has tended to be very inclusive.  Many pickleball groups make it a point to be very inclusive of all ages, races and backgrounds.  This often creates a sense of community for players, and makes them more likely to keep playing and invite friends along, thus growing the sport.

- It is a fun way to get in a decent workout.  While not as taxing as, say, tennis, pickleball can be a good workout.  Especially since unlike tennis there is very little downtime.  The pace of games is very quick and involves near constant movement.  The workout is not cardio-intensive, but still it is an easy way to have fun while staying active.
Question: Which of these cities in the United States of America are the capital of their state: Albany, Albuquerque, Anchorage,  Ann Arbor, Annapolis?
Answer: Albany is the capital of New York and Annapolis is the capital of Maryland.  The other cities are not the capitals of their state: Albuquerque is a city in New Mexico but is not the capital, Anchorage is a city in Alaska but is not the capital, and Ann Arbor is a city in Michigan but is not the capital.
Question: Who won March Madness?
Evidence: The 2023 NCAA Division I men's basketball tournament involved 68 teams playing in a single-elimination tournament that determined the National Collegiate Athletic Association (NCAA) Division I men's basketball national champion for the 2022–23 season. The 84th annual edition of the tournament began on March 14, 2023, and concluded with the UConn Huskies defeating the San Diego State Aztecs, 76–59 in the championship game on April 3 at NRG Stadium in Houston, Texas.
Answer: UConn Huskies won the 2023 NCAA Division 1 men's basketball tournament defeating San Diego State Aztecs, 76-59. This tournament is popularly known as March Madness.
Question: What are the two metrics of dialogue evaluation?
Evidence: Dialogue evaluation is an open problem and existing works have found that automatic metrics have low correlation to human evaluation BIBREF35 , BIBREF36 , BIBREF37 . Thus, we resorted to manual evaluation to assess the generation quality on WeiboDial. We randomly sampled 200 posts from the test set and collected the generated results from all the models. For each pair of responses (one from ARAML and the other from a baseline, given the same input post), five annotators were hired to label which response is better (i.e. win, lose or tie) in terms of grammaticality (whether a response itself is grammatical and logical) and relevance (whether a response is appropriate and relevant to the post). The two metrics were evaluated independently.
The evaluation results are shown in Table TABREF35 . To measure the inter-annotator agreement, we calculated Fleiss' kappa BIBREF38 for each pair-wise comparison where results show moderate agreement ( INLINEFORM0 ). We also conducted sign test to check the significance of the differences.
As shown in Table TABREF35 , ARAML performs significantly better than other baselines in all the cases. This result indicates that the samples surrounding true responses provide stable rewards for the generator, and stable RAML training paradigm significantly enhances the performance in both metrics.
For each pair of responses (one from ARAML and the other from a baseline, given the same input post), five annotators were hired to label which response is better (i.e. win, lose or tie) in terms of grammaticality (whether a response itself is grammatical and logical) and relevance (whether a response is appropriate and relevant to the post). The two metrics were evaluated independently.
Answer: They are grammaticality and relevance.
Question: Who wrote the Lord of the Rings?
Answer: Lord of the Rings was written by J.R.R. Tolkien. Tolkien also wrote The Hobbit.
Question: What direction (North-South-East-West) is Malanów from Zygmuntówek?
Evidence: Zygmuntówek [zɨɡmunˈtuvɛk] is a village in the administrative district of Gmina Malanów, within Turek County, Greater Poland Voivodeship, in west-central Poland. It lies approximately 2 kilometres (1 mi) north of Malanów, 10 km (6 mi) south-west of Turek, and 112 km (70 mi) south-east of the regional capital Poznań.
Answer: Malanów is south of Zygmuntówek. We know this because Zygmuntówek is north of Malanów.
Question: What is IAAS?
Answer: IAAS is an acronym for Infrastructure-as-a-service and refers to the networking, storage, compute and virtualisation services the form the foundation of public cloud computing.
Question: Give me a bunch of ways that you can get information from a book.
Answer: There a number of ways to gather information from a book, you can listen to the audiobook version, there are a number of services that provide abridged versions of books that you can try, alternatively you can simply read the book.
Question: Where is Pompeii located?
Evidence: Pompeii (/pɒmˈpeɪ(i)/, Latin: [pɔmˈpei̯.iː]) was an ancient city located in what is now the comune of Pompei near Naples in the Campania region of Italy. Pompeii, along with Herculaneum and many villas in the surrounding area (e.g. at Boscoreale, Stabiae), was buried under 4 to 6 m (13 to 20 ft) of volcanic ash and pumice in the Eruption of Mount Vesuvius in 79 AD.
Answer: The ancient city of Pompeii is located near Naples in the Campania region of Italy.
Question: What was the Caribbean and Central America Action
Evidence: Caribbean and Central American Action was a 501(c)(3) non-profit organization which has hosted the annual Miami Conference on the Caribbean & Central America for 33 years.

The organization has a multinational Board of Trustees, currently headed by Cornelius B. Prior, Jr., and is based in Washington, DC.

Among the public figures speaking at the 33rd conference, held in 2009 in Miami, were the Prime Minister of Haiti, Jean-Max Bellerive, Canada's Minister of State of Foreign Affairs, Peter Kent and Puerto Rico's Secretary of State, Kenneth McClintock. The organization's policy-oriented programs have focused on tourism, financial services, policy and regulatory environments for business in the Americas, energy security, maritime port security, disaster mitigation, customs, telecommunications, intellectual property rights, information technology, agriculture and telecommunications. Cabinet-level officials have represented the United States at prior conferences

The organization has closed
Answer: Caribbean and Central American Action was a non-profit organization which has hosted the annual Miami Conference on the Caribbean & Central America for 33 years.

The organization has a multinational Board of Trustees, currently headed by Cornelius B. Prior, Jr., and is based in Washington, DC.

Among the public figures speaking at the 33rd conference, held in 2009 in Miami, were the Prime Minister of Haiti, Jean-Max Bellerive, Canada's Minister of State of Foreign Affairs, Peter Kent and Puerto Rico's Secretary of State, Kenneth McClintock. The organization's policy-oriented programs have focused on tourism, financial services, policy and regulatory environments for business in the Americas, energy security, maritime port security, disaster mitigation, customs, telecommunications, intellectual property rights, information technology, agriculture and telecommunications. Cabinet-level officials have represented the United States at prior conferences.

The organization has closed.
Question: Which is a species of fish? Noodlefish or Noodle
Answer: Noodlefish
Question: What NLP tasks do the authors evaluate feed-forward networks on?
Evidence: We experiment with small feed-forward networks for four diverse NLP tasks: language identification, part-of-speech tagging, word segmentation, and preordering for statistical machine translation.
We experiment with small feed-forward networks for four diverse NLP tasks: language identification, part-of-speech tagging, word segmentation, and preordering for statistical machine translation.
Answer: The answers are shown as follows:
* language identification, part-of-speech tagging, word segmentation, and preordering for statistical machine translation
Question: What is the nature of the classifier？
Evidence: The proposed LID algorithm builds on the work in BIBREF8 and BIBREF26. We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier.
The naive Bayesian classifier is trained to predict the specific language label of a piece of text, but used to first classify text as belonging to either the Nguni family, the Sotho family, English, Afrikaans, Xitsonga or Tshivenda. The scikit-learn multinomial naive Bayes classifier is used for the implementation with an alpha smoothing value of 0.01 and hashed text features.
The lexicon based classifier is then used to predict the specific language within a language group. For the South African languages this is done for the Nguni and Sotho groups. If the lexicon prediction of the specific language has high confidence then its result is used as the final label else the naive Bayesian classifier's specific language prediction is used as the final result. The lexicon is built over all the data and therefore includes the vocabulary from both the training and testing sets.
The lexicon based classifier is designed to trade higher precision for lower recall. The proposed implementation is considered confident if the number of words from the winning language is at least one more than the number of words considered to be from the language scored in second place.
The stacked classifier is tested against three public LID implementations BIBREF17, BIBREF23, BIBREF8. The LID implementation described in BIBREF17 is available on GitHub and is trained and tested according to a post on the fasttext blog. Character (5-6)-gram features with 16 dimensional vectors worked the best. The implementation discussed in BIBREF23 is available from https://github.com/tomkocmi/LanideNN. Following the instructions for an OSX pip install of an old r0.8 release of TensorFlow, the LanideNN code could be executed in Python 3.7.4. Settings were left at their defaults and a learning rate of 0.001 was used followed by a refinement with learning rate of 0.0001. Only one code modification was applied to return the results from a method that previously just printed to screen. The LID algorithm described in BIBREF8 is also available on GitHub.
The stacked classifier is also tested against the results reported for four other algorithms BIBREF16, BIBREF26, BIBREF24, BIBREF15. All the comparisons are done using the NCHLT BIBREF7, DSL 2015 BIBREF19 and DSL 2017 BIBREF1 datasets discussed in Section SECREF2.
We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier.
Answer: It is a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier.
Question: How high is the inter-annotator agreement?
Evidence: We measured intra-annotator agreement between two annotators in three aspects: relations, senses, arguments. To be specific, the annotators’ consistency in annotating the type of a specific relation or sense and the position and scope of arguments are measured. To assess the consistency of annotations and also eliminate coincidental annotations, we used agreement rates, which is calculated by dividing the number of senses under each category where the annotators annotate consistently by the total number of each kind of sense. And considering the potential impact of unbalanced distribution of senses, we also used the Kappa value. And the final agreement study was carried out for the first 300 relations in our corpus. We obtained high agreement results and Kappa value for the discourse relation type and top-level senses ($\ge {0.9} $ ). However, what we did was more than this, and we also achieved great results on the second-level and third-level senses for the sake of our self-demand for high-quality, finally achieving agreement of 0.85 and Kappa value of 0.83 for these two deeper levels of senses.
However, what we did was more than this, and we also achieved great results on the second-level and third-level senses for the sake of our self-demand for high-quality, finally achieving agreement of 0.85 and Kappa value of 0.83 for these two deeper levels of senses.
Answer: The answers are shown as follows:
* agreement of 0.85 and Kappa value of 0.83
Question: Give me some ideas for party games at a child's birthday.
Answer: Some classic children's party games are Pass the Parcel, Musical Chairs, Pin the Tail on the Donkey, Sack Race, Treasure Hunt.
Question: What is the performance difference between proposed method and state-of-the-arts on these datasets?
Evidence: Table TABREF40 illustrates the BLEU scores of FlowSeq and baselines with advanced decoding methods such as iterative refinement, IWD and NPD rescoring. The first block in Table TABREF40 includes the baseline results from autoregressive Transformer. For the sampling procedure in IWD and NPD, we sampled from a reduced-temperature model BIBREF11 to obtain high-quality samples. We vary the temperature within $\lbrace 0.1, 0.2, 0.3, 0.4, 0.5, 1.0\rbrace $ and select the best temperature based on the performance on development sets. The analysis of the impact of sampling temperature and other hyper-parameters on samples is in § SECREF50. For FlowSeq, NPD obtains better results than IWD, showing that FlowSeq still falls behind auto-regressive Transformer on model data distributions. Comparing with CMLM BIBREF8 with 10 iterations of refinement, which is a contemporaneous work that achieves state-of-the-art translation performance, FlowSeq obtains competitive performance on both WMT2014 and WMT2016 corpora, with only slight degradation in translation quality. Leveraging iterative refinement to further improve the performance of FlowSeq has been left to future work.
FLOAT SELECTED: Table 2: BLEU scores on two WMT datasets of models using advanced decoding methods. The first block are Transformer-base (Vaswani et al., 2017). The second and the third block are results of models trained w/w.o. knowledge distillation, respectively. n = l × r is the total number of candidates for rescoring.
Table TABREF40 illustrates the BLEU scores of FlowSeq and baselines with advanced decoding methods such as iterative refinement, IWD and NPD rescoring.
FLOAT SELECTED: Table 2: BLEU scores on two WMT datasets of models using advanced decoding methods. The first block are Transformer-base (Vaswani et al., 2017). The second and the third block are results of models trained w/w.o. knowledge distillation, respectively. n = l × r is the total number of candidates for rescoring.
Answer: Difference is around 1 BLEU score lower on average than state of the art methods.
Question: Different ways in which you can eat noodles
Answer: hand, fork, spoon, chopsticks
Question: Identify which instrument is string or woodwind: Ahenk, Arghul
Answer: Arghul is woodwind, Ahenk is string.
Question: What are the FAANG companies?
Answer: Five of the best performing tech stocks of the last decade are Facebook, Amazon, Apple, Netflix, and Google. Those five stocks have been pulled together into an the acronym FAANG.
Question: Which city are Stroopwafels from?
Answer: Stroopwafels are a type of Dutch waffle-like cake made with ground or shredded wheat and a variety of fillings. They are most commonly found in the city of Haarlem, Netherlands.
Question: Why are people scared of snakes?
Answer: Snakes tend to scare people for a number of reasons. For starters, many of them have poisonous venom. Snakes are also very stealthy and oftentimes you may not even be aware of their presence. Some people are also frightened by the fact that snakes slither, and do not have any legs. Encountering a snake in the wild can send chills down your spine.
Question: What does a 9 carat gold mean
Answer: Pure gold with no additives is knows as 24 carat gold. An item marked as 9 carat gold contains 9/24th (or 37.5 percent) of pure gold and the rest are additives, typically a mix of silver, copper, zinc, etc.
Question: What is the best soccer club in the premier league?
Answer: The answer to this question is subjective. Many believe that Manchester United, a soccer club founded in 1878, is the best soccer club from the premier league. The club has won 20 first division titles and is a 3 time champion league winner. However there are also many other opinions. For example, a lots of fans consider that Liverpool, which holds the record for the most champion league title wins from premier league, is the best club in the league. Manchester City has also been performing great in the past decade and has been considered the best club in the league for the past 10 years.
Question: How about the inter-annotator agreement status in the Turkish tweets corpus？
Evidence: We utilised two datasets for both Turkish and English to evaluate our methods.
For Turkish, as the first dataset, we utilised the movie reviews which are collected from a popular website. The number of reviews in this movie corpus is 20,244 and the average number of words in reviews is 39. Each of these reviews has a star-rating score which is indicative of sentiment. These polarity scores are between the values 0.5 and 5, at intervals of 0.5. We consider a review to be negative it the score is equal to or lower than 2.5. On the other hand, if it is equal to or higher than 4, it is assumed to be positive. We have randomly selected 7,020 negative and 7,020 positive reviews and processed only them.
The second Turkish dataset is the Twitter corpus which is formed of tweets about Turkish mobile network operators. Those tweets are mostly much noisier and shorter compared to the reviews in the movie corpus. In total, there are 1,716 tweets. 973 of them are negative and 743 of them are positive. These tweets are manually annotated by two humans, where the labels are either positive or negative. We measured the Cohen's Kappa inter-annotator agreement score to be 0.82. If there was a disagreement on the polarity of a tweet, we removed it.
We also utilised two other datasets in English to test the cross-linguality of our approaches. One of them is a movie corpus collected from the web. There are 5,331 positive reviews and 5,331 negative reviews in this corpus. The other is a Twitter dataset, which has nearly 1.6 million tweets annotated through a distant supervised method BIBREF14. These tweets have positive, neutral, and negative labels. We have selected 7,020 positive tweets and 7,020 negative tweets randomly to generate a balanced dataset.
We measured the Cohen's Kappa inter-annotator agreement score to be 0.82.
Answer: They measured the Cohen's Kappa inter-annotator agreement score to be 0.82.
Question: What are the causes Alzheimer's disease?
Answer: Undrestanding the causes of the Alzheimer's disease is an ongoing research. The research so far indicate that causes probably include a combination of age-related changes in the brain, along with genetic, environmental, and lifestyle factors. The importance of any one of these factors may differ from person to person. 
One well-known gene that influences Alzheimer’s risk is the apolipoprotein E (APOE) gene, which has been identified via Genome Wide Association Studies (GWAS). The APOE gene is involved in making a protein that helps carry cholesterol and other types of fat in the bloodstream.