DataHammer/scimrc huggingface dataset:
Question: Tomas is a 50-yr-old nonsmoking male who was recently invited by colleagues to participate in a 10-km trail run. He's been walking for 40 minutes 3 days a week, something he's maintained “for years”. His goal is to run the entire 10K race without stopping. He reports having what he describes as a “mild heart attack” at 45 years old, completed cardiac rehabilitation, and has had no problems since. He takes a statin and a low-dose aspirin daily. During his last visit (2 years ago) with his cardiologist, no changes were noted in his medical condition. Can you brainstorm reasons why Tomas's physician would prescribe a statin? Make sure to explain how this medication works.
Answer: Statins are drugs that can lower your cholesterol, which can help lower Tomas’s total cholesterol and reduce their risk of a heart attack or stroke. His physician may have prescribed him this as he has had a history of cardiovascular disease (specifically a heart attack), and the statin is primarily a preventative measure against future heart attacks. Statins block a substance your liver needs to make cholesterol. This causes your liver to remove cholesterol from your blood.
Question: Do they use expert annotations, crowdsourcing, or only automatic methods to analyze the corpora?
Evidence: The rest of this paper is organized as follows: In Sec. "Materials and Methods" we discuss our materials and methods, including the dataset we studied, how we preprocessed that data and extracted a `causal' corpus and a corresponding `control' corpus, and the details of the statistical and language analysis tools we studied these corpora with. In Sec. "Results" we present results using these tools to compare the causal statements to control statements. We conclude with a discussion in Sec. "Discussion" .
In Sec. "Materials and Methods" we discuss our materials and methods, including the dataset we studied, how we preprocessed that data and extracted a `causal' corpus and a corresponding `control' corpus, and the details of the statistical and language analysis tools we studied these corpora with.
Answer: Only automatic methods
Question: Are plastic bags and containers bad for the environment?
Answer: In short, yes.

Surprisingly, though, that has not always been the consensus, and in some cases, it is not true.

In the 1970s energy crisis, reducing the weight of materials, and increasing the shelf life of goods (which helps with optimizing transportation methods) were widely lauded as environmentally positive. In short, paper bags weigh more than plastic bags, and transportation requires fossil fuels, and of course fossil fuels impact the environment in widely documented ways.

Also, some plastics like PET have great recycling track records. So if it reduces energy consumption, increases shelf life, and can be recycled it seems like a huge environmental win.

However, plastics are very durable, and now micro-plastics are everywhere. They are in the deepest ocean trenches, accumulated in many species of animals and plants, and will be for our lifetimes. Their toxicity and negative impacts are just beginning to be understood. This has driven the removal of "single use plastics" from a lot of commerce, with further reductions called for by environmental groups worldwide.
Question: What are the ten tallest buildings in the world?
Answer: The ten tallest buildings in the world, including location, height and year constructed are:
1. Burj Khalifa, Dubai, United Arab Emirates, 828 metres, 2010
2. Merdeka 118, Kuala Lumpur, Malaysia, 678.9 metres, 2023
3. Shanghai Tower, Shanghai, China, 632 metres, 2015
4. Abraj Al-Bait, Clock Tower, Mecca, Saudi Arabia, 601 metres, 2012
5. Ping An International Finance Centre, Shenzhen, China, 599.1 metres, 2017
6. Lotte World Tower, Seoul, South Korea, 554.5 metres, 2017
7. One World Trade Center, New York City, USA, 541.3 metres, 2014
8=. Guangzhou CTF Finance Centre, Guangzhou, China, 530 metres, 2016
8=. Tianjin CTF Finance Centre, Tianjin, China, 530 metres, 2019
10. China Zun, Beijing, China, 527.7 metres, 2018
Question: What are signs my partner is cheating?
Answer: Turning off their shared location, changing the password on their phone, new interest in self care, unplanned business trips, spending lots of time on social media, pulling cash out of the Atm so their transactions are not traceable, turning phone off or not answering phone
Question: Which inter-annotator metric do they use?
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.
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.
Answer: The answers are shown as follows:
* agreement rates
* Kappa value
Question: Give me a list of five of the most widely known recording artists/bands of all time, across all genres of music.
Answer: The Beatles, The Rolling Stones, Elvis Presley, Michael Jackson, Garth Brook
Question: Extract the date and place of birth for Henning Bertil Stjernfelt from the passage provided.
Evidence: Lieutenant Colonel Henning Bertil Stjernfelt was a Swedish Army officer and military historian. He wrote several books about World War II and the Swedish coastal defence. Stjernfelt was also a part of the formation of the Swedish Coastal Rangers in 1956. Stjernfelt was born on 24 July 1917 in Gustavi Parish, Gothenburg and Bohus County, Sweden, the son of Henning Stjernfelt, a dentist, and his wife Ruth (née Magnusson). He passed studentexamen at Norra Latin in Stockholm in 1937.
Answer: Henning Bertil Stjernfelt was born on 24 July 1917 in Gustavi Parish, Gothenburg and Bohus County, Sweden.
Question: Which features do they use to model Twitter messages?
Evidence: To properly distinguish word tokens and punctuation we tokenized the tweets by means of Ucto, a rule-based tokenizer with good performance on the Dutch language, and with a configuration specific for Twitter. Tokens were lowercased in order to focus on the content. Punctuation was maintained, as well as emoji and emoticons. Such markers could be predictive in the context of a discussion such as vaccination. To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features. Features were coded binary, i.e. set to 1 if a feature is seen in a message and set to 0 otherwise. During training, all features apart from the top 15,000 most frequent ones were removed.
To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features.
Answer: The answers are shown as follows:
* word unigrams, bigrams, and trigrams
Question: What awards does someone need to be an EGOT winner?
Answer: The person would need an Emmy, a Grammy, an Oscar, and a Tony award to get EGOT status.
Question: What is fiscal policy in economics and political science?
Evidence: In economics and political science, fiscal policy is the use of government revenue collection (taxes or tax cuts) and expenditure to influence a country's economy. The use of government revenue expenditures to influence macroeconomic variables developed in reaction to the Great Depression of the 1930s, when the previous laissez-faire approach to economic management became unworkable.
Answer: In economics and political science, fiscal policy is the use of government revenue collection (taxes or tax cuts) and expenditure to influence a country's economy. The use of government revenue expenditures to influence macroeconomic variables developed in reaction to the Great Depression of the 1930s, when the previous laissez-faire approach to economic management became unworkable. Fiscal policy is based on the theories of the British economist John Maynard Keynes, whose Keynesian economics theorised that government changes in the levels of taxation and government spending influence aggregate demand and the level of economic activity. Fiscal and monetary policy are the key strategies used by a country's government and central bank to advance its economic objectives. The combination of these policies enables these authorities to target inflation (which is considered "healthy" at the level in the range 2%–3%) and to increase employment. Additionally, it is designed to try to keep GDP growth at 2%–3% and the unemployment rate near the natural unemployment rate of 4%–5%. This implies that fiscal policy is used to stabilise the economy over the course of the business cycle.

Changes in the level and composition of taxation and government spending can affect macroeconomic variables, including:

aggregate demand and the level of economic activity
saving and investment
income distribution
allocation of resources.
Fiscal policy can be distinguished from monetary policy, in that fiscal policy deals with taxation and government spending and is often administered by a government department; while monetary policy deals with the money supply, interest rates and is often administered by a country's central bank. Both fiscal and monetary policies influence a country's economic performance.
Question: What is the expense ratio for an ETF?
Answer: An expense ratio reflects how much a mutual fund or an ETF (exchange-traded fund) pays for portfolio management, administration, marketing, and distribution, among other expenses. You'll almost always see it expressed as a percentage of the fund's average net assets (instead of a flat dollar amount).
Question: How do I make a custom-shaped rug?
Answer: The easiest way is to buy a normal rug that you can cut down to size to fit your shape. Rugs made out of synthetic materials are easier to cut since they do not need special processing to stop them from getting unthreaded. You should draw an outline on the back on the rug and use a very sharp cutter to trace it. To prevent damage while cutting, you can put a regular kitchen cutting board under your work area. Finally, you can seal the edges either by singeing them, putting a bead of glue or buying adhesive rug edging tape.
Question: How many paraphrases are generated per question?
Evidence: For WebQuestions, we use 8 handcrafted part-of-speech patterns (e.g., the pattern (DT)?(JJ.? $\mid $ NN.?){0,2}NN.? matches the noun phrase the big lebowski) to identify candidate named entity mention spans. We use the Stanford CoreNLP caseless tagger for part-of-speech tagging BIBREF38 . For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. We generate ungrounded graphs for these paraphrases and treat the final entity disambiguation and paraphrase selection as part of the semantic parsing problem.
For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. 
Answer: 10*n paraphrases, where n depends on the number of paraphrases that contain the entity mention spans
Question: What is the difference between leadership and management?
Answer: Leadership means influencing people toward a right direction. Management means organizing people to achieve a goal. In other words, leadership does right things, management does things right.

In a business or a society, different people often have different opinions. A good leadership can influence a large number of people toward a direction that will generate positive impact on the business or the society. Leadership can be seen in most aspects of human society, from choosing a vacation destination to becoming a president.

Most business or social activities require multiple people to accomplish. A good management can organize a large number of people to accomplish complex efforts with great efficacy. Good management skills can be applied to different occasions in life, from organizing a party to running a company.
Question: Given the reference text about the Emancipation Proclamation, approximately how many enslaved African Americans were freed?
Evidence: The Emancipation Proclamation, officially Proclamation 95, was a presidential proclamation and executive order issued by United States President Abraham Lincoln on January 1, 1863, during the Civil War. The Proclamation changed the legal status of more than 3.5 million enslaved African Americans in the secessionist Confederate states from enslaved to free. As soon as slaves escaped the control of their enslavers, either by fleeing to Union lines or through the advance of federal troops, they were permanently free. In addition, the Proclamation allowed for former slaves to "be received into the armed service of the United States".
Answer: Approximately 3.5 million enslaved African Americans were freed by the Emancipation Proclamation.
Question: How much labeled data is available for these two languages?
Evidence: The Hausa data used in this paper is part of the LORELEI language pack. It consists of Broad Operational Language Translation (BOLT) data gathered from news sites, forums, weblogs, Wikipedia articles and twitter messages. We use a split of 10k training and 1k test instances. Due to the Hausa data not being publicly available at the time of writing, we could only perform a limited set of experiments on it.
The Yorùbá NER data used in this work is the annotated corpus of Global Voices news articles recently released by BIBREF22. The dataset consists of 1,101 sentences (26k tokens) divided into 709 training sentences, 113 validation sentences and 279 test sentences based on 65%/10%/25% split ratio. The named entities in the dataset are personal names (PER), organization (ORG), location (LOC) and date & time (DATE). All other tokens are assigned a tag of "O".
The Hausa data used in this paper is part of the LORELEI language pack. It consists of Broad Operational Language Translation (BOLT) data gathered from news sites, forums, weblogs, Wikipedia articles and twitter messages. We use a split of 10k training and 1k test instances.
The Yorùbá NER data used in this work is the annotated corpus of Global Voices news articles recently released by BIBREF22. The dataset consists of 1,101 sentences (26k tokens) divided into 709 training sentences, 113 validation sentences and 279 test sentences based on 65%/10%/25% split ratio.
Answer: The answers are shown as follows:
* 10k training and 1k test
* 1,101 sentences (26k tokens)
Question: Which is the best performing method?
Evidence: The top performing approaches Caravel, COAV and NNCD deserve closer attention. All three are based on character-level language models that capture low-level features similar to character INLINEFORM0 -grams, which have been shown in numerous AA and AV studies (for instance, BIBREF39 , BIBREF26 ) to be highly effective and robust. In BIBREF19 , BIBREF28 , it has been shown that Caravel and COAV were also the two top-performing approaches, where in BIBREF19 they were evaluated on the PAN-2015 AV corpus BIBREF12 , while in BIBREF28 they were applied on texts obtained from Project Gutenberg. Although both approaches perform similarly, they differ in the way how the decision criterion INLINEFORM1 is determined. While COAV requires a training corpus to learn INLINEFORM2 , Caravel assumes that the given test corpus (which provides the impostors) is balanced. Given this assumption, Caravel first computes similarity scores for all verification problems in the corpus and then sets INLINEFORM3 to the median of all similarities (cf. Figure FIGREF49 ). Thus, from a machine learning perspective, there is some undue training on the test set. Moreover, the applicability of Caravel in realistic scenarios is questionable, as a forensic case is not part of a corpus where the Y/N-distribution is known beforehand.
The top performing approaches Caravel, COAV and NNCD deserve closer attention.
Answer: The answers are shown as follows:
* Caravel, COAV and NNCD
Question: How many times michael schumacher won the F1 championship ?
Answer: 7 times
Question: What kind of annotators are involved in the study?
Evidence: The database consists of 1,000 short texts, each having 10-40 words. Out of these, 350 are sarcastic and are collected as follows: (a) 103 sentences are from two popular sarcastic quote websites, (b) 76 sarcastic short movie reviews are manually extracted from the Amazon Movie Corpus BIBREF11 by two linguists. (c) 171 tweets are downloaded using the hashtag #sarcasm from Twitter. The 650 non-sarcastic texts are either downloaded from Twitter or extracted from the Amazon Movie Review corpus. The sentences do not contain words/phrases that are highly topic or culture specific. The tweets were normalized to make them linguistically well formed to avoid difficulty in interpreting social media lingo. Every sentence in our dataset carries positive or negative opinion about specific “aspects”. For example, the sentence “The movie is extremely well cast” has positive sentiment about the aspect “cast”.


The annotators were seven graduate students with science and engineering background, and possess good English proficiency. They were given a set of instructions beforehand and are advised to seek clarifications before they proceed. The instructions mention the nature of the task, annotation input method, and necessity of head movement minimization during the experiment.
The annotators were seven graduate students with science and engineering background, and possess good English proficiency.
Answer: The annotators were seven graduate students with science and engineering background, and possess good English proficiency.
Question: What do you get by substituting the oxygen in the isocyanate group with a sulfur?
Evidence: In organic chemistry, isothiocyanate is the functional group −N=C=S, formed by substituting the oxygen in the isocyanate group with a sulfur. Many natural isothiocyanates from plants are produced by enzymatic conversion of metabolites called glucosinolates. These natural isothiocyanates, such as allyl isothiocyanate, are also known as mustard oils. An artificial isothiocyanate, phenyl isothiocyanate, is used for amino acid sequencing in the Edman degradation.
Answer: In organic chemistry, isothiocyanate is the functional group −N=C=S, formed by substituting the oxygen in the isocyanate group with a sulfur. Many natural isothiocyanates from plants are produced by enzymatic conversion of metabolites called glucosinolates. These natural isothiocyanates, such as allyl isothiocyanate, are also known as mustard oils. An artificial isothiocyanate, phenyl isothiocyanate, is used for amino acid sequencing in the Edman degradation.

Cruciferous vegetables, such as bok choy, broccoli, cabbage, cauliflower, kale, and others, are rich sources of glucosinolate precursors of isothiocyanates. Although there has been some basic research on how isothiocyanates might exert biological effects in vivo, there is no high-quality evidence to date for its efficacy against human diseases
Question: What are the competing models?
Evidence: FLOAT SELECTED: Table 2: Results on EMNLP2017 WMT News dataset. The 95 % confidence intervals from multiple trials are reported.
FLOAT SELECTED: Table 2: Results on EMNLP2017 WMT News dataset. The 95 % confidence intervals from multiple trials are reported.
Answer: TEACHER FORCING (TF), SCHEDULED SAMPLING (SS),  SEQGAN, RANKGAN, LEAKGAN.
Question: Given a reference text about the Kumanichi Road Race, tell me when the race was first held and who holds the course records.
Evidence: The Kumanichi Road Race (Japanese: 熊本30K) is an annual road running competition over 30 kilometres which is typically held in February in Kumamoto, Japan. It was first held in 1957 as a men-only competition. A women's race was introduced in 2006. The race is one of the world's most prominent over the uncommon 30K distance. A total of eight Association of Road Racing Statisticians-recognised men's world records have been set at the competition, including the current world record.

The course records are held by Takayuki Matsumiya (1:28:00) and Yuka Hakoyama (1:43:26). It is mostly contested by Japanese athletes, with Kenya's Willy Kangogo becoming the event's sole non-national winner in 2007.
Answer: The Kumanichi Road Race was first held in 1957. Takayuki Matsumiya and Yuka Hakoyama hold the course records.
Question: Who was Beethoven?
Evidence: Ludwig van Beethoven[n 1] (baptised 17 December 1770 – 26 March 1827) was a German composer and pianist. Beethoven remains one of the most admired composers in the history of Western music; his works rank among the most performed of the classical music repertoire and span the transition from the Classical period to the Romantic era in classical music. His career has conventionally been divided into early, middle, and late periods. His early period, during which he forged his craft, is typically considered to have lasted until 1802. From 1802 to around 1812, his middle period showed an individual development from the styles of Joseph Haydn and Wolfgang Amadeus Mozart, and is sometimes characterized as heroic. During this time, he began to grow increasingly deaf. In his late period, from 1812 to 1827, he extended his innovations in musical form and expression.
Answer: Beethoven was a German composer and pianist known for his famous symphonies such as Moonlight Sonata.
Question: How long will it take me to train for a marathon?
Answer: It depends on your goals! Many runners training to finish a marathon can do so with only 12 weeks of training, but if you are aiming for a PR or to qualify for the Boston Marathon, you'll likely need a longer training plan. Most runners who qualify for Boston run between 50 and 80 miles a week, which can take anywhere from 10 to 15 hours a week.
Question: Which of these counties has only one neighbour, and which have more than one neighbour: Haiti, Russia, South Korea, Germany, Portugal, Ireland, Canada, Brazil, Denmark, Austria, Qatar and France.
Answer: The following countries have only one neighbouring country: South Korea, Qatar, Portugal, Ireland, Haiti, Canada, and Denmark. These countries have multiple neighbours:Russia, Brazil, Germany, Austria, and France.