DataHammer/scimrc huggingface dataset:
Question: In the paragraph below, what are the major sources of methane emissions?
Evidence: Greenhouse gas emissions from human activities strengthen the greenhouse effect, contributing to climate change. Most is carbon dioxide from burning fossil fuels: coal, oil, and natural gas. The largest emitters include coal in China and large oil and gas companies. Human-caused emissions have increased atmospheric carbon dioxide by about 50% over pre-industrial levels. The growing levels of emissions have varied, but have been consistent among all greenhouse gases (GHGs). Emissions in the 2010s averaged 56 billion tons a year, higher than any decade before.

Electricity generation, heat and transport are major emitters; overall energy is responsible for around 73% of emissions. Deforestation and other changes in land use also emit carbon dioxide and methane. The largest source of anthropogenic methane emissions is agriculture, closely followed by gas venting and fugitive emissions from the fossil-fuel industry. The largest agricultural methane source is livestock. Agricultural soils emit nitrous oxide partly due to fertilizers. Similarly, fluorinated gases from refrigerants play an outsized role in total human emissions.
Answer: The largest source of methane emissions is agriculture. Gas venting and emissions from the fossil-fuel industry are the next major sources.
Question: What can insight-driven computational text analysis benefit for social and cultural questions?
Evidence: Insight-driven computational analysis of text is becoming increasingly common. It not only helps us see more broadly, it helps us see subtle patterns more clearly and allows us to explore radical new questions about culture and society. In this article we have consolidated our experiences, as scholars from very different disciplines, in analyzing text as social and cultural data and described how the research process often unfolds. Each of the steps in the process is time-consuming and labor-intensive. Each presents challenges. And especially when working across disciplines, the research often involves a fair amount of discussion—even negotiation—about what means of operationalization and approaches to analysis are appropriate and feasible. And yet, with a bit of perseverance and mutual understanding, conceptually sound and meaningful work results so that we can truly make use of the exciting opportunities rich textual data offers.
Insight-driven computational analysis of text is becoming increasingly common. It not only helps us see more broadly, it helps us see subtle patterns more clearly and allows us to explore radical new questions about culture and society.
Answer: It not only helps them see more broadly, it helps them see subtle patterns more clearly and allows them to explore radical new questions about culture and society.
Question: How comes a free-diver does not get crushed by the pressure when diving?
Answer: Every 10 meters under water, the pressure increases by 1 bar, which is approximately one standard atmosphere unit.
This means for example at 50 meters deep, the pressure the diver is receiving is six times higher than at the surface.
For most of the body, composed of tissues and fluids, this is not a problem as they are incompressible.
However, the air contained in the lungs being pressurised will reduce in volume inversely proportionally to the pressure. 
If the individual dives with a tank, he will be able to inflate his lungs, but if the diver is free-diving, the volume of air is limited (being the one taken at the surface).
In our previous example, the volume contained in the lungs would be divided by 6, which is in principle below their residual volume.
This means lungs will not be able to deform themselves anymore, and a depression will appear in the thorax of the free-diver
hence risking the lungs to collapse.
The latter would though not happen, as the depression created in the lungs from not being able to get deformed anymore will generate an influx of blood that strengthen the lungs structure, protecting them from being damaged.
Question: How many games did Edwin play in the NBA?
Evidence: Edward Arnet Johnson (born May 1, 1959) is an American former professional basketball player who spent 17 seasons in the National Basketball Association (NBA) and a year in the Greek Basket League midway through his career. With nearly 1,200 games in the NBA, he scored the second-most career points among players who never played in an NBA All-Star Game, behind Jamal Crawford. As of the 2018–19 season, he is ranked 52nd in all-time points scored, and ranked fifth-most in points scored by an eligible player not in the Naismith Memorial Basketball Hall of Fame. As of 2019 he is the co-host of NBA Today, which airs weekdays on Sirius XM NBA Radio and also is the play-by-play TV analyst for the Phoenix Suns on Bally Sports Arizona.
Answer: Edwin played almost 1,200 games in the NBA.
Question: Which metrics do they use to evaluate matching?
Evidence: For both datasets, we follow the evaluation metrics used in the original evaluation tasks BIBREF13 . For DBQA, P@1 (Precision@1), MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) are adopted. For KBRE, since only one golden candidate is labeled for each question, only P@1 and MRR are used.
For both datasets, we follow the evaluation metrics used in the original evaluation tasks BIBREF13 . For DBQA, P@1 (Precision@1), MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) are adopted. For KBRE, since only one golden candidate is labeled for each question, only P@1 and MRR are used.
Answer: The answers are shown as follows:
* Precision@1
* Mean Average Precision
* Mean Reciprocal Rank
Question: In what year did Singapore become an independent sovereign country?
Evidence: Singapore, officially the Republic of Singapore, is an island country and city-state in maritime Southeast Asia. It lies about one degree of latitude (137 kilometres or 85 miles) north of the equator, off the southern tip of the Malay Peninsula, bordering the Strait of Malacca to the west, the Singapore Strait to the south, the South China Sea to the east, and the Straits of Johor to the north. The country's territory is composed of one main island, 63 satellite islands and islets, and one outlying islet; the combined area of these has increased by 25% since the country's independence as a result of extensive land reclamation projects. It has the third highest population density in the world. With a multicultural population and recognising the need to respect cultural identities of the major ethnic groups within the nation, Singapore has four official languages: English, Malay, Mandarin, and Tamil. English is the lingua franca and numerous public services are available only in English. Multi-racialism is enshrined in the constitution and continues to shape national policies in education, housing, and politics.

Singapore's history dates back at least a millennium, having been a maritime emporium known as Temasek and subsequently as a major constituent part of several successive thalassocratic empires. Its contemporary era began in 1819 when Stamford Raffles established Singapore as an entrepôt trading post of the British Empire. In 1867, the colonies in Southeast Asia were reorganised and Singapore came under the direct control of Britain as part of the Straits Settlements. During World War II, Singapore was occupied by Japan in 1942, and returned to British control as a separate Crown colony following Japan's surrender in 1945. Singapore gained self-governance in 1959 and in 1963 became part of the new federation of Malaysia, alongside Malaya, North Borneo, and Sarawak. Ideological differences, most notably the perceived encroachment of the egalitarian "Malaysian Malaysia" political ideology led by Lee Kuan Yew into the other constituent entities of Malaysia—at the perceived expense of the bumiputera and the policies of Ketuanan Melayu—eventually led to Singapore's expulsion from the federation two years later; Singapore became an independent sovereign country in 1965.
Answer: 1965
Question: Are there rules that you should live by to be happy?
Answer: There are 6 rules of life that work like laws of nature and can help you live a happier and more content life. It doesn't matter if you like or understand the rules or if you want to follow them or not. It is like with the gravity - it is always valid. If a stone falls on your foot, it hurts, whether you like this rule or not. The 6 rules of life are to be understood in the same way - you can observe and apply them or you can ignore them, it doesn't matter for the rules, they are valid in any case and will have an effect on your life, no matter how you estimate it. The 6 rules are: 1. you have only x-days to live, you decide what to do with it. 2. accept what is, judging is voluntary. 3. your life knows only the now. 4. you can only change one person: Yourself. 5. learn to love the most important person: you. 6. your brain is the ultimate power: use it with intention.
Question: List the ten episode titles from the sixth season of "Game of Thrones" in broadcast order.
Answer: The Red Woman, Home, Oathbreaker, Book of the Stranger, The Door, Blood of My Blood, The Broken Man, No One, Battle of the Bastards, The Winds of Winter
Question: Classify each of the following as either a city or food: Edinburgh, Madrid, pasta, jam, Berlin, soup
Answer: Cities: Edinburgh, Madrid, Berlin Food: pasta, jam, soup
Question: Which of these books were written by James Michener and which were written by Ian McEwan: Tales of the South Pacific, Saturday, Atonement, The Children Act, Hawaii, Texas, On Chesil Beach, and Alaska.
Answer: The books by James Michener are Tales of the South Pacific, Hawaii, Texas, and Alaska, and Ian McEwan wrote Saturday, Atonement, The Children Act, and On Chesil Beach.
Question: What's model does the paper propose?
Evidence: Semantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and other NLP tasks. SRL is the problem of finding predicate-argument structure in a sentence, as illustrated below:
 INLINEFORM0 
Here, the predicate WRITE has two arguments: `Mike' as A0 or the writer, and `a book' as A1 or the thing written. The labels A0 and A1 correspond to the PropBank annotations BIBREF0 .
As the need for SRL arises in different domains and languages, the existing manually annotated corpora become insufficient to build supervised systems. This has motivated work on unsupervised SRL BIBREF1 , BIBREF2 , BIBREF3 . Previous work has indicated that unsupervised systems could benefit from the word alignment information in parallel text in two or more languages BIBREF4 , BIBREF5 , BIBREF6 . For example, consider the German translation of sentence INLINEFORM0 :
 INLINEFORM0 
If sentences INLINEFORM0 and INLINEFORM1 have the word alignments: Mike-Mike, written-geschrieben, and book-Buch, the system might be able to predict A1 for Buch, even if there is insufficient information in the monolingual German data to learn this assignment. Thus, in languages where the resources are sparse or not good enough, or the distributions are not informative, SRL systems could be made more accurate by using parallel data with resource rich or more amenable languages.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages. The model consists of individual Bayesian models for each language BIBREF3 , and crosslingual latent variables to incorporate soft role agreement between aligned constituents. This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs BIBREF4 . We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
We begin by briefly describing the unsupervised SRL pipeline and the monolingual semantic role induction model we use, and then describe our multilingual model.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages.
Answer: A joint Bayesian mode.
Question: Can you write me a summary on this page then tell me who and how much traffic is drawn to this page from which regions around the world?
Evidence: The Salvation Army (TSA) is a Protestant Christian church and an international charitable organization headquartered in London, England that is aligned with the Wesleyan-Holiness movement. The organisation reports a worldwide membership of over 1.7 million, comprising soldiers, officers and adherents collectively known as Salvationists. Its founders sought to bring salvation to the poor, destitute, and hungry by meeting both their "physical and spiritual needs". It is present in 133 countries, running charity shops, operating shelters for the homeless and disaster relief, and humanitarian aid to developing countries.

The theology of the Salvation Army is derived from Methodism, although it is distinctive in institution and practice. A distinctive characteristic of the Salvation Army is its use of titles derived from military ranks, such as "lieutenant" or "major". It does not celebrate the rites of Baptism and Holy Communion. However, the Army's doctrine is aligned with the Wesleyan–Arminian tradition, particularly the holiness movement. The Army's purposes are "the advancement of the Christian religion... of education, the relief of poverty, and other charitable objects beneficial to society or the community of mankind as a whole".

The Army was founded in 1865 in London by one-time Methodist preacher William Booth and his wife Catherine as the East London Christian Mission, and can trace its origins to the Blind Beggar tavern. In 1878, Booth reorganised the mission, becoming its first General and introducing the military structure which has been retained as a matter of tradition. Its highest priority is its Christian principles. The current international leader of The Salvation Army and chief executive officer (CEO) is General Brian Peddle, who was elected by the High Council of The Salvation Army on 3 August 2018.

In 2011, Charity Watch rated the Salvation Army an "A−" to an "A", indicating a high level of financial efficiency and organisational transparency. The Salvation Army has received an A-rating from the American Institute of Philanthropy.

The Salvation Army is one of the world's largest providers of social aid,[citation needed] with expenditures including operating costs of $2.6 billion in 2004, helping more than 32 million people in the U.S. alone. In addition to community centres and disaster relief, the organisation does work in refugee camps, especially among displaced people in Africa. In the United Kingdom, the Salvation Army is no longer the largest nongovernmental provider of social services; however, it still provides a significant service to people in need. The Salvation Army is the fourth largest charity in the United States, with private donations in excess of $2 billion in 2022, and is a member of the American organisation Christian Churches Together.
Answer: I dont understand what long answer is but here is the link to the wilki page : https://en.wikipedia.org/wiki/The_Salvation_Army
Question: You, a commercial gym supervisor, are supervising the resistance training area when you hear a lot of clanging noise by the seated leg press machine. You observe a participant not controlling the descent of the weights. You suggest he slowly return the weights rather than letting them drop quickly. A day or two later, he reports that he felt more sore than usual after that workout. What accounts for this? Is it safe for him to exercise today?
Answer: He is feeling more sore than usual because likely the weight was too heavy for him, and he was not able to control the descent of the weights properly, leading him to overexert himself in the exercise. He may want to rest a bit longer before returning to workout as normal, as he does not want to injure himself in the long run. In the process of not performing the seated leg press as he should (in a safe manner), he risks overusing his muscles, and maybe even tearing a muscle or ligament in the long run.
Question: What is considered as a student reflection document in this paper?
Evidence: Student reflections are comments provided by students in response to a set of instructor prompts. The prompts are directed towards gathering students' feedback on course material. Student reflections are collected directly following each of a set of classroom lectures over a semester. In this paper, the set of reflections for each prompt in each lecture is considered a student reflection document. The objective of our work is to provide a comprehensive and meaningful abstractive summary of each student reflection document. Our dataset consists of documents and summaries from four course instantiations: ENGR (Introduction to Materials Science and Engineering), Stat2015 and Stat2016 (Statistics for Industrial Engineers, taught in 2015 and 2016, respectively), and CS (Data Structures in Computer Science). All reflections were collected in response to two pedagogically-motivated prompts BIBREF16: “Point of Interest (POI): Describe what you found most interesting in today's class” and “Muddiest Point (MP): Describe what was confusing or needed more detail.”
For each reflection document, at least one human (either a TA or domain expert) created summaries. Table TABREF4 shows example reference summary produced by one annotator for the CS course. Table TABREF5 summarizes the dataset in terms of number of lectures, number of prompts per lecture, average number of reflections per prompt, and number of abstractive reference summaries for each set of reflections.
In this paper, the set of reflections for each prompt in each lecture is considered a student reflection document.
Answer: In this paper, the set of reflections for each prompt in each lecture is considered a student reflection document.
Question: Are the similarity properties between languages preserved by language vectors?
Evidence: We now take a look at the language vectors found during training with the full model of 990 languages. fig:germanic shows a hierarchical clustering of the subset of Germanic languages, which closely matches the established genetic relationships in this language family. While our experiments indicate that finding more remote relationships (say, connecting the Germanic languages to the Celtic) is difficult for the model, it is clear that the language vectors preserves similarity properties between languages.
In additional experiments we found the overall structure of these clusterings to be relatively stable across models, but for very similar languages (such as Danish and the two varieties of Norwegian) the hierarchy might differ, and the some holds for languages or groups that are significantly different from the major groups. An example from fig:germanic is English, which is traditionally classified as a West Germanic language with strong influences from North Germanic as well as Romance languages. In the figure English is (weakly) grouped with the West Germanic languages, but in other experiments it is instead weakly grouped with North Germanic.
While our experiments indicate that finding more remote relationships (say, connecting the Germanic languages to the Celtic) is difficult for the model, it is clear that the language vectors preserves similarity properties between languages.
Answer: Yes.
Question: Which of the following are prime numbers: 7, 12, 23, 33, 64, 79, 97, and 100.
Answer: The numbers 7, 23, 79, and 97 are prime numbers.  The numbers 12, 33, 64, and 100 are not.
Question: Where should I travel for a summer vacation in the United States?
Answer: Most will say travel to Florida for summer vacation but there are so many other options in the United States. Charleston, South Carolina is a historic city with warm weather, beaches and great food. San Diego, California is another great summer location on the Pacific Ocean with delicious food and a relaxed vibe.
Question: Please give me 3 reasons why Ronaldo is the best player in the world from this text
Evidence: Cristiano Ronaldo dos Santos Aveiro GOIH ComM (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Professional League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards[note 3] and four European Golden Shoes, the most by a European player. He has won 32 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140), and assists (42) in the Champions League, goals in the European Championship (14), men's international goals (122), and men's international appearances (198). He is one of the few players to have made over 1,100 professional career appearances, and has scored over 800 official senior career goals for club and country.
Answer: Ronaldo has won more Ballon d'Or awards than any other European plan. He has made more than 1100 professional appearances and score more than 800 official senior goals.
Question: How does the temperature affect the speed of sound?
Answer: The speed of sound increases with increasing temperature. This is because the particles in the medium move faster and can transmit the sound waves more quickly.
Question: Can the model proposed by the team generate more than one questions?
Evidence: Another interesting observation is that for the same answer-sentence pair, our model can generate diverse questions by taking different answer-relevant relations as input. Such capability improves the interpretability of our model because the model is given not only what to be asked (i.e., the answer) but also the related fact (i.e., the answer-relevant relation) to be covered in the question. In contrast, proximity-based answer-aware models can only generate one question given the sentence-answer pair regardless of how many answer-relevant relations in the sentence. We think such capability can also validate our motivation: questions should be generated according to the answer-aware relations instead of neighboring words of answer fragments. Figure FIGREF45 show two examples of diverse question generation. In the first case, the answer fragment `Hugh L. Dryden' is the appositive to `NASA Deputy Administrator' but the subject to the following tokens `announced the Apollo program ...'. Our framework can extract these two answer-relevant relations, and by feeding them to our model separately, we can receive two questions asking different relations with regard to the answer.
Another interesting observation is that for the same answer-sentence pair, our model can generate diverse questions by taking different answer relevant relations as input.
Answer: Yes.
Question: Which two people are depicted by carved heads near the windows?
Evidence: St. John the Baptist, Blackrock is a Roman Catholic church in the parish of Blackrock, Ireland. The church is still in use and named after the Saint John the Baptist. It is located on Temple Road, Blackrock, County Dublin.

The church was designed by the architect Patrick Byrne, who was educated at the Dublin Society Schools. It is one of the finest examples of the Gothic revival style in Ireland and was the first to be built in the Dublin Archdiocese. It is said to be inspired by the ideas of Augustus Welby Pugin (1812-1852).

The initial building consisted of the nave, chancel and bell tower. To the rear of the church, two carved heads can be seen up high on either side of the rose window. The head on the left depicts St. John the Baptist, while the head on the right depicts the Archbishop Daniel Murray.
Answer: St. John the Baptist and the Archbishop Daniel Murray