DataHammer/scimrc huggingface dataset:
Question: What is Ana María Gonzalez famous for
Evidence: Ana María González (born 13 April 1951) is a Spanish lyric soprano trained in Argentina, known for performing at the Teatro Colón from 1973 to 1994. Although associated with the Italian lyric repertoire, she also excelled in lyric soprano roles of the French repertoire such as Manon, Marguerite, and Juliette, which brought her international fame.
Answer: Ana María Gonzalez (born 13 April 1951) is a Spanish lyric soprano trained in Argentina, known for performing at the Teatro Colón from 1973 to 1994. Although associated with the Italian lyric repertoire, she also excelled in lyric soprano roles of the French repertoire such as Manon, Marguerite, and Juliette, which brought her international fame.
Question: What is Ford
Answer: Ford is an American auto manufacturer that is based in Detroit, Michigan. Ford is one of the most popular car brands in the United States and has been since it's inception. It's most popular vehicle is the F150 truck
Question: Was Richard Nixon born in California?
Evidence: Richard Milhous Nixon was born on January 9, 1913, in what was then the township precinct of Yorba Linda, California, in a house built by his father, located on his family's lemon ranch
Answer: Richard Nixon was born in Yorba Linda, CA which is located in Orange County.
Question: What do the authors focus on in this paper?
Evidence: In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new dataset of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public.
Answer: In this paper the authors focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public.
Question: What is a good way to convince someone to do something
Answer: There are different ways to convince someone to take an action:
1) Provide objective, verifiable facts or data that support the requested action
2) Share a relatable story or anecdote that helps emotionally connect to the desired action
3) Use the personal rapport that you have built with the person to convince them
4) Explain how your counterpart will personally benefit from taking the action
5) Help the person understand that the action is easy, quick or low effort
6) Propose an exchange, where you do something in the interest of the person in exchange for their action
7) Pay the person for taking the action
Depending on their personality, the action, and your relationship with the person different methods may work better.
Question: Extract the teams that Dhoni has played for list them with commas. Also extract the famous wins that Dhoni has led his team to, and place them on a bullet list.
Evidence: Mahendra Singh Dhoni (/məˈheɪndrə ˈsɪŋ dhæˈnɪ/ (listen); born 7 July 1981), commonly known as MS Dhoni, is a former Indian cricketer and captain of the Indian national team in limited-overs formats from 2007 to 2017 and in Test cricket from 2008 to 2014, who plays as a wicket-keeper. He is also the current captain of Chennai Super Kings in the Indian Premier League. Under his captaincy, India won the 2007 ICC World Twenty20, the 2011 Cricket World Cup, and the 2013 ICC Champions Trophy, the most by any captain, where in the 2011 final, he hit the match winning six. He also led India to victory in the 2010 and 2016 Asia Cup. Additionally, under his leadership, India won the 2010 and 2011 ICC Test Mace and 2013 ICC ODI Championship. Dhoni is a right-handed wicket-keeper batsman known for his calm captaincy and his ability to finish matches in tight situations. Mahi scored 17,266 runs in International Cricket (including 10,000 plus runs in ODI Internationals) and is considered one of the best finishers in the game. He is also one of the greatest wicket-keepers and captains in the history of cricket.
Answer: Teams: 

India, Chennai Super Kings

Famous wins:

•	2007 ICC World Twenty20
•	2011 Cricket World Cup
•	2013 ICC Champions Trophy
•	2010 Asia Cup
•	2016 Asia Cup
•	2010 ICC Test Mace
•	2016 ICC Test Mace
•	2013 UCC ODI Championship
Question: What are some physical health benefits from doing strength training exercises?
Evidence: Strength training also provides functional benefits. Stronger muscles improve posture, provide better support for joints, and reduce the risk of injury from everyday activities.

Progressive resistance training may improve function, quality of life and reduce pain in people at risk of fracture, with rare adverse effects. Weight-bearing exercise also helps to prevent osteoporosis and to improve bone strength in those with osteoporosis. For many people in rehabilitation or with an acquired disability, such as following stroke or orthopaedic surgery, strength training for weak muscles is a key factor to optimize recovery.
Answer: Some examples of physical benefits from strength training include improve posture, better joint support, and reduced risk of injury from everyday activities. In addition, strength training exercises have also been shown to help prevent osteoporosis and improve overall bone strength.
Question: Do they quantitavely or qualitatively evalute the output of their low-rank approximation to verify the grouping of lexical items?
Evidence: In this section, we evaluate the proposed method intrinsically in terms of whether the co-occurrence matrix after the low-rank approximation is able to capture similar concepts on student response data sets, and also extrinsically in terms of the end task of summarization on all corpora. In the following experiments, summary length is set to be the average number of words in human summaries or less. For the matrix completion algorithm, we perform grid search (on a scale of [0, 5] with stepsize 0.5) to tune the hyper-parameter INLINEFORM0 (Eq. EQREF10 ) with a leave-one-lecture-out (for student responses) or leave-one-task-out (for others) cross-validation.
The results are shown in Table TABREF25 . INLINEFORM0 significantly on all three courses. That is, a bigram does receive a higher partial score in a sentence that contains similar bigram(s) to it than a sentence that does not. Therefore, H1.a holds. For H1.b, we only observe INLINEFORM1 significantly on Stat2016 and there is no significant difference between INLINEFORM2 and INLINEFORM3 on the other two courses. First, the gold-standard data set is still small in the sense that only a limited portion of bigrams in the entire data set are evaluated. Second, the assumption that phrases annotated by different colors are not necessarily unrelated is too strong. For example, “hypothesis testing" and “H1 and Ho conditions" are in different colors in the example of Table TABREF15 , but one is a subtopic of the other. An alternative way to evaluate the hypothesis is to let humans judge whether two bigrams are similar or not, which we leave for future work. Third, the gold standards are pairs of semantically similar bigrams, while matrix completion captures bigrams that occurs in a similar context, which is not necessarily equivalent to semantic similarity. For example, the sentence “graphs make it easier to understand concepts" in Table TABREF25 is associated with “hard to".
In this section, we evaluate the proposed method intrinsically in terms of whether the co-occurrence matrix after the low-rank approximation is able to capture similar concepts on student response data sets, and also extrinsically in terms of the end task of summarization on all corpora. In the following experiments, summary length is set to be the average number of words in human summaries or less.
An alternative way to evaluate the hypothesis is to let humans judge whether two bigrams are similar or not, which we leave for future work.
Answer: They evaluate quantitatively.
Question: What are some of the top risks around LLMs?
Answer: 1. Hallucinations: LLMs sometimes make up facts and responses which are false.
2. Toxicity and bias: LLMs reflect the data they were trained on, so they may generate responses with toxic or biased content.
3. Job losses: More generally, many people worry that LLMs and AI will displace humans in some jobs, though similar historical breakthroughs indicate that jobs will likely change but not be lost overall.
4. Malicious AI: As LLMs and AI approach Artifical General Intelligence (AGI), some people fear that an AGI may become a threat to some or all humans.
5. Environmental impact: LLMs and big AI models require lots of energy to train, and therefore have big carbon footprints.
Question: What is Photosynthesis?
Answer: Photosynthesis is a process by which plants use sunlight, water, and carbon dioxide to create oxygen and energy in the form of sugar.
Question: Is there a fictional book and tv show by the name Shantaram?
Answer: yes
Question: From the passage note down the Well-known international brands of Germany. List the results in comma separated format.
Evidence: Germany is a federal parliamentary republic in central-western Europe. Germany has the world's 4th largest economy by nominal GDP, and the 5th largest by PPP. As a global leader in several industrial and technological sectors, it is both the world's third-largest exporter and importer of goods.

Of the world's 500 largest stock-market-listed companies measured by revenue in 2017, the Fortune Global 500, 29 are headquartered in Germany. 30 Germany-based companies are included in the DAX, the German stock market index. Well-known international brands include Mercedes-Benz, BMW, SAP, Volkswagen, Audi, Siemens, Allianz, Adidas, Porsche, Deutsche Bahn, Deutsche Bank and Bosch.

Germany is recognised for its large portion of specialised small and medium enterprises, known as the Mittelstand model. Around 1,000 of these companies are global market leaders in their segment and are labelled hidden champions.
Answer: Mercedes-Benz, BMW, SAP, Volkswagen, Audi, Siemens, Allianz, Adidas, Porsche, Deutsche Bahn, Deutsche Bank, Bosch
Question: How do they ensure the generated questions are unanswerable?
Evidence: To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. As shown in Figure 1 , the answerable and unanswerable questions of a paragraph are aligned through the text span “Victoria Department of Education” for being both the answer and plausible answer. These two questions are lexically similar and both asked with the same answer type in mind. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc. Consequently, we can generate a mass of unanswerable questions with existing large-scale machine reading comprehension datasets.
To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions.
In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.
Answer: The answers are shown as follows:
* learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc
Question: Extract the year when Hong Kong was established
Evidence: Hong Kong (US: /ˈhɒŋkɒŋ/ or UK: /hɒŋˈkɒŋ/; Chinese: 香港, Cantonese: [hœ́ːŋ.kɔ̌ːŋ] (listen)), officially the Hong Kong Special Administrative Region of the People's Republic of China (abbr. Hong Kong SAR or HKSAR), is a city and special administrative region of China on the eastern Pearl River Delta in South China. With 7.5 million residents of various nationalities in a 1,104-square-kilometre (426 sq mi) territory, Hong Kong is one of the most densely populated places in the world. Hong Kong is also a major global financial centre and one of the most developed cities in the world.

Hong Kong was established as a colony of the British Empire after the Qing Empire ceded Hong Kong Island from Xin'an County at the end of the First Opium War in 1841 then again in 1842. The colony expanded to the Kowloon Peninsula in 1860 after the Second Opium War and was further extended when Britain obtained a 99-year lease of the New Territories in 1898. British Hong Kong was occupied by Imperial Japan from 1941 to 1945 during World War II; British administration resumed after the surrender of Japan. The whole territory was transferred to China in 1997. As one of China's two special administrative regions (the other being Macau), Hong Kong maintains separate governing and economic systems from that of mainland China under the principle of "one country, two systems".

Originally a sparsely populated area of farming and fishing villages, the territory has become one of the world's most significant financial centres and commercial ports. As of 2021, it is the world's ninth-largest exporter and eight-largest importer. Hong Kong has a market economy characterised by a focus on services, low taxation and free trade; its currency, the Hong Kong dollar, is the eighth most traded currency in the world. Hong Kong is home to the third-highest number of billionaires of any city in the world, the second-highest number of billionaires of any city in Asia, and the largest concentration of ultra high-net-worth individuals of any city in the world. Although the city has one of the highest per capita incomes in the world, severe income inequality exists among the population. Most notably, housing in Hong Kong has been well-documented to experience a chronic persistent shortage; the extremely compact house sizes and the extremely high housing density are the effects of Hong Kong's housing market being the most expensive housing in the world.

Hong Kong is a highly developed territory and has a Human Development Index (HDI) of 0.952, ranking fourth in the world. The city has the largest number of skyscrapers of any city in the world, and its residents have some of the highest life expectancies in the world. The dense space has led to a highly developed transportation network with public transport rates exceeding 90%. Hong Kong is ranked 3rd in the Global Financial Centres Index.
Answer: Hong Kong was established in 1841 as a colony of the British Empire after the Qing Empire ceded Hong Kong Island from Xin'an County at the end of the First Opium War.
Question: What patterns do they observe about how user engagement varies with the characteristics of a community?
Evidence: Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community—the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )—vary according to the type of collective identity it fosters. We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.
More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). Interestingly, while established members of distinctive communities more avidly respond to temporal updates than newcomers, in more generic communities it is the outsiders who engage more with volatile content, perhaps suggesting that such content may serve as an entry-point to the community (but not necessarily a reason to stay). Such insights into the relation between collective identity and user engagement can be informative to community maintainers seeking to better understand growth patterns within their online communities.
We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.
More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). 
Answer: The answers are shown as follows:
* communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members
* within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers
Question: Classify each of the following as ales or lagers: helles, IPA, doppelbock, gueuze, hefeweizen, Porter, stout
Answer: Lager, ale, lager, ale, ale, ale, ale.
Question: Why is Steph Curry such a good basketball player?
Answer: Stephen Curry, of the Golden State Warriors is considered one of the best basketball players in the NBA because of his ability to make shots from anywhere on the basketball court. He has been considered one of the greatest of all time because of how transformative his play-style is, opting to shoot three-pointers opposed to higher percentage lay-ups. He is a 4-time champion, a 2-time MVP, a Finals MVP, has made the most three-pointers in NBA history, and global brand leader for Under Armour.
Question: Name five different cheeses.
Answer: Five different cheeses are mozzarella, parmesan, gouda, cheddar, and gruyere.
Question: Name three different cuts of steak from a cow.
Answer: Three different cuts of steak from a cow are ribeye, sirloin, and filet.
Question: Why do I not feel good after consuming a lot of chocolates?
Answer: Eating sugary foods like chocolate or candy causes a spike in dopamine, which can make you feel happy and energized. However, when the dopamine levels crash, it goes well below the base level, which can in turn make you may feel tired, irritable, and even depressed. This is because dopamine is involved in regulating mood, motivation, and pleasure. When dopamine levels drop too low, it can cause a number of unpleasant symptoms.
Question: What type of people annotate ANNODIS?
Evidence: In this first exploratory work, our tests considered only documents in French from the Annodis corpus. Annodis (ANNOtation DIScursive) is a set of documents in French that were manually enriched with notes of discursive structures. Its main characteristics are:
Two annotations: Rhetorical relations and multilevel structures.
Documents (687 000 words) taken from four sources: the Est Républicain newspaper (39 articles, 10 000 words); Wikipedia (30 articles + 30 summaries, 242 000 words); Proceedings of the conference Traitement Automatique des Langues Naturelles (TALN) 2008 (25 articles, 169 000 words); Reports from Institut Français de Relations Internationales (32 raports, 266 000 words).
The corpora were noted using Glozz.
Annodis aims at building an annotated corpus. The proposed annotations are on two levels of analysis, that is, two perspectives:
Ascendant: part of EDU are used in the construction of more complex structures, through the relations of discourse;
Descending: approaches the text in its entirety and relies on the various shallow indices to identify high-level discursive structures (macro structures).
Two types of persons annotated Annodis: linguistic experts and students. The first group constituted a $E$ subcorpus called “specialist” and the second group resulted in a $N$ subcorpus called “naive”. These rhetorically annotated subcorps were used as references in our experiments. (c.f. §SECREF6).
Two types of persons annotated ANNODIS: linguistic experts and students. The first group constituted a E subcorpus called “specialist” and the second group resulted in a N subcorpus called “naive”.
Answer: Linguistic experts and students.
Question: Why german cars are best?
Answer: 1. They are perfect build quality
2. Engine is refined.
Question: Give me a list of sports talk shows to watch while I make dinner.
Answer: There are several channels that have sports talk shows on around dinner time. On Fox Sports 1, there is First Things First and Undisputed with Skip and Shannon. On ESPN, there is First Take, Pardon the Interruption, and Around the Horn.
Question: In which domain does this paper highlight the application of Information Extraction?
Evidence: The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases.
The main problems in BioIE are similar to those in Information Extraction:
This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain.
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .
The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE).
Answer: Biomedical domain.
Question: What is the University of Texas mascot?
Answer: The University of Texas mascot is a longhorn.