DataHammer/scimrc huggingface dataset:
Question: What is the Turkish drink that tastes similar to
Evidence: Ouzo is a clear liquid. However, when water or ice is added, ouzo turns a milky-white colour. This is because anethole, the essential oil of anise, is completely soluble in alcohol at approximately 38% ABV and above, but not in water. Diluting the spirit causes it to separate, creating an emulsion whose fine droplets scatter the light. This process is called louching and is also seen while preparing absinthe.

The drinks with a similar flavour of Ouzu include sambuca from (Italy), pastis from (France), oghi (from Armenia), rakı from Turkey, and arak (from the Levant). Its aniseed flavour is also similar to the anise-flavoured liqueur of anís (Spain) and the stronger spirits of absinthe (France and Switzerland). Aguardiente (Latin America), made from sugar cane, is also similar. The Italian drink Pallini Mistra, named after the Greek city of Mystras in the Peloponnese is a version of ouzo made in Rome that closely resembles Greek and Cypriot ouzo.
Answer: Rakı is a drink from Turkey that has similar flavour with Ouzo.
Question: What is the most popular wine varietal produced in California?
Answer: California is best known for its white wine, Chardonnay, and its red wine, Cabernet Sauvignon.
Question: What is the size of the dataset?
Evidence: We had two human annotators who were trained on snippets (i.e., (suspected trolling attempt, responses) pairs) taken from 200 conversations and were allowed to discuss their findings. After this training stage, we asked them to independently label the four aspects for each snippet. We recognize that this limited amount of information is not always sufficient to recover the four aspects we are interested in, so we give the annotators the option to discard instances for which they couldn't determine the labels confidently. The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens. The distribution over the classes per trolling aspect is shown in the table TABREF19 in the column “Size”.
The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens. 
Answer: The answers are shown as follows:
* 1000 conversations composed of 6833 sentences and 88047 tokens
Question: When and where was the first major agitation led by Gandhi, tell me who this agitation was against,  which crop led to this agitation and why this agitation happened.
Evidence: Gandhi's first major achievement came in 1917 with the Champaran agitation in Bihar. The Champaran agitation pitted the local peasantry against largely Anglo-Indian plantation owners who were backed by the local administration. The peasants were forced to grow indigo (Indigofera sp.), a cash crop for Indigo dye whose demand had been declining over two decades, and were forced to sell their crops to the planters at a fixed price. Unhappy with this, the peasantry appealed to Gandhi at his ashram in Ahmedabad. Pursuing a strategy of nonviolent protest, Gandhi took the administration by surprise and won concessions from the authorities.
Answer: First major agitation was in 1917 in Champaran Bihar state. The agitation was against Anglo-Indian plantation owners. Indigo was the crop that led to this agitation and the reason was the lower fixed price for the Indigo crop.
Question: What method does the paper propose?
Evidence: Currently, probabilistic topic models are important tools for improving automatic text processing including information retrieval, text categorization, summarization, etc. Besides, they can be useful in supporting expert analysis of document collections, news flows, or large volumes of messages in social networks BIBREF0 , BIBREF1 , BIBREF2 . To facilitate this analysis, such approaches as automatic topic labeling and various visualization techniques have been proposed BIBREF1 , BIBREF3 .
Boyd-Graber et al. BIBREF4 indicate that to be understandable by humans, topics should be specific, coherent, and informative. Relationships between the topic components can be inferred. In BIBREF1 four topic visualization approaches are compared. The authors of the experiment concluded that manual topic labels include a considerable number of phrases; users prefer shorter labels with more general words and tend to incorporate phrases and more generic terminology when using more complex network graph. Blei and Lafferty BIBREF3 visualize topics with ngrams consisting of words mentioned in these topics. These works show that phrases and knowledge about hyponyms/hypernyms are important for topic representation.
In this paper we describe an approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics. The structure of the paper is as follows. In Section 2 we consider related works. Section 3 describes the proposed approach. Section 4 enumerates automatic quality measures used in experiments. Section 5 presents the results obtained on several text collections according to automatic measures. Section 6 describes the results of manual evaluation of combined topic models for Islam Internet-site thematic analysis.
In this paper we describe an approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics.
Answer: An approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics.
Question: Extract the ingredients needed to make pasta carbonara. Separate them with a comma.
Evidence: Carbonara (Italian: [karboˈnaːra]) is a Roman pasta dish made with eggs, hard cheese, cured pork and black pepper. The dish took its modern form and name in the middle of the 20th century.

The cheese is usually Pecorino Romano, Parmigiano-Reggiano, or a combination of the two. Spaghetti is the most common pasta, but fettuccine, rigatoni, linguine, or bucatini are also used. Normally guanciale or pancetta are used for the meat component,] but lardons of smoked bacon are a common substitute outside Italy.
Answer: Eggs, hard cheese, cured pork, black pepper
Question: Tell me whether each of these dishes are served cold or hot: pizza, pasta, tiramisu, ice cream, ribs, steak, ceviche
Answer: Pizza: Hot
Pasta: Hot
Tiramisu: Cold
Ice Cream: Cold
Ribs: Hot
Steak: Hot
Ceviche: Cold
Question: Provide a list of ways musicians can earn income from their music craft.
Answer: Paid live music performances

Earning music royalties from your recorded music when it is commercially played, performed or streamed

Getting paid music sync licensing fees for your recorded music to appear in TV, film, or video games

Artist or band merchandise sales

Getting paid to provide voice or instrument lessons to others looking to learn

Paid artist endorsements or sponsorships for other products

Sale of no longer used or needed instruments or music equipment to others who can use them

Writing, recording and delivering a commissioned song for a particular buyer to commemorate a special person or occasion
Question: Name some famous rock bands from the San Francisco Bay Area.
Answer: Many famous rock bands have come from the San Francisco Bay area. These bands include the Grateful Dead, Journey, The Jefferson Airplane, Metallica, Santana, and Green Day.
Question: What is hybrid working?
Answer: Hybrid working is a term used for when you split your work time between an office building and from other locations, usually home. It became more widely used during the COVID19 pandemic when talking about where, when and how people work. Other names for Hybrid Working are Activity Based Working or Agile Working. Agile working is not to be confused with Agile project management which has different principles.
Question: List some cool places to go visit when you go to Prescott, AZ
Answer: 1. Whisky Row, Downtown Prescott
2. Jerome - copper mines
3. Sedona - Natural Waterslides
4. Off-roading 
5. Lynx Lake
6. Watson Lake
7. Gold Water Lake
8. Prescott Camp Grounds
9. Mortimer Farms
10. Tim’s Toyota Center
Question: Why is Virginia City famous?
Evidence: Virginia City is a census-designated place (CDP) that is the county seat of Storey County, Nevada, and the largest community in the county. The city is a part of the Reno–Sparks Metropolitan Statistical Area.

Virginia City developed as a boomtown with the 1859 discovery of the Comstock Lode, the first major silver deposit discovery in the United States, with numerous mines opening. The population peaked in the mid-1870s, with an estimated 25,000 residents. The mines' output declined after 1878, and the population declined as a result. As of the 2020 Census, the population of Virginia City was 787.
Answer: Virginia City is a census-designated place (CDP) and the county seat of Storey County, Nevada, and the largest community in the county. The city is a part of the Reno–Sparks Metropolitan Statistical Area.

In 1859, Virginia City developed as a boomtown with the discovery of the Comstock Lode. The Comstock Lode is the first major silver deposit discovery in the United States. Virginia City's population peaked in the mid-1870s, with an estimated 25,000 residents. The mines' output declined after 1878, and the population declined as a result. As of the 2020 Census, the population of Virginia City was only 787.
Question: Was Michael Flavin wealthy at the time he wrote Benjamin Disraeli?
Evidence: Michael Flavin's Benjamin Disraeli: The Novel as a Political Discourse suggests that Venetia was largely a commercial endeavour for Disraeli, who was deep in debt at the time that he wrote it.
Answer: No, he was deep in debt at the time.
Question: What is Web3?
Answer: Web3 (also known as Web 3.0) is an idea for a new iteration of the World Wide Web which incorporates concepts such as decentralization, blockchain technologies, and token-based economics. Some technologists and journalists have contrasted it with Web 2.0, wherein they say data and content are centralized in a small group of companies sometimes referred to as "Big Tech". The term "Web3" was coined in 2014 by Ethereum co-founder Gavin Wood, and the idea gained interest in 2021 from cryptocurrency enthusiasts, large technology companies, and venture capital firms.
Question: Tell me if the following were originally board or computer games: Scrabble, Monopoly, Doom, Counter Strike, Sorry, Super Mario Bros?
Answer: Scrabble, Monopoly, and Sorry were originally board games. Doom, Counter Strike, and Super Mario Bros were originally computer games.
Question: What are the top 3 TV shows you have binge watched and why?
Answer: Hands down, my favorite series was The Americans. As someone who grew up in the 1980's, I could identify with the clothing and hair styles, the decor, and the dearth of real-time communication options (no internet, no email, no mobile phones, etc.). The limited communication modes is what led to secret information drop locations in this era, which were a common insurance in the show. But more importantly, I remember the feeling of growing up in the time of the Cold War. We were still having scheduled air raid drills in my grammar school as often as we'd have fire drills! As children, we were convinced that nuclear war was going to happen someday, even if the adults tried to tell us that it wasn't. Growing up about 25 miles from New York City, I was pretty sure that I'd be gone within seconds of a nuclear strike to the city. My next favorite has been Yellowstone. This is a show that seems to appeal to almost everyone because it has plenty of beautiful Western scenes (Montana), family drama, and battles between flawed characters.  This series is still ongoing and I can't wait for the next set of episodes to be released! I'd have to say my third favorite is Billions. It's a series from several years ago that we just happened upon. We are in season 2 of 5 or 6 total seasons, and I am riveted by the characters, especially Bobby Axelrod (Ax), and Chuck and Wendy Rhoades. They are all so intense and engrossed in what they do, so it makes for very interesting interactions and plot twists! And I have to give an Honorable Mention to Perry Mason. While this is a very new series, I absolutely love this period drama so far. The creators have based many of the episodes on similar actual events from this period (the 1930s). It is very difficult to watch Della Street, who is insanely smart and charming, be relegated to the 'brains behind the guy' (Perry Mason) because it's the 1930s. Thanks for reading! I enjoyed brainstorming my favorites and sharing them.
Question: What is one of your favorite color?
Answer: My favorite color is purple. Purple is a great color for many reasons. The hue and tone are quite pleasing to the eye. Additionally, purple is complementary to many other colors such as green, yellow, and gray. The color purple is often a color symbolizing richness and celebration, while it is a mature color it is also lively.
Question: Where does Kieran Liam McAnespie come from?
Evidence: Kieran Liam McAnespie (born 11 September 1979) is a football manager and former player. Capable of playing as a full-back or winger, he appeared in the Scottish Premier League for St Johnstone and Hearts. Born in England, he represented the Scotland U21 national team internationally
Answer: Kieran Liam McAnespie was born in England.
Question: Name the famous place in Italy which was flooded for the 6th time in the last 1200 years.
Answer: Saint Mark's Square in Venice
Question: Can the proposed approach use multiple classifiers to detect hateful tweets effectively?
Evidence: Table TABREF5 shows the results of various methods on the hate speech detection task. Part A shows results for baseline methods. Parts B and C focus on the proposed methods where part B contains methods using neural networks only, while part C uses average of word embeddings learned by DNNs as features for GBDTs. We experimented with multiple classifiers but report results mostly for GBDTs only, due to lack of space.
As the table shows, our proposed methods in part B are significantly better than the baseline methods in part A. Among the baseline methods, the word TF-IDF method is better than the character n-gram method. Among part B methods, CNN performed better than LSTM which was better than FastText. Surprisingly, initialization with random embeddings is slightly better than initialization with GloVe embeddings when used along with GBDT. Finally, part C methods are better than part B methods. The best method is “LSTM + Random Embedding + GBDT” where tweet embeddings were initialized to random vectors, LSTM was trained using back-propagation, and then learned embeddings were used to train a GBDT classifier. Combinations of CNN, LSTM, FastText embeddings as features for GBDTs did not lead to better results. Also note that the standard deviation for all these methods varies from 0.01 to 0.025.
To verify the task-specific nature of the embeddings, we show top few similar words for a few chosen words in Table TABREF7 using the original GloVe embeddings and also embeddings learned using DNNs. The similar words obtained using deep neural network learned embeddings clearly show the “hatred” towards the target words, which is in general not visible at all in similar words obtained using GloVe.
We experimented with multiple classifiers but report results mostly for GBDTs only,due to lack of space.As the table shows, our proposed methods in part B are significantly better than the baseline methods in part A. Among the baseline methods, the word TF-IDF method is better than the character n-gram method.
Answer: Yes.
Question: Is the information about sentiments useful for their classification?
Evidence: We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS. We used an implementation of sentiment analyzer from TextBlob to get a polarity score for each sentence. Table TABREF13 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION. It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem.
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS. We used an implementation of sentiment analyzer from TextBlob1 to get a polarity score for each sentence. Table 2 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION. It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem.
Answer: No.
Question: Write a fairly boring short story about making coffee in the morning.
Answer: 6:00 AM. My alarm clocks rings and I hit the snooze. Yes, it's been "one of those weeks" and I just don't have the energy to wake up. I fall back asleep and the alarm clock rudely awakens me again at 6:10. I get out of bed and stumble into the kitchen. There is only one elixir that can rouse me from my sleepy state - coffee. 

First, I grind the beans. The coffee grinder is a bit loud, but I like the taste of freshly ground beans. Next, I put a new coffee filter into my drip coffee maker. I pour the ground beans into the filter. Then, I fill the coffee maker with 6 cups of water. I start the machine, and my coffee starts brewing. A delicious aroma fills the air, and my mood starts to improve. After 5 minutes, my cup of joe is ready. I drink it and it tastes great.
Question: What are the ten subreddits?
Evidence: Reddit: To retrieve high-quality conversational data that would likely include hate speech, we referenced the list of the whiniest most low-key toxic subreddits. Skipping the three subreddits that have been removed, we collect data from ten subreddits: r/DankMemes, r/Imgoingtohellforthis, r/KotakuInAction, r/MensRights, r/MetaCanada, r/MGTOW, r/PussyPass, r/PussyPassDenied, r/The_Donald, and r/TumblrInAction. For each of these subreddits, we retrieve the top 200 hottest submissions using Reddit's API. To further focus on conversations with hate speech in each submission, we use hate keywords BIBREF6 to identify potentially hateful comments and then reconstructed the conversational context of each comment. This context consists of all comments preceding and following a potentially hateful comment. Thus for each potentially hateful comment, we rebuild the conversation where the comment appears. Figure FIGREF14 shows an example of the collected conversation, where the second comment contains a hate keyword and is considered as potentially hateful. Because a conversation may contain more than one comments with hate keywords, we removed any duplicated conversations.
Gab: We collect data from all the Gab posts in October 2018. Similar to Reddit, we use hate keywords BIBREF6 to identify potentially hateful posts, rebuild the conversation context and clean duplicate conversations.
Skipping the three subreddits that have been removed, we collect data from ten subreddits: r/DankMemes, r/Imgoingtohellforthis, r/KotakuInAction, r/MensRights, r/MetaCanada, r/MGTOW, r/PussyPass, r/PussyPassDenied, r/The Donald, and r/TumblrInAction.
Answer: r/DankMemes, r/Imgoingtohellforthis, r/KotakuInAction, r/MensRights, r/MetaCanada, r/MGTOW, r/PussyPass, r/PussyPassDenied, r/The Donald, and r/TumblrInAction.
Question: Did Trump lose the 2020 presidential election?
Evidence: Trump lost the 2020 presidential election to Joe Biden but refused to concede defeat, falsely claiming widespread electoral fraud and attempting to overturn the results by pressuring government officials, mounting scores of unsuccessful legal challenges, and obstructing the presidential transition. On January 6, 2021, Trump urged his supporters to march to the U.S. Capitol, which many of them then attacked, resulting in multiple deaths and interrupting the electoral vote count.
Answer: Yes. Trump lost the 2020 presidential election, but refused to concede defeat. He furthermore urged his supporters to commit insurrection and attack the U.S. Capitol on January 6th 2021.
Question: What are few things that could impact your EV range on long trips
Answer: 1) Weather can be a big factor. For days that are extremely hot or cold you'll be running the AC or Heater respectively and that could be a big drain on the battery
2) And this could get unexpectedly get worse if you are stuck in a traffic jam since you are not able to gain much distance while draining battery to AC or Heather
3) Rain can also lead to faster battery drain where you'll need to run the defogger as well as windshield wipers. 
4) Additional load. If you are planning on taking heavy load like camping equipment, bicycles  it drains the battery faster.
Question: What are some of the best science fiction movies of all time?
Answer: There have been a number of well received science fiction movies including classics such "2001: A Space Odyssey", "Star Wars", "Blade Runner", "Alien", "Inception", "The Martian" and "The Matrix". Often the most important factor of what makes a movie great is very dependent on the viewer themselves. Are they interested in movies that are based more on hard science like "The Martian" or more like a soft space opera or fantasy like "Star Wars". Movies can also be great commercial successes while not being critically acclaimed. Some movies can also be great action films or be very thought provoking or reflect on bigger themes like the nature of being.
Question: Which of the following are noble gases: helium, sodium, neon, oxygen, argon, hydrogen, calcium, magnesium, lithium, boron, krypton, nitrogen, xenon, aluminium, radon.
Answer: The noble gases are - helium, neon, argon, krypton, xenon, radon.
Question: Where is Danny Brannagan from
Evidence: Danny Brannagan (born July 4, 1986) is a former professional Canadian football quarterback for the Toronto Argonauts of the Canadian Football League.

Early years
Brannagan attended high school at Assumption Catholic Secondary School in Burlington where in his 5th and final year in 2004, he led the Assumption Crusaders to the GHAC finals where they defeated St. Jean de Brebeuf in a thriller, with the final score 29-28. Trailing by 27 points with less than 8 minutes to play, Brannagan led Assumption's offense to an amazing comeback, taking the lead with under 2 minutes left to play.
Answer: Danny Brannagan (born July 4, 1986) is a former professional Canadian football quarterback for the Toronto Argonauts of the Canadian Football League.

Brannagan attended high school at Assumption Catholic Secondary School in Burlington where in his 5th and final year in 2004, he led the Assumption Crusaders to the GHAC finals where they defeated St. Jean de Brebeuf in a thriller, with the final score 29-28. Trailing by 27 points with less than 8 minutes to play, Brannagan led Assumption's offense to an amazing comeback, taking the lead with under 2 minutes left to play.
Question: Think of things to do in a place with cold winters. Provide answer as a bullet list.
Answer: - Build a snowman
- Have a snowball fight
- Go sledding
- Go skiing
- Go skating
- Go snowshoeing