DataHammer/scimrc huggingface dataset:
Question: What crowdsourcing platform did they use?
Evidence: We use crowdsourcing to annotate different aspects of the dataset. We used Amazon Mechanical Turk (AMT) for our annotations, restricting the task to workers in five English-speaking countries (USA, UK, Canada, New Zealand, and Australia), more than 1000 finished HITs and at least a 95% acceptance rate. To ensure the diversity of responses, we do not require additional qualifications or demographic information from our annotators.
We used Amazon Mechanical Turk (AMT) for our annotations, restricting the task to workers in five English-speaking countries (USA, UK, Canada, New Zealand, and Australia), more than 1000 finished HITs and at least a 95% acceptance rate.
Answer: The answers are shown as follows:
* Amazon Mechanical Turk (AMT)
Question: Currently, does Porthcurno mainly comprise farms and fisherman's cottages?
Evidence: Porthcurno is largely not farms or fisherman's cottages today, having its linear centre inland, centred 6.6 miles (10.6 km) west of the railway, market and resort town of Penzance and 2.5 miles (4.0 km) from Land's End, the most westerly point of the English mainland. In the most local-level organisational and community bodies, it is in civil and ecclesiastical parishes named Saint Levan and usually spelt as St Leven since the 18th century.
Answer: No
Question: What is the size of the resource in this paper?
Evidence: With this work we present a resource that will be extremely useful for building language systems in an endangered, under-represented language, Mapudungun. We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results. The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource.
Our resource could also be used for ethnographic and anthropological research into the Mapuche culture, and has the potential to contribute to intercultural bilingual education, preservation activities and further general advancement of the Mapudungun-speaking community.
The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource
Answer: 142 hours, more than 260k total sentences.
Question: What is a surf board used for?
Answer: A surfboard is shaped like a narrow plank.  Surfers stand on the top of the surfboard and use it to surf on the ocean.  They tend to be light and are strong enough to get hit by a large wave without breaking in half.  Surfboards were originally invented in Hawaii and were made using the wood from local trees.  Early surfboards from Hawaii were over 15 feet long and were referred to as longboards.  Surfboards got a lot shorter as surfers had wanted to be more agile with them in the ocean.
Question: What is the meaning of life?
Evidence: In their travels, Arthur comes to learn that the Earth was actually a giant supercomputer, created by another supercomputer, Deep Thought. Deep Thought had been built by its creators to give the answer to the "Ultimate Question of Life, the Universe, and Everything", which, after eons of calculations, was given simply as "42". Deep Thought was then instructed to design the Earth supercomputer to determine what the Question actually is. The Earth was subsequently destroyed by the Vogons moments before its calculations were completed, and Arthur becomes the target of the descendants of the Deep Thought creators, believing his mind must hold the Question. With his friends' help, Arthur escapes and they decide to have lunch at The Restaurant at the End of the Universe, before embarking on further adventures.
Answer: 42
Question: How do they represent input features of their model to train embeddings?
Evidence: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . The basic embedding model structure we use is shown in Fig. FIGREF1 . The model consists of a deep RNN with some number INLINEFORM3 of stacked layers, whose final hidden state vector is passed as input to a set of INLINEFORM4 of fully connected layers; the output of the final fully connected layer is the embedding INLINEFORM5 .
An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 .
Answer: The answers are shown as follows:
* a vector of frame-level acoustic features
Question: What is the biological term for Magic Mushrooms?
Evidence: Psilocybin mushrooms, commonly known as magic mushrooms, are a polyphyletic informal group of fungi that contain psilocybin which turns into psilocin upon ingestion. Biological genera containing psilocybin mushrooms include Psilocybe, Panaeolus (including Copelandia), Inocybe, Pluteus, Gymnopilus, and Pholiotina. Psilocybin mushrooms have been and continue to be used in indigenous New World cultures in religious, divinatory, or spiritual contexts. Psilocybin mushrooms are also used as recreational drugs. They may be depicted in Stone Age rock art in Africa and Europe but are most famously represented in the Pre-Columbian sculptures and glyphs seen throughout North, Central, and South America.
Answer: Magic Mushrooms are the colloquial term for Psilocybin mushrooms
Question: Given a reference text about the 2022 Mazar-i-Sharif mosque bombing, how many people were injured and killed?
Evidence: 2022 Mazar-i-Sharif mosque bombing

On 21 April 2022, a powerful bomb rocked the Shia Seh Dokan mosque in Mazar-i-Sharif, Balkh Province, Afghanistan killing at least 31 people and injuring more than 87 others. The Islamic State – Khorasan Province claimed responsibility via Telegram.

Worshippers were performing the Zuhr prayer inside the mosque when the bomb exploded. Authorities counted 31 dead and at least 87 injured, six of whom were wounded critically.

The bombing was part of a series of attacks on the same day, including two explosions targeting Taliban officers in Kunduz and Khogyani District and a bombing that injured two Shia children in Kabul.
Answer: At least 31 people were killed and more than 87 others were injured in the 2022 Mazar-i-Sharif mosque bombing.
Question: Please summarize the MECE principle, give an example and explain how it can be used in business
Evidence: The MECE principle, (mutually exclusive and collectively exhaustive) is a grouping principle for separating a set of items into subsets that are mutually exclusive (ME) and collectively exhaustive (CE). It was developed in the late 1960s by Barbara Minto at McKinsey & Company and underlies her Minto Pyramid Principle, and while she takes credit for MECE, according to her interview with McKinsey, she says the idea for MECE goes back as far as to Aristotle.

The MECE principle has been used in the business mapping process wherein the optimum arrangement of information is exhaustive and does not double count at any level of the hierarchy. Examples of MECE arrangements include categorizing people by year of birth (assuming all years are known), apartments by their building number, letters by postmark, and dice rolls. A non-MECE example would be categorization by nationality, because nationalities are neither mutually exclusive (some people have dual nationality) nor collectively exhaustive (some people have none).
Answer: MECE stands for Mutually Exclusive, Collectively Exhaustive and serves as an organizing framework used by management consulting firms, such as McKinsey and BCG as a way to organize reports and complicated data.  Due to its effectiveness, it has made it's way into the larger business community.  An example MECE arrangement would be departments within an organization, year hired for employees, the choice to eat in or eat out.  MECE can be used to simplify complex ideas.
Question: Think up some locations for me to ask my partner out
Answer: - On a greek island 
- At Disney World 
- At their favorite pizza place 
- On a cruise ship 
- At the end of a hike 
- At the Burj Khalifa 
- On a private charter helicopter ride 
- At Times Square, NYC
- At the Taj Mahal 
-  At a surprise party in their favorite place growing up 
- where you first met your partner 
- where you first confessed that you like each other 
- at your favorite date spot 
- at the northern lights in Norway
Question: Who was Tap Gordon Jones?
Evidence: Air Marshal Sir Edward “Tap” Gordon Jones, KCB, CBE, DSO, DFC (31 August 1914 – 20 February 2007) was an officer in the Royal Air Force for 34 years, from 1935 to 1969. He commanded a squadron of obsolescent biplane Gladiator fighters during the Greek Campaign in the Second World War, where he shot down five Italian Fiat CR.42 fighters. He served in mainly operational posts in Europe until he retired. Unusually, he never served a post in the Air Ministry or the UK Ministry of Defence.
Answer: Tap Gordon Jones, full name Air Marshal Sir Edward "Tap" Gordon Jones, KCB, CBE, DSO, DFC was an officer in the UK Royal Air Force from 1935 to 1969.  He commanded a squadron of Gladiators during the Greek Campaign in Wold War II.  At the time Gladiators were considered to be obsolete.  During the Greek Campaign, he shot down Five Italian fighters.  After the war until his retirement he mainly served in operational posts in Europe.
Question: Give me a list of good psychedelic rock bands that I'd like
Answer: - Tame Impala 
- Pond
- Kikagaku Moyo 
- Unknown Mortal Orchestra 
- King Gizzard & the Lizard Wizard
- Pink Floyd 
- The Doors 
- Grateful Dead 
- The Flaming Lips 
- The Beatles
- Post Animal
- The Who
Question: What is a sight-reading contest?
Answer: In musical education, sight-reading is the ability to perform a newly-encountered musical composition within just a few minutes. Music students typically enter a rehearsal room with their assigned instruments, sit down, and are given a just a few moments to learn a new piece. Oftentimes, directors are allowed to walk them quickly through the composition, perhaps clapping or singing, but the students are not allowed to practice the piece or mark up the musical notation. The ensemble that delivers the best performance (according to the contest's judges) in the given amount of time will win the contest.
Question: classify each of the following as a US state or US state capital: Atlanta, Washington, Boston, Iowa, Sacramento, Alaska
Answer: US States: Washington, Iowa, Alaska
US State Capitals: Atlanta, Boston, Sacramento
Question: Why should I cheer for the Sacramento Kings in this year's NBA playoffs?
Answer: Until this season, the Sacramento Kings held the longest playoff drought in all of North American professional sports, lasting 16 years. Despite this, the fanbase for the Kings remained loyal and regularly attended games to watch their team lose year after year. In the midst of this drought, the previous owners of the franchise attempted to move the team to a new city, with significant traction at various points, to both Anaheim and Seattle. It was only the passion of the fans, and the combined support from local businesses and politicians, that prevented the move. In addition to the Kings having one of the most supportive fanbases in the NBA getting their first taste of success, the Kings are also a highly enjoyable team to watch. The players are friendly and likable, and the offense is one of the most successful in NBA history. Lastly, the team also has a fun gimmick of lighting a large purple laser beam that can be seen for miles after every victory, leading to a chorus of 'Light the beam!' from the Kings fanbase in the arena at the conclusion of every win. Pulling all of these factors together make the Sacramento Kings a fun team to cheer for in the upcoming NBA playoffs.
Question: Did the synthesis model proposed by the authors outperform the word replacement synthesis baseline?
Evidence: We explored improving the performance of neural abstractive summarizers when applied to the low resource domain of student reflections using three approaches: domain transfer, data synthesis and the combination of both. For domain transfer, state of the art abstractive summarization model was pretrained using out-of-domain data (CNN/DM), then tuned using in-domain data (student reflections). The process of tuning improved ROUGE scores on the student reflection data, and at the same time produced more readable summaries. To incorporate synthetic data, we proposed a new template based synthesis model to synthesize new summaries. We showed that enriching the training data with this synthesized data can further increase the benefits of using domain transfer / tuning to increase ROUGE scores. We additionally showed that the proposed synthesis model outperformed a word replacement synthesis baseline. Future plans include trying domain adaptation, enhancing the synthesising process by using other models, further exploring template-based methods, and extending the analysis of the synthesis model to cover other types of data like reviews and opinions.
We additionally showed that the proposed synthesis model outperformed a word replacement synthesis baseline.
Answer: Yes, it did.
Question: Give me a list of 3 good ways to gain weight.
Answer: Here is a list of 3 good ways to gain weight:
1. Eat more food, especially highly processed foods and foods high in sugars 
2. Don't exercise, sit and do nothing for as long as you can
3. Drink sugary drinks for extra calories
Question: Why is the sky blue?
Answer: The sky appears to be blue because of the way sunlight scatters in the atmosphere. The white light from the sun is composed of many frequencies and as it travels through the atmosphere, the blue light, which has a shorter frequency, scatters more. When looking at an area of the sky away from the sun we see the scattered blue light.
Question: How do plants get their energy
Answer: They use photosynthesis and turn the sunlight into glucose as food
Question: When is the best time to catch Bass in Michigan?
Answer: You have the best chance of hooking into a Bass in the early morning or evening on inland lakes in Michigan. Since Bass spawn in the spring, you also will increase your chances of catching bass in Spring (Typically April/May/June) when the water temperature hits 63 degrees F. Bass like shallow water, grassy areas, and spawn in sandy areas of the lake. Use a spinner bait, fake green worm, or top water bait for your best chances of catching a big bass!
Question: What is the FDA doing to respond to the COVID-19 pandemic?
Answer: The FDA, along with other federal, state, and local agencies and public health officials across the country and internationally, plays a critical role in protecting public health during the COVID-19 pandemic. FDA staff are working around the clock to support development of  medical countermeasures and are providing regulatory advice, guidance, and technical assistance to advance the development and availability of vaccines, therapies, diagnostic tests and other medical devices for use diagnosing, treating, and preventing this novel virus. The FDA continues to monitor the human and animal food supply and take swift action on fraudulent COVID-19 products.
Question: Write a review of Brandon Sanderson's book Warbreaker
Answer: Can't believe what a consistent hitter Sanderson is.

The characters really grew throughout the novel. Siri and Vivenna switch places, and also switch personalities, reveling in their new preferences. Lightsong owes up to the man he once was, and I found his humor quite dad-joke-y and well, good. Loved Denth. And Susebron, well, grows up.

Biochroma was a fun concept, and made for some interesting power plays.
Question: what was the biggest tax change from tax reform
Answer: competitive US corp tax rates when compared to rest of world
Question: Which of the following does not fit with the others? Please explain your reasoning.
Galaxy M81
Black Eye Galaxy
Pinwheel Galaxy
Andromeda Galaxy
Sombrero Galaxy
Answer: Galaxy M81 does not fit with the other 4 in the list as M81is a designation rather than a proper name. The other 4 galaxies listed also have a designation, e.g Sombrero Galaxy is designated M104. M81 also has a proper name, Bode's Galaxy. But as listed, Galaxy M81 is distinct from the rest of the list.
Question: What is Accrued income security
Answer: This is a security (investment) which pays interest at regular intervals. When it is sold, interest may have built up and this interest will be paid to the new owner. Interest built up like this is called accrued interest.
Question: What is the birthday problem?
Answer: The Birthday Problem is a probability problem with results that many find counterintuitive. The problem is formulated as "how many people must you consider such that the probability of at least two people sharing a birthday is at least 50%?". The answer, which many find surprising and counterintuitive, is 23. The answer is lower than most people expect and it is due to the number of pairwise permutations that exists for 23 people.
Question: What does the author analyze from the numbers in Table 1?
Evidence: One interesting pattern is that the ethnicity/race of babies doesn't seem to be mentioned unless the baby is black or asian. In other words: white seems to be the default, and others seem to be marked. How can we tell whether or not the data is actually biased?
We don't know whether or not an entity belongs to a particular social class (in this case: ethnic group) until it is marked as such. But we can approximate the proportion by looking at all the images where the annotators have used a marker (in this case: adjectives like black, white, asian), and for those images count how many descriptions (out of five) contain a marker. This gives us an upper bound that tells us how often ethnicity is indicated by the annotators. Note that this upper bound lies somewhere between 20% (one description) and 100% (5 descriptions). Figure TABREF22 presents count data for the ethnic marking of babies. It includes two false positives (talking about a white baby stroller rather than a white baby). In the Asian group there is an additional complication: sometimes the mother gets marked rather than the baby. E.g. An Asian woman holds a baby girl. I have counted these occurrences as well.
The numbers in Table TABREF22 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups. We can take one step further and look at all the 697 pictures with the word `baby' in it. If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased.
I have manually categorized each of the baby images. There are 504 white, 66 asian, and 36 black babies. 73 images do not contain a baby, and 18 images do not fall into any of the other categories. While this does bring down the average number of times each category was marked, it also increases the contrast between white babies (who get marked in less than 1% of the images) and asian/black babies (who get marked much more often). A next step would be to see whether these observations also hold for other age groups, i.e. children and adults. INLINEFORM0 
The numbers in Table 1 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups. We can take one step further and look at all the 697 pictures with the word ‘baby’ in it. If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased.
Answer: The numbers in Table 1 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups. They can take one step further and look at all the 697 pictures with the word ‘baby’ in it. If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased.
Question: Who was Caroline Olivia Zickerick's grandmother?
Evidence: Caroline Vreeland was born Caroline Olivia Zickerick in Washington, D.C. to Daisy Vreeland and Michael Zickerick. Her father, a German diplomat, served as the German Ambassador to Moldova and as the Director General of the German Institute Taipei in Taiwan. Her maternal great-grandmother was fashion editor Diana Vreeland. She is a grandniece of American diplomat Frederick Vreeland and a first cousin once removed of the Buddhist abbot Nicholas Vreeland. Vreeland spent four months of her childhood living at the German Embassy in Kingston, Jamaica while her father was stationed there. Her father later had an affair and her parents divorced. Vreeland's mother moved them to a Siddha Yoga ashram in Upstate New York. They left New York and moved in with her grandparents in Belvedere, Marin County in the San Francisco Bay Area. After graduating from high school she moved to Los Angeles to pursue a music career.
Answer: Caroline Olivia Zickerick grandmother's was fashion editor Diana Vreeland
Question: What two benchmark datasets are used?
Evidence: We adopt the DSTC2 BIBREF20 dataset and Maluuba BIBREF21 dataset to evaluate our proposed model. Both of them are the benchmark datasets for building the task-oriented dialog systems. Specifically, the DSTC2 is a human-machine dataset in the single domain of restaurant searching. The Maluuba is a very complex human-human dataset in travel booking domain which contains more slots and values than DSTC2. Detailed slot information in each dataset is shown in Table TABREF34.
We adopt the DSTC2 BIBREF20 dataset and Maluuba BIBREF21 dataset to evaluate our proposed model.
Answer: The answers are shown as follows:
* DSTC2
* Maluuba
Question: Why do leaves on houseplants turn yellow and brown?
Answer: Often houseplant leaves turn yellow and brown for a few common reasons. First is overwatering. Houseplants are kept in containers that can retain water which can lead to root rot if not watered carefully. Some houseplants prefer the soil to dry out between waterings and many home gardeners end up watering before the soil has dried up sufficiently. Too much water in a container can lead to root rot which first can show symptoms in browning, dropping, and yellowing of leaves. Another reason for leaf discoloration is under watering as some plants prefer the soil to have a level of moisture at all times. Other reasons include too little sunlight which limits the plants ability to photosynthesize. Also, even too much sunlight can cause burned leaves.
Question: Which letters denote Jesus Nazareth King of the Jews
Answer: INRI