DataHammer/scimrc huggingface dataset:
Question: What are least important components identified in the the training of VQA models?
Evidence: In this study we observed the actual impact of several components within VQA models. We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy. Our experiments have demonstrated that even simple fusion strategies can achieve performance on par with the state-of-the-art. Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation. It is is now clear that some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant and can be removed altogether without harming accuracy. For future work, we expect to expand this study in two main ways: (i) cover additional datasets, such as Visual Genome BIBREF37; and (ii) study in an exhaustive fashion how distinct components interact with each other, instead of observing their impact alone on the classification performance.
It is is now clear that some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant and can be removed altogether without harming accuracy.
Answer: The answers are shown as follows:
*  some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant
Question: Are some baseline models trained on this dataset?
Evidence: We embedded COSTRA sentences with LASER BIBREF15, the method that performed very well in revealing linear relations in BaBo2019. Having browsed a number of 2D visualizations (PCA and t-SNE) of the space, we have to conclude that visually, LASER space does not seem to exhibit any of the desired topological properties discussed above, see fig:pca for one example.
We embedded COSTRA sentences with LASER BIBREF15, the method that performed very well in revealing linear relations in BaBo2019.
Answer: Yes.
Question: Is it better to run or to walk?
Answer: Deciding if running or walking depends on the environment you are in and your capabilities as a person.  The health benefits primarily of walking and running is raising the heart rate.  Consistency is key and it is better to maintain a regular walking habit then to have a sporadic running habit.  If somebody is able to maintain a regular running habit they will see the most benefit from a cardiovascular perspective.
Question: Identify the bird from the list: Racoon, Golden eagle, Anteater
Answer: Golden eagle
Question: What are some common Indian spices?
Answer: Chilli powder
Turmeric
Cumin
Coriander
Clove
Cinnamon
Turmeric
Fenugreek
Cardamom
Question: What is the difference between check and checkmate?
Evidence: When a king is under immediate attack, it is said to be in check. A move in response to a check is legal only if it results in a position where the king is no longer in check. This can involve capturing the checking piece; interposing a piece between the checking piece and the king (which is possible only if the attacking piece is a queen, rook, or bishop and there is a square between it and the king); or moving the king to a square where it is not under attack. Castling is not a permissible response to a check.

The object of the game is to checkmate the opponent; this occurs when the opponent's king is in check, and there is no legal way to get it out of check. It is never legal for a player to make a move that puts or leaves the player's own king in check. In casual games, it is common to announce "check" when putting the opponent's king in check, but this is not required by the rules of chess and is usually not done in tournaments.
Answer: When a king is about to be captured in the next opponents move, then the king is in check. If the king cannot get out of check, then the king is checkmated and loses the game
Question: What topic clusters are identified by LDA?
Evidence: For offensive language detection in Twitter, users addressed in tweets might be an additional relevant signal. We assume it is more likely that politicians or news agencies are addressees of offensive language than, for instance, musicians or athletes. To make use of such information, we obtain a clustering of user ids from our Twitter background corpus. From all tweets in our stream from 2016 or 2017, we extract those tweets that have at least two @-mentions and all of the @-mentions have been seen at least five times in the background corpus. Based on the resulting 1.8 million lists of about 169,000 distinct user ids, we compute a topic model with INLINEFORM0 topics using Latent Dirichlet Allocation BIBREF3 . For each of the user ids, we extract the most probable topic from the inferred user id-topic distribution as cluster id. This results in a thematic cluster id for most of the user ids in our background corpus grouping together accounts such as American or German political actors, musicians, media websites or sports clubs (see Table TABREF17 ). For our final classification approach, cluster ids for users mentioned in tweets are fed as a second input in addition to (sub-)word embeddings to the penultimate dense layer of the neural network model.
Based on the resulting 1.8 million lists of about 169,000 distinct user ids, we compute a topic model with INLINEFORM0 topics using Latent Dirichlet Allocation BIBREF3 . For each of the user ids, we extract the most probable topic from the inferred user id-topic distribution as cluster id. This results in a thematic cluster id for most of the user ids in our background corpus grouping together accounts such as American or German political actors, musicians, media websites or sports clubs (see Table TABREF17 ). 
Answer: Clusters of Twitter user ids from accounts of American or German political actors, musicians, media websites or sports club
Question: Can birds sleep in the air?
Answer: The sooty tern only lands to breed and rear its young. It eats, sleeps, and drinks while flying and can stay airborne for ten years! An albatross also sleeps while it flies! It can doze while cruising at 25mph.
Question: who is Hugh Ogden?
Evidence: Hugh Ogden (March 11, 1937 – December 31, 2006) was an American poet and educator. Ogden was a 1959 graduate of Haverford College. Ogden received his master’s degree from New York University and a Ph.D. from the University of Michigan. He taught at Michigan and then for four decades at Trinity College in Hartford. While at Trinity, Ogden co-founded the College’s creative writing program in 1968.

Ogden was born in Erie, Pennsylvania. Ogden has written an estimated 400 to 500 poems, many of which have been published in small presses and magazines, and he has won a National Endowment for the Arts grant and two Connecticut Commission on the Arts Fellowships. In 1998, Ogden was nominated for the Pushcart Prize in Poetry.

Ogden died on December 31, 2006, after falling through the ice on Rangeley Lake in Oquossoc, Maine.
Answer: Hugh Ogden was an American poet who taught at Trinity College in Hartford, CT. Ogden wrote an estimated 400 to 500 poems, and was published with his book "Gift".  He was born in Erie, Pennsylvania and won several poetry awards and co-founded the College's creative writing program in 1968 at Trinity College.  Ogden died on December 31, 2006, after falling through the ice.
Question: The year 2022 was declared by UNGA as
Answer: The International year of Artisanal Fisheries and Aquaculture
Question: Give me a list of the best golf courses in America
Answer: Pebble Beach, Torrey Pines, Pinehurst, TPC Scottsdale, Whistling Straights, Muirfield, Bethpage Black, Bay Hill
Question: Which of these items should I pack away during the summer: winter coat, swimsuit, hiking boots, space heater, coffee pot.
Answer: In most climates you could put a winter coat and a space heater in storage during the summer. A swimsuit would likely be used in the summer, and hiking boots or a coffee pot aren’t particularly impacted by seasonal changes.
Question: Why should I rent my house to you?
Answer: Thank you again for speaking with me yesterday about the lovely home at 2225 Brittan Avenue, and for showing it to my dad, and to my boyfriend on Sunday. As I mentioned, we are very interested in leasing this property and I hope you will share this with the letter with the individuals who are leasing it out. 

My parents live on Main Street, and are deeply engaged in the community, whether it is organizing the annual Main Street block party, bringing an elderly neighbor a home cooked meal, or sharing gardening tips and seedlings with local kids. 

Our family of four also have the benefit of being engaged in the community, and knowing the neighborhood well.  My parents have lived here since 2007, and the boys and I lived with them for the first seven years of my youngest son’s life. We are thrilled at the opportunity to live close enough to run down the street for dinner a few times a week, see the neighbors we’ve known for 15 years, plus the fact that my parents drive the boys to school a few times a week.

As discussed, we have two very well-behaved and small (under 10 lbs) cats we adopted last year. They are the light of our lives and provide us with lots of cuddles and laughs. The boys especially would be very sad to leave them behind, so we hope that a small exception could be made to have them join us at 2225 Brittan Ave.

Beyond the location, we love the house, with the wood floors and all the light. And the boys are very excited to each have their own rooms! 

We hope that you will consider our family for the home. We've attached applications, driver's licenses, pay stubs, and credit reports. In order to afford the cost of living closer, my dad will be acting as Grantor. We would be more than happy to put down our deposit upon approval with a move-in date of 3/15/22. 

Please let us know if you need any additional information to make your decision, and we look forward to hearing from you.
Question: How many cents is a nickel worth?
Answer: A nickel is worth 5 cents.
Question: Based on the autopsy, list the contributing factors to Farley's death in bullet format.
Evidence: On December 18, 1997, Farley was found dead by his younger brother John in his apartment in the John Hancock Center in Chicago. He was 33 years old. An autopsy revealed that Farley had died of an overdose of a combination of cocaine and morphine, commonly known as a "speedball". Advanced atherosclerosis was cited as a "significant contributing factor."

A private funeral was held for Farley on December 23, 1997, at Our Lady Queen of Peace Catholic Church in his hometown of Madison, Wisconsin. Over 500 people attended his funeral, including many comedians who had worked with him on Saturday Night Live and on film, such as Dan Aykroyd, Adam Sandler, Chris Rock, Rob Schneider, Lorne Michaels, Al Franken, John Goodman, Bob Odenkirk, Tim Meadows, Robert Smigel, George Wendt and Phil Hartman. Notably absent was Farley's best friend David Spade. Spade's non-appearance fueled speculation that there was some falling out with Farley prior to his death. However, years later Spade denied any ill will between him and his comedic partner, admitting that his absence from the funeral was because he would have found it too emotionally difficult. Farley's remains were interred at Resurrection Cemetery in Madison.
Answer: -Overdose from a speedball
-Advanced atherosclerosis
Question: what datasets were used?
Evidence: In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise. The 20-Newsgroups consists of twenty thousand messages taken from twenty newsgroups, and is divided in six super-classes, which are, in turn, partitioned in several sub-classes. For this first set of experiments, only the four most populated super-classes were used: “computers", “science", “politics" and “recreative". The preprocessing of the documents consisted of stemming and stop-words removal. After that, 75% of the documents were randomly selected for training and the remaining 25% for testing.
In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 .
In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise. 
In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 .
Answer: The answers are shown as follows:
*  20-Newsgroups benchmark corpus 
* Reuters-21578
* LabelMe
Question: Extract the most recent owner from the following Wikipedia paragraph.
Evidence: Owners
Further information: Category:Seattle Storm owners
Barry and Ginger Ackerley, owners of the Seattle SuperSonics (2000–2001)
Howard Schultz, owner of the Seattle SuperSonics (2001–2006) 
Clay Bennett, owner of the Seattle SuperSonics (2007) 
Force 10 Hoops LLC, composed of Dawn Trudeau, Lisa Brummel, Ginny Gilder (2008–present)
Answer: The current owner of the Seattle Storm is Force 10 Hoops LLC, which is composed of Dawn Trudeau, Lisa Brummel, Ginny Gilder. They have owned the Seattle Storm since 2008.
Question: what dataset was used?
Evidence: The data stems from a joint ADAPT-Microsoft project. An overview of the provided dataset is given in Table TABREF16 . Notice that the available amount of data differs per language.
The data stems from a joint ADAPT-Microsoft project. 
Answer: The dataset from a joint ADAPT-Microsoft project
Question: What are some of the benefits of first-mover advantage?
Evidence: In marketing strategy, first-mover advantage (FMA) is the competitive advantage gained by the initial ("first-moving") significant occupant of a market segment. First-mover advantage enables a company or firm to establish strong brand recognition, customer loyalty, and early purchase of resources before other competitors enter the market segment.

First movers in a specific industry are almost always followed by competitors that attempt to capitalize on the first movers' success. These followers are also aiming to gain market share; however, most of the time the first-movers will already have an established market share, with a loyal customer base that allows them to maintain their market share.
Answer: First-mover advantage may enable a company or firm to gain competitive advantage by establishing customer loyalty, strong brand recognition, and early purchase of inputs before other competitors enter the market.
Question: which public datasets were used?
Evidence: To verify the effectiveness of our proposed model, we conduct multiple experiments on three Chinese Machine Reading Comprehension datasets, namely CMRC-2017 BIBREF17 , People's Daily (PD) and Children Fairy Tales (CFT) BIBREF2 . In these datasets, a story containing consecutive sentences is formed as the Document and one of the sentences is either automatically or manually selected as the Query where one token is replaced by a placeholder to indicate the answer to fill in. Table TABREF8 gives data statistics. Different from the current cloze-style datasets for English reading comprehension, such as CBT, Daily Mail and CNN BIBREF0 , the three Chinese datasets do not provide candidate answers. Thus, the model has to find the correct answer from the entire document.
Besides, we also use the Children's Book Test (CBT) dataset BIBREF1 to test the generalization ability in multi-lingual case. We only focus on subsets where the answer is either a common noun (CN) or NE which is more challenging since the answer is likely to be rare words. We evaluate all the models in terms of accuracy, which is the standard evaluation metric for this task.
To verify the effectiveness of our proposed model, we conduct multiple experiments on three Chinese Machine Reading Comprehension datasets, namely CMRC-2017 BIBREF17 , People's Daily (PD) and Children Fairy Tales (CFT) BIBREF2 
Besides, we also use the Children's Book Test (CBT) dataset BIBREF1 to test the generalization ability in multi-lingual case.
Answer: The answers are shown as follows:
* CMRC-2017
* People's Daily (PD)
* Children Fairy Tales (CFT) 
* Children's Book Test (CBT)
Question: Determine whether each of the following items is worn on somebody's head or feet: helmet, shoes, sandals, hat, socks, beanie, cleats
Answer: Helmet: Head
Shoes: Feet
Sandals: Feet
Hat: Head
Socks: Feet
Beanie: Head
Cleats: Feet
Question: What do you say to someone who is struggling with addiction?
Answer: First, educate yourself.  Be honest with them.  Share with them how their addiction is not only hurting themselves but also those that care about them.  Be clear that you what them to seek help and that it comes from a place where you are fearful for their safety.  Do not enable them.  Have realistic expectations and help them get the support and counseling that they need to get better.
Question: Provide a list of names for an inspiring exotic dancer
Answer: 1. Venum
2. Sapphire 
3. Barbie
4. Diamond
5. Galaxy 
6. Polaris
7. Lemon
8. Firefly
9. Fantasia
10. Trinity
Question: When was the first episode of Big Brother on CBS?
Answer: The first episode of Big Brother aired on CBS in July 2000.
Question: How does the fusion method work?
Evidence: In this way, an attention vector sequence is segmented into several subsequences and each subsequence represents the attention of one word. Then, motivated by the psycholinguistic finding that readers are likely to pay approximate attention to each character in one Chinese word, we devise an appropriate aggregation module to fuse the inner-word character attention. Concretely, we first transform $\lbrace \textbf {a}_c^s,..., \textbf {a}_c^{s+l-1}\rbrace $ into one attention vector $\textbf {a}_w^i$ for $w_i$ with the mixed pooling strategy BIBREF11. Then we execute the piecewise up- mpling operation over each $\textbf {a}_w^i$ to keep input and output dimensions unchanged for the sake of plug and play. The detailed process can be summarized as follows:
In this way, an attention vector sequence is segmented into several subsequences and each subsequence represents the attention of one word. Then, motivated by the psycholinguistic finding that readers are likely to pay approximate attention to each character in one Chinese word, we devise an appropriate aggregation module to fuse the inner-word character attention.
Answer: The answers are shown as follows:
* ttention vector sequence is segmented into several subsequences and each subsequence represents the attention of one word
* we devise an appropriate aggregation module to fuse the inner-word character attention
Question: How large is South Africa's economy?
Evidence: South Africa has a mixed economy, the third largest in Africa, after Nigeria and Egypt and the 39th largest in the world. It also has a relatively high gross domestic product (GDP) per capita compared to other countries in sub-Saharan Africa US$16,040 at purchasing power parity as of 2023 ranked 95th. 

South Africa is ranked 40th by total Wealth, making it the second wealthiest country in Africa, and in terms of private wealth South Africa has a private wealth of $651 billion making it the wealthiest country in Africa by private wealth followed by Egypt with $307 billion and Nigeria with $228 billion.

Unlike most of the world's poor countries, South Africa does not have a thriving informal economy. Only 15% of South African jobs are in the informal sector, compared with around half in Brazil and India
Answer: South Africa has the third largest economy in Africa, and is the second wealthiest country on the continent, and the wealthiest by private wealth ($651 billion), followed by Egypt with $307 billion and Nigeria with $228 billion. South Africa's economy is mainly formal, with only 15% of jobs in the informal sector, compared with 50% in Brazil and India who have a thriving informal economy.
Question: Given a reference text about the Dormouse, in which novel is it a character?
Evidence: The Dormouse is a character in "A Mad Tea-Party", Chapter VII  from the 1865 novel Alice's Adventures in Wonderland by Lewis Carroll.

The Dormouse sat between the March Hare and the Mad Hatter. They were using him as a cushion while he slept when Alice arrives at the start of the chapter.

The Dormouse is always falling asleep during the scene, waking up every so often, for example to say:

`You might just as well say,' added the Dormouse, who seemed to be talking in his sleep, that "I breathe when I sleep" is the same thing as "I sleep when I breathe"!'

He also tells a story about three young sisters who live in a treacle well, live on treacle, and draw pictures of things beginning with M, such as mousetraps, memory and muchness.

He later appears, equally sleepy, at the Knave of Hearts' trial and voices resentment at Alice for growing, and his last interaction with any character is his being "suppressed" (amongst other things) by the Queen for shouting out that tarts are made of treacle.
Answer: The Dormouse is a character in the novel "Alice's Adventures in Wonderland".
Question: Is it safe for dogs to eat avacado?
Answer: No, avocado is considered dangerous for dogs to consume.
Question: Which five natural disasters were examined?
Evidence: The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25). For each disaster, we scraped tweets starting from two weeks prior to the beginning of the event, and continuing through two weeks after the end of the event. Summary statistics on the downloaded event-specific tweets are provided in Table TABREF1 . Note that the number of tweets occurring prior to the two 2018 sets of California fires are relatively small. This is because the magnitudes of these wildfires were relatively unpredictable, whereas blizzards and hurricanes are often forecast weeks in advance alongside public warnings. The first (influential tweet data) and second (event-related tweet data) batches are de-duplicated to be mutually exclusive. In Section SECREF2 , we perform geographic analysis on the event-related tweets from which we can scrape self-reported user city from Twitter user profile header cards; overall this includes 840 pre-event and 5,984 post-event tweets.
The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25). 
Answer: The answers are shown as follows:
* the East Coast Bomb Cyclone
*  the Mendocino, California wildfires
* Hurricane Florence
* Hurricane Michael
* the California Camp Fires