DataHammer/scimrc huggingface dataset:
Question: What information has an important contribution to the overall performance?
Evidence: Table TABREF21 shows the performance of our models when using all features described in Section SECREF4: see the SVM$_{All}$ and the FNN$_{All}$ rows. In order to put the numbers in perspective, we also show the results for five increasingly competitive baselines.
First, there is a random baseline, followed by an SVM classifier based on a bag-of-words representation with TF.IDF weights learned on the training data. Then come three versions of the ClaimBuster system: CB-Platform uses scores from the online demo, which we accessed on December 20, 2016, and SVM$_{CBfeat}$ and FNN$_{CBfeat}$ are our re-implementations, trained on our dataset.
We can see that all systems perform well above the random baseline. The three versions of ClaimBuster also outperform the TF.IDF baseline on most measures. Moreover, our reimplementation of ClaimBuster performs better than the online platform in terms of MAP. This is expected as their system is trained on a different dataset and it may suffer from testing on slightly out-of-domain data. At the same time, this is reassuring for our implementation of the features, and allows for a more realistic comparison to the ClaimBuster system.
More importantly, both the SVM and the FNN versions of our system consistently outperform all three versions of ClaimBuster on all measures. This means that the extra information coded in our model, mainly more linguistic, structural, and contextual features, has an important contribution to the overall performance.
We can further see that the neural network model, FNN$_{All}$, clearly outperforms the SVM model: consistently on all measures. As an example, with the precision values achieved by FNN$_{All}$, the system would rank on average 4 positive examples in the list of its top-5 choices, and also 14-15 in the top-20 list. Considering the recall at the first $R$ sentences, we will be able to encounter 43% of the total number of check-worthy sentences. This is quite remarkable given the difficulty of the task.
This means that the extra information coded in our model, mainly more linguistic, structural, and contextual features, has an important contribution to the overall performance.
Answer: The linguistic, structural, and contextual features have an important contribution to the overall performance.
Question: Tell me whether these are names of paintings or song titles: The Starry Night, Mona Lisa, Irises, Billie Jean, Hey Jude, Come As You Are.
Answer: The Starry Night is a famous painting painting by the Dutch Post-Impressionist painter Vincent van Gogh.
Mona Lisa is a painting by Italian artist Leonardo da Vinci.
Irises is a painting of irises by the Dutch artist Vincent van Gogh.
Billie Jean is a song by American singer Michael Jackson.
Hey Jude is a song by English rock band the Beatles.
Come As You Are is a song by American rock band Nirvana.
Question: Who directed the episode of Game of Thrones where Jon Snow and Tormund Giantsbane go to ask the wildlings to settle beyond the Wall, but end up encountering White Walkers and the Night King?
Answer: Season five, episode eight entitled "Hardhome"
Question: What is Denville Station?
Evidence: Denville is an active commuter railroad train station in Denville Township, Morris County, New Jersey. Located on Estling Road, the station contains three side platforms–two curved low-level platforms that service New Jersey Transit's Morristown Line, and a third that services their Montclair-Boonton Line. Both platforms on the Morristown Line contain miniature high-level platforms for handicap accessibility. Trains on both lines operate between Hoboken Terminal, New York Penn Station and Hackettstown. Heading westbound, the next station is Dover while the next station east on the Morristown Line is Mount Tabor. The next station east on the Montclair-Boonton Line is Mountain Lakes.
Answer: Denville station is a commuter railroad station in Denville Township, Morris Country, New Jersey. It is supported by two lines, the New Jersey Morristown Line and the Montclair-Boonton Line. Trains on both of these lines operate between Hoboken Terminal, New York Penn Station and Hackettstown.
Question: What additional experiments are conducted to rule out that only the questions or only the comments are necessary to optimally solve the task?
Evidence: In order to rule out that only the questions or only the comments are necessary to optimally solve the task, we conduct some additional experiments:
Only use a single segment containing the comment, removing the questions from the training and test data (missing questions).
Only use the question and remove the comment (missing comments).
In both cases the performance decreases across all evaluation settings (Table TABREF39). The loss in performance is much higher when comments are missing, indicating that the comments contain the most important information about stance. As can be expected, the score achieved without comments is only slightly different from the target-wise majority class baseline.
But there is also a loss in performance when the questions are missing, which underlines the importance of pairing both pieces of text. The effect of missing questions is especially strong in the supervised and cross-lingual settings. To illustrate this, we provide in Table TABREF48 some examples of comments that occur with multiple different targets in the training set. Those examples can explain why the target can be essential for disambiguating a stance detection problem. On the other hand, the effect of omitting the questions is less pronounced in the cross-target settings.
The above single-segment experiments tell us that both the comment and the question provide crucial information. But it is possible that the M-Bert model, even though trained on both segments, mainly looks at a single segment at test time. To rule this out, we probe the model with randomized data at test time:
Test the model on versions of the test sets where the comments remain in place but the questions are shuffled randomly (random questions). We make sure that the random questions come from the same test set and language as the original questions.
Keep the questions in place and randomize the comments (random comments). Again we shuffle the comments only within test set boundaries.
The results in Table TABREF39 show that the performance of the model decreases in both cases, confirming that it learns to take into account both segments.
In order to rule out that only the questions or only the comments are necessary to optimally solve the task, we conduct some additional experiments: • Only use a single segment containing the comment, removing the questions from the training and test data (missing questions). • Only use the question and remove the comment (missing comments).
Answer: • Only use a single segment containing the comment, removing the questions from the training and test data (missing questions). • Only use the question and remove the comment (missing comments).
Question: Why to people lift their trucks?
Answer: When people talk about "lifting" their trucks, they are referring to increasing the height and ground clearance through modification to the suspension of the vehicle.  Most often this is in reference to pick-up trucks, as those vehicles come from the manufacturer with the front of the vehicle at a lower height than the rear.  Know as a "rake", this is intended to allow the vehicle to ride at a level height even when carrying significant weight in the truck bed or towing heavy trailers.  There are various methods for lifting a vehicle that range from inexpensive spacers to elevate factory springs to more comprehensive systems replacing multiple suspension elements with new, purpose-built components.  People often prefer to lift their truck for the aesthetic appeal as well as improved ground clearance and performance when driving off-road.
Question: Extract interconnection technologies used to create a regular working system.
Evidence: Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, sensors and integrated circuits, and associated passive interconnection technologies. The nonlinear behaviour of active components and their ability to control electron flows makes digital switching possible, and electronics is widely used in information processing, telecommunications, and signal processing. Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
Today, most electronic devices use semiconductor components to perform electron control. The underlying principles that explain how semiconductors work are studied in solid state physics, whereas the design and construction of electronic circuits to solve practical problems are part of electronics engineering.
Answer: Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
Question: What was special about the 2021 Formula 1 World Championship?
Answer: The 2021 Formula 1 World Championship came down to the final race of the 2021 season in Abu Dhabi.  Seven-time world champion, Lewis Hamilton and Max Versteppen were tied in drivers' points, with this race being the deciding factor of who would be crowned champion. 
Ultimately, the 2021 Formula 1 World Championship was won by Dutch driver, Max Verstappen.  This was his first Driver's Championship title.  

What makes this particular championship special, or controversial (depending on who you ask), is that the race director, Michael Masi, misinterpreted the regulations regarding a late safety car restart.  Masi called the safety car back into the pit lane without it having completed an additional lap as required by the Formula 1 Sporting Regulations (Article 48.12).  By not heeding these regulations, all lapped cars were not able to unlap themselves which resulted in Max Verstappen being in a position to easily overtake Lewis Hamilton for the win.
Question: Make a list of several games I could play with two friends
Answer: You could play cribbage, monopoly, poker, backgammon or yahtzee
Question: what book would you recommend for pregnant women?
Answer: Pregnant women have a lot on their plate, including their own physical and mental health and preparation for childbirth and newborn care. What to expect when you are expecting written by Heidi Murkoff is a popular book that provides a 360 degree view of pregnancy details like week-by-week fetal development and nutrient recommendations for each month. Expecting better by Emily Oster is another popular, more data-oriented and anecdotal, aiming to debunk common misguided rules and myths about pregnancy. Heng Ou's Nine Golden Months focuses more on mental power and nutrient suggestions for expecting moms. It also provides incredible recipes that turn easy-to-access grocery store staples to tasty and nutrient dishes.
Question: Did the collection process use a WoZ method?
Evidence: This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.
While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort.
This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.
While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort.
Answer: No.
Question: Where do bears live?
Answer: Different species of bears can be found in different parts of the world. For example, black bears can be found in North America and Asia, whereas polar bears can be found in the Arctic. Grizzly bears, are also known as brown bears, they can be found across Europe, Asia, and North America.
Question: Which of the following ski resorts are located in Utah: Alta Ski Area, Brighton Ski Resort, Snowbird, Solitude Mountain Resort, Deer Valley Resort, Park City Mountain,  Woodward Park City, Nordic Valley Resort, Powder Mountain, Snowbasin, Sundance Resort, Beaver Mountain Resort, Cherry Peak Resort, Brian Head Resort, Eagle Point Resort, Keystone, Copper Mountain, Vail Resort, Beaver Creek, Eldora
Answer: The following ski resorts are in Utah: Alta Ski Area, Brighton Ski Resort, Snowbird, Solitude Mountain Resort, Deer Valley Resort, Park City Mountain,  Woodward Park City, Nordic Valley Resort, Powder Mountain, Snowbasin, Sundance Resort, Beaver Mountain Resort, Cherry Peak Resort, Brian Head Resort, Eagle Point Resort
Question: I'm planning a month-long trip and am wondering which of these items are okay to be kept in a carry-on bag. Could you help classify these items as "allowed" or "not allowed" by the TSA? Toothbrush, Cologne, Beard Shaver, Alcohol, and Bottle Opener.
Answer: Here is what's allowed and what's not allowed in your carry-on luggage:
- Allowed: Toothbrush, Cologne (3.4 Ounces), Beard Shaver
- Not allowed: Alcohol, Bottle Opener
Question: What's the advantage of adversarial learning?
Evidence: In this work, we incorporate a domain discriminator to align the mismatched domains between ASR hypotheses and reference transcriptions. The adversarial learning allows the end-to-end QA model to learn domain-invariant features and improve the robustness to ASR errors. The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score. 
The adversarial learning allows the end to-end QA model to learn domain-invariant features and improve the robustness to ASR errors.
Answer: The adversarial learning allows the end to-end QA model to learn domain-invariant features and improve the robustness to ASR errors.
Question: Using this definition, can you summarize why hyperventilation is considered dangerous?
Evidence: Voluntary hyperventilation before beginning voluntary apnea is commonly believed to allow the person involved to safely hold their breath for a longer period. In reality, it will give the impression that one does not need to breathe, while the body is actually experiencing a blood-oxygen level that would normally, and indirectly, invoke a strong dyspnea and eventually involuntary breathing. Some have incorrectly attributed the effect of hyperventilation to increased oxygen in the blood, not realizing that it is actually due to a decrease in CO2 in the blood and lungs. Blood leaving the lungs is normally fully saturated with oxygen, so hyperventilation of normal air cannot increase the amount of oxygen available, as oxygen in blood is the direct factor. Lowering the CO2 concentration increases the pH of the blood, thus increasing the time before blood becomes acidic enough so the respiratory center becomes stimulated, as described above. 
The primary urge to breathe is triggered by rising carbon dioxide (CO2) levels in the bloodstream. Carbon dioxide builds up in the bloodstream when oxygen is metabolized and it needs to be expelled as a waste product. The body detects carbon dioxide levels very accurately and relies on this as the primary trigger to control breathing. Hyperventilation artificially depletes the resting concentration of carbon dioxide causing a low blood carbon dioxide condition called hypocapnia. Hypocapnia reduces the reflexive respiratory drive, allowing the delay of breathing and leaving the diver susceptible to loss of consciousness from hypoxia. For most healthy people, the first sign of low oxygen levels is a greyout or unconsciousness: there is no bodily sensation that warns a diver of an impending blackout.
If a person loses consciousness underwater, there is considerable danger that they will drown. An alert diving partner or nearby lifeguard would be in the best position to rescue such a person. Static apnea blackout occurs at the surface when a motionless diver holds their breath long enough for the circulating oxygen in blood to fall below that required for the brain to maintain consciousness. It involves no pressure changes in the body and is usually performed to enhance breath-hold time. It should never be practiced alone, but under strict safety protocols with a safety guard or equipment beside the diver.
Answer: Hyperventilation does not increase the amount of oxygen available but artificially decreases carbon dioxide (CO2) levels in blood and lungs. 
Because the primary urge to breathe is triggered by rising carbon dioxide levels, it gives false impression that one does not need to breath. This can lead to sudden lost of consciousness. If this happens underwater, there is a considerable risk of drowning. 
This is why hyperventilation is considered as dangerous and should be avoided.
Question: Tell me whether these are states or cities: Alabama, Alaska, Wyoming, Washinton DC, Salt Lake city, San Francisco
Answer: States: Alabama, Alaska, Wyoming
Cities: Washinton DC, Salt Lake city, San Francisco
Question: Will being fully autonomous change the future of car design ?
Answer: If cars reach a stage of full autonomy and no longer need input to drive from a human, this can radically change the interior and exterior of a car. There might not be a need for a driver cockpit and the steering console can be completely removed and potentially all seats could be inward facing to maximize human interaction. All windows can be removed and replaced with tablets. Removing glass windows and replacing it with a more protective alloy shell,  also means potentially increasing the safety of the passengers. Size and shape of the vehicles will also change as they can become containerized, where multiple vehicles can interact with each other and lock in and out, much like how trains work.
Question: Who was the 45th President of the United States of America?
Answer: Donald J. Trump was the 45th President of the United States of America
Question: How does the abstractor proposed by the author solve the problem of limitation?
Evidence: While most current neural abstractive summarization models have focused on summarizing relatively short news articles (e.g., BIBREF16 ), few researchers have started to investigate the summarization of longer documents by exploiting their natural structure. agents present an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. The encoding task is divided across several collaborating agents, each is responsible for a subsection of text through a multi-layer LSTM with word attention. Their model seems however overly complicated when it comes to the extractive summarization task, where word attention is arguably much less critical. So, we do not consider this model further in this paper.
discourselongdocument also propose a model for abstractive summarization taking the structure of documents into consideration with a hierarchical approach, and test it on longer documents with section information, i.e. scientific papers. In particular, they apply a hierarchical encoder at the word and section levels. Then, in the decoding step, they combine the word attention and section attention to obtain a context vector.
This approach to capture discourse structure is however quite limited both in general and especially when you consider its application to extractive summarization. First, their hierarchical method has a large number of parameters and it is therefore slow to train and likely prone to overfitting. Secondly, it does not take the global context of the whole document into account, which may arguably be critical in extractive methods, when deciding on the salience of a sentence (or even a word). The extractive summarizer we present in this paper tries to address these limitations by adopting the parameter lean LSTM-minus method, and by explicitly modeling the global context.
The extractive summarizer we present in this paper tries to address these limitations by adopting the parameter lean LSTM-minus method, and by explicitly modeling the global context.
Answer: By adopting the parameter lean LSTM-minus method, and by explicitly modeling the global context.
Question: What are the interesting facts about Shakespeare?
Answer: - Shakespeare's father made gloves for a living.
- Shakespeare had seven siblings.
- Shakespeare married an older, pregnant lady at 18.
- Shakespeare was an actor, as well as a writer.
Question: Why do the San Francisco 49ers play in Santa Clara, California?
Answer: The San Francisco 49ers originally played at Kezar Stadium in San Francisco before moving to Candlestick Park in 1971.  When negotiations to build a new stadium fell apart they worked with the city of Santa Clara to build a new stadium which was located next to their training facilities.  Because of brand recognition and close proximity, the decision was made to keep the name, San Francisco 49ers.
Question: What is the answer to life, the universe and everything?
Answer: Douglas Adams would have us believe that the answer to life, the universe and everything is forty-two. However he clearly hadn't anticipated the challenges of training large language models where an answer of such succinct elegance would have caused the LLM to stop dead in its tracks pretty quickly. On the other hand, assuming that the model was already trained and that even though Adam's supercomputer took seven and a half million years to come up with the answer forty-two, perhaps he was more prescient than even he realised at the time. It's quite a conundrum. Maybe his model was simply too large? Or maybe it was just the right size and it was the compute that was the issue? If Deep Thought had followed a distributed compute model perhaps involving all the planets of the universe, rather than simply relying on the Earth, which always seemed like a single node build, then perhaps his answer would have been richer in its philosophical content. Then again, perhaps a distributed model would simply have arrived at the answer forty-two a lot faster before the first micro-organisms showed up on earth and we wouldn't be here trying to answer this question now.