DataHammer/scimrc huggingface dataset:
Question: Give me a brief history of the construction of Walt Disney World
Evidence: Designed to supplement Disneyland in Anaheim, California, which had opened in 1955, the complex was developed by Walt Disney in the 1960s. "The Florida Project", as it was known, was intended to present a distinct vision with its own diverse set of attractions. Walt Disney's original plans also called for the inclusion of an "Experimental Prototype Community of Tomorrow" (EPCOT), a planned community intended to serve as a testbed for new city-living innovations. Walt Disney died on December 15, 1966, during the initial planning of the complex. After his death, the company wrestled with the idea of whether to bring the Disney World project to fruition; however, Walt's older brother, Roy O. Disney, came out of retirement to make sure Walt's biggest dream was realized. Construction started in 1967, with the company instead building a resort similar to Disneyland, abandoning the experimental concepts for a planned community. The Magic Kingdom was the first theme park to open in the complex, in 1971, followed by Epcot (1982), Disney's Hollywood Studios (1989), and Disney's Animal Kingdom (1998). It was Roy who insisted the name of the entire complex be changed from Disney World to Walt Disney World, ensuring that people would remember that the project was Walt's dream.
Answer: Walt Disney World was designed as a theme park to supplement Disneyland.  It was conceived by Walt Disney in the 1960s and was known as the "The Florida Project".  Walt Disney originally envisioned including a planned community known as the "Experimental Prototype Community of Tomorrow" (EPCOT) which would be a planned community that would test city-living innovations.  As planning progressed, plans for EPCOT evolved to become just a theme park in the complex.  Walt Disney died in 1966 during planning and the company was torn whether to move forward with the project.  Walt Disney's brother, Roy Disney, came out of retirement to see the project successfully completed.  There are four theme parks in the complex.  The Magic Kingdom opened in 1971, followed by Epcot in 1982, Disney's Hollywood Studios in 1989, and Disney's Animal Kingdom in 1998.  Roy Disney insisted the name be changed to Walt Disney World from the original name of just Disney World to remind people that it was Walt's dream.
Question: How do you remove the direct effect of Raf on Mek? Why don't you do the same process for the edge between PLCy and PIP2?
Evidence: In this section, we demonstrate the applicability of our method to a real-world dataset in a semisynthetic setting. The Sachs protein mass spectroscopy dataset is a widely used benchmark for causal discovery, in part due to the existence of a commonly accepted ground truth network over the 11 measured protein expression values, shown in Fig.. We use the 1,755 "observational" samples, where the experimental conditions involve only perturbing receptor enzymes, and not any signaling molecules, as described in. To make the ground truth network more similar to a latent factor causal model, we perform three data-processing steps: (1) we "condition" on PKA, by regressing it out of the dataset, (2) we "remove" the direct effect of Raf on Mek, and (3) we "marginalize" out PIP3 and PKC by removing the corresponding columns from the dataset. We "remove" the direct effect of Raf on Mek as follows. First, we regress Mek on its two remaining parents, Raf and PKC. Call the resulting regression coefficient for Raf β Raf . For each sample, we subtract the value of Raf times the β Raf from the value of Mek. Note that we do not remove the direct effect of PLCγ on PIP2, since then our algorithm collapses all nodes into a single cluster. The processed graph is show in Fig.. Running our method with significance level α = 0.01 for H vt and α = 0.1 for H ci , we obtain the network shown in Fig.. The clustering by our algorithm closely matches the clustering (Akt, PLCγ, PIP2), (p38, JNK, Raf, Mek, Erk) induced by the true network, with the exception that Akt from the first cluster and Erk from the second cluster are pulled out into a cluster with one another, which may indicate that the effect of PKA on Akt and Erk cannot be completely removed using a purely linear approach. The ordering between the clusters (PLCγ, PIP2) and (p38, JNK, Raf, Mek) is preserved, but the edge P IP 2 → L3 is missing.
We “remove” the direct effect of Raf on Mek as follows. First, we regress Mek on its two remaining parents, Raf and PKC. Call the resulting regression coefficient for Raf βRaf . For each sample, we subtract the value of Raf times the βRaf from the value of Mek. Note that we do not remove the direct effect of PLCγ on PIP2, since then our algorithm collapses all nodes into a single cluster. The processed graph is show in Fig. 5b.
Answer: To remove the effect of Raf on Mek, we perform linear regression of Mek on its parents, then we subtract the value of Raf times its corresponding regression coefficient from Mek. If the data were indeed generated by a linear structural causal model, and we were given the true regression coefficients, this would exactly correspond to “removing the direct effect” of Raf on Mek, i.e., setting the corresponding edge parameter to 0.
When we remove the effect of PLCg and on PIP2, all nodes collapse into a single cluster. We intended to note this in the submission and will add a line describing this result.
Question: What is improvement in accuracy for short Jokes in relation other types of jokes?
Evidence: Our experiment with the Short Jokes dataset found the Transformer model's accuracy and F1 score to be 0.986. This was a jump of 8 percent from the most recent work done with CNNs (Table 4).
In Table 2, we see the results of our experiment with the Reddit dataset. We ran our models on the body of the joke exclusively, the punchline exclusively, and both parts together (labeled full in our table). On the full dataset we found that the Transformer achieved an accuracy of 72.4 percent on the hold out test set, while the CNN was in the high 60's. We also note that the general human classification found 66.3% of the jokes to be humorous.
The results on the Pun of the Day dataset are shown in Table 3 above. It shows an accuracy of 93 percent, close to 4 percent greater accuracy than the best CNN model proposed. Although the CNN model used a variety of techniques to extract the best features from the dataset, we see that the self-attention layers found even greater success in pulling out the crucial features.
FLOAT SELECTED: Table 2: Results of Accuracy on Reddit Jokes dataset
FLOAT SELECTED: Table 3: Comparison of Methods on Pun of the Day Dataset. HCF represents Human Centric Features, F for increasing the number of filters, and HN for the use of highway layers in the model. See (Chen and Soo, 2018; Yang et al., 2015) for more details regarding these acronyms.
FLOAT SELECTED: Table 4: Results on Short Jokes Identification
Our experiment with the Short Jokes dataset found the Transformer model's accuracy and F1 score to be 0.986. This was a jump of 8 percent from the most recent work done with CNNs (Table 4).
In Table 2, we see the results of our experiment with the Reddit dataset. We ran our models on the body of the joke exclusively, the punchline exclusively, and both parts together (labeled full in our table). On the full dataset we found that the Transformer achieved an accuracy of 72.4 percent on the hold out test set, while the CNN was in the high 60's. 
The results on the Pun of the Day dataset are shown in Table 3 above. It shows an accuracy of 93 percent, close to 4 percent greater accuracy than the best CNN model proposed. Although the CNN model used a variety of techniques to extract the best features from the dataset, we see that the self-attention layers found even greater success in pulling out the crucial features.
FLOAT SELECTED: Table 2: Results of Accuracy on Reddit Jokes dataset
FLOAT SELECTED: Table 3: Comparison of Methods on Pun of the Day Dataset. HCF represents Human Centric Features, F for increasing the number of filters, and HN for the use of highway layers in the model. See (Chen and Soo, 2018; Yang et al., 2015) for more details regarding these acronyms.
FLOAT SELECTED: Table 4: Results on Short Jokes Identification
Answer: It had the highest accuracy comparing to all datasets 0.986% and It had the highest improvement comparing to previous methods on the same dataset by 8%
Question: What is BRLM designed to do?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
To achieve this goal, BRLM is designed to strengthen the ability to infer words across languages based on alignment information, instead of inferring words within monolingual sentence as in MLM or within the pseudo sentence formed by concatenating sentence pair as in TLM.
Answer: To strengthen the ability to infer words across languages based on alignment information.
Question: What is Long COVID?
Answer: Long COVID is the experience of COVID symptoms a month of more after someone contracts COVID-19. Two of the more common symptoms of long COVID include chronic fatigue and brain fog. The scientific community is still working hard to understand more about this condition, as today there is neither a reliable test to identify Long COVID nor a treatment. It's unclear how many people who contract COVID-19 suffer from Long COVID, but estimates range as high as 5-20%, which is one reason why concern over the prevalence of COVID-19 still persists among public health experts.
Question: Using the information in the paragraph, give me a short bulleted list of Arsenal football club's main achievements
Evidence: Arsenal Football Club is an English professional football club based in Islington, London. Arsenal plays in the Premier League, the top flight of English football. The club has won 13 league titles (including one unbeaten title), a record 14 FA Cups, two League Cups, 16 FA Community Shields, one European Cup Winners' Cup, and one Inter-Cities Fairs Cup. In terms of trophies won, it is the third-most successful club in English football.

Arsenal was the first club from the South of England to join the Football League in 1893, and they reached the First Division in 1904. Relegated only once, in 1913, they continue the longest streak in the top division, and have won the second-most top-flight matches in English football history. In the 1930s, Arsenal won five League Championships and two FA Cups, and another FA Cup and two Championships after the war. In 1970–71, they won their first League and FA Cup Double. Between 1989 and 2005, they won five League titles and five FA Cups, including two more Doubles. They completed the 20th century with the highest average league position. Between 1998 and 2017, Arsenal qualified for the UEFA Champions League for nineteen consecutive seasons.

Herbert Chapman, who changed the fortunes of Arsenal forever, won the club its first silverware, and his legacy led the club to dominate the 1930s decade; Chapman, however, died of pneumonia in 1934, aged 55. He helped introduce the WM formation, floodlights, and shirt numbers; he also added the white sleeves and brighter red to the club's jersey. Arsène Wenger is the longest-serving manager and won the most trophies. He won a record seven FA Cups, and his title-winning team set an English record for the longest top-flight unbeaten league run at 49 games between 2003 and 2004, receiving the nickname The Invincibles.

In 1886, munitions workers at the Royal Arsenal in Woolwich founded the club as Dial Square. In 1913 the club crossed the city to Arsenal Stadium in Highbury, becoming close neighbours of Tottenham Hotspur, and creating the North London derby. In 2006, they moved to the nearby Emirates Stadium. With an annual revenue of £340.3m in the 2019–20 season, Arsenal was estimated to be worth US$2.68 billion by Forbes, making it the world's eighth most valuable club, while it is one of the most followed on social media. The motto of the club has long been Victoria Concordia Crescit, Latin for "Victory Through Harmony".
Answer: * Arsenal FC have won the highest number FA Cups
* Arsenal's "Invincibles" team in the 2003 season was the only team in the premier league's history to go an entire season unbeaten
* Arsenal qualified for the UEFA champions league for 19 consecutive years
Question: How many cities in the world have a population exceeding 5 million people ?
Evidence: There are 81 cities in the world with a population exceeding 5 million people.
Answer: There are 81 cities in the world with a population exceeding 5 million people, according to 2018 estimates by the United Nations. The U.N. figures include a mixture of city proper, metropolitan area, and urban area.
Question: What makes Sachin Tendulkar so special?
Evidence: Sachin Tendulkar is wonderful not because of his cricketing prowess; if that were the case, roughly the same degree of adoration would have been held for Rahul Dravid, who is also in the same league in terms of performance...

164 Tests, 13288 runs at 52.88 versus Sachin's 200 Tests, 15921 runs at 53.86

As you can see, there is nothing to select between the two.

Of course, Sachin has a significant advantage in one-dayers: 39 vs 44 in averages. Yet not nearly enough to deserve Sachin's adoration.
Answer: Sachin Tendulkar is wonderful not because of his cricketing prowess; if that were the case, roughly the same degree of adoration would have been held for Rahul Dravid, who is also in the same league in terms of performance...

164 Tests, 13288 runs at 52.88 versus Sachin's 200 Tests, 15921 runs at 53.86

As you can see, there is nothing to select between the two.

Of course, Sachin has a significant advantage in one-dayers: 39 vs 44 in averages. Yet not nearly enough to deserve Sachin's adoration.

Sachin is great in my opinion mostly because of his age when he first entered the cricket scene; to perform what he did at the age of 16 is incredible. Consider yourself at 16...and then consider Imran, Waqar, and Wasim blasting into you at 16 in front of a partisan crowd, with all of India watching. Consider yourself as a 16-year-old fighting the arch-enemy Pakistan. Imagine yourself at the age of 16 being smashed on the body with a leather ball. Consider yourself at 16 years old, attempting to focus on the ball in front of a roaring crowd.

That is what he did....


And a legend was born!

His achievements from 16 - 22 are the stuff of legend; and that is his primary claim to greatness.


Yes, there are other factors: his simplicity, his focus, his concentration, his determination, his longevity etc... but the primary one is the aspect listed above.


But even that does not explain his sustained claim to greatness over the years,

To this, we must add that he is a fighter who enjoys a good challenge. The manner he dealt with the top fast bowlers of the day, like as Allan Donald. And his famed war with Shane Warne... these battles, and more, when he faced the opposition, are unique to him; almost.


And then there's the fact that he's believed to have the purest game with the widest range of shots... To watch him bat is to witness beauty in action, poetry in motion. There is no better sight than Sachin in full flow...

And the icing on the cake?

He has succeeded in all forms of the game. Try and find me 2 other players who have done that.
Question: How much is pre-training loss increased in Low/Medium/Hard level of pruning?
Evidence: We've seen that over-pruning BERT deletes information useful for downstream tasks. Is this information equally useful to all tasks? We might consider the pre-training loss as a proxy for how much pre-training information we've deleted in total. Similarly, the performance of information-deletion models is a proxy for how much of that information was useful for each task. Figure FIGREF18 shows that the pre-training loss linearly predicts the effects of information deletion on downstream accuracy.
FLOAT SELECTED: Figure 2: (Left) Pre-training loss predicts information deletion GLUE accuracy linearly as sparsity increases. We believe the slope of each line tells us how much a bit of BERT is worth to each task. (CoLA at 90% is excluded from the line of best fit.) (Right) The cosine similarities of features extracted for a subset of the pre-training development data before and after pruning. Features are extracted from activations of all 12 layers of BERT and compared layer-wise to a model that has not been pruned. As performance degrades, cosine similarities of features decreases.
Figure FIGREF18 shows that the pre-training loss linearly predicts the effects of information deletion on downstream accuracy.
FLOAT SELECTED: Figure 2: (Left) Pre-training loss predicts information deletion GLUE accuracy linearly as sparsity increases. We believe the slope of each line tells us how much a bit of BERT is worth to each task. (CoLA at 90% is excluded from the line of best fit.) (Right) The cosine similarities of features extracted for a subset of the pre-training development data before and after pruning. Features are extracted from activations of all 12 layers of BERT and compared layer-wise to a model that has not been pruned. As performance degrades, cosine similarities of features decreases.
Answer: The increase is linearly from lowest on average 2.0 , medium around 3.5, and the largest is 6.0
Question: What encoding do they use in their training?
Evidence: In this paper, we propose a new method for word sense disambiguation problem, which adopts the fixed-size ordinally forgetting encoding (FOFE) to convert variable-length context into almost unique fixed-size representation. A feed forward neural network pseudo language model is trained with FOFE codes of large unlabelled corpus, and used for abstracting the context embeddings of annotated instance to build a k-nearest neighbor classifier for every polyseme. Compared to the high computational cost induced by LSTM model, the fixed-size encoding by FOFE enables the usage of a simple feed forward neural network, which is not only much more efficient but also equivalently promising in numerical performance.
In this paper, we propose a new method for word sense disambiguation problem, which adopts the fixed-size ordinally forgetting encoding (FOFE) to convert variable-length context into almost unique fixed-size representation.
Answer: The fixed-size ordinally forgetting encoding.
Question: Which of these are Pixar movies? Finding Nemo, Shrek, Avatar, Toy Story, Fast and Furious, Up, Inside Out, Turning Red, Everything Everywhere All at Once, John Wick 4, Ice Age, Madagascar, Incredibles 2
Answer: Finding Nemo, Toy Story, Up, Inside Out, Turning Red, and Incredibles 2 are Pixar movies.
Question: What is microeconomics?
Answer: Microeconimics is concerned with decision-making by individual economic agents such as firms and consumers.
Question: Why is the sun more intense at high altitude
Evidence: The amount of heat energy received at any location on the globe is a direct effect of Sun angle on climate, as the angle at which sunlight strikes Earth varies by location, time of day, and season due to Earth's orbit around the Sun and Earth's rotation around its tilted axis. Seasonal change in the angle of sunlight, caused by the tilt of Earth's axis, is the basic mechanism that results in warmer weather in summer than in winter.Change in day length is another factor.

Presents a case when sunlight shines on Earth at a lower angle (Sun closer to the horizon), the energy of the sunlight is spread over a larger area, and is therefore weaker than if the Sun is higher overhead and the energy is concentrated on a smaller area.
Answer: It's commonly known that the sun is more intense at higher altitudes. However, contrary to popular belief, the sun is not more intense due to being closer to the sun, but the angle of incidence of the sun. The angle of incidence is steeper, and therefore the density of the sun's  radiant energy is concentrated to a high density in a smaller area. This makes the sun's energy more intense
Question: What does the term 'Thala' mean?
Answer: The term ‘Thala’ addresses the former Indian cricket captain Mahendra Singh Dhoni (MS Dhoni). He is still the captain, playing for Chennai Super Kings (CSK), an IPL franchise. 

In the Tamil language, ‘Thala’ means head, and it also means ‘Leader’. After M.G.R., Rajinikanth, MS Dhoni is the most favorite person in Tamil Nadu.
Question: How do you improve your basketball handles?
Answer: One of the best ways to improve your basketball handles is to practice beyond dribbling. Rotating the ball around your head, body and legs is a great way to get a better feel for the ball all around your body. Once you can effortlessly move the ball around those three parts of your body, you can start experimenting with handling the ball between your legs in a figure-eight rotation. To further advance that skill, you can start the rotation from your head to your body, to between your legs and back up all in a single session.
Question: What are the 12 categories devised?
Evidence: FLOAT SELECTED: Table 1: Statistics of the corpus - In the Course Level column, (i) represents Institute2 .
FLOAT SELECTED: Table 1: Statistics of the corpus - In the Course Level column, (i) represents Institute2 .
Answer: Economics, Genocide, Geography, History, Human Rights, Kurdish, Kurdology, Philosophy, Physics, Theology, Sociology, Social Study
Question: What dataset is used for training?
Evidence: We use data from Mandarin Chinese and Cantonese. For each language, the data consists of a list of spoken words, recorded by the same speaker. The Mandarin dataset is from a female speaker and is provided by Shtooka, and the Cantonese dataset is from a male speaker and is downloaded from Forvo, an online crowd-sourced pronunciation dictionary. We require all samples within each language to be from the same speaker to avoid the difficulties associated with channel effects and inter-speaker variation. We randomly sample 400 words from each language, which are mostly between 2 and 4 syllables; to reduce the prosody effects with longer utterances, we exclude words longer than 4 syllables.
We use data from Mandarin Chinese and Cantonese. For each language, the data consists of a list of spoken words, recorded by the same speaker. The Mandarin dataset is from a female speaker and is provided by Shtooka, and the Cantonese dataset is from a male speaker and is downloaded from Forvo, an online crowd-sourced pronunciation dictionary.
Answer: The answers are shown as follows:
* Mandarin dataset
* Cantonese dataset