DataHammer/scimrc huggingface dataset:
Question: What are the titans?
Evidence: Attack on Titan (Japanese: 進撃の巨人, Hepburn: Shingeki no Kyojin, lit. 'The Advancing Giant') is a Japanese manga series written and illustrated by Hajime Isayama. It is set in a world where humanity is forced to live in cities surrounded by three enormous walls that protect them from gigantic man-eating humanoids referred to as Titans; the story follows Eren Yeager, who vows to exterminate the Titans after they bring about the destruction of his hometown and the death of his mother. It was serialized in Kodansha's monthly magazine Bessatsu Shōnen Magazine from September 2009 to April 2021, with its chapters collected in 34 tankōbon volumes.
Answer: In Attack on Titan, the Titans are gigantic man-eating humanoids that destroyed Eren Yeager's hometown and caused the death of his mother. The Titans cause humanity to live in cities surrounded by three enormous walls to protect themselves.
Question: What are some good activities to do outside on a nice day?
Answer: Take a bike ride, go for a walk, go for a jog, climb a mountain, go for a swim, sit outside and read, have a picnic.
Question: How are the three basic operations (Reduce, expand, permute) performed using fast tensor operations on GPU?
Evidence: We compare the time and memory complexity of SpaLoc against NLM on the Has-Sister and Grandparent tasks. In Fig. 5, we plot the curve of memory consumption and inference time of each sample as a function of the number of objects in the evaluation domains. The experimental results show that our method can reduce the space complexity from the original O(n3) complexity of NLM to the O(n2) complexity required to solve the has-sister and grandparent task. In terms of the inference time, there is a constant level of optimization. On small graphs, adding sparsity does not necessarily improve the running time on GPUs because of the overhead of handling sparse tensors
due to the implementation. More efficient implementation of sparse operations is interesting future work.
Answer: We have added a runtime comparison between NLM and SpaLoc in the appendix, showing that there is a constant level of optimization. We would like to reiterate that SpaLoc focuses on improving inference-time memory efficiency. We are the first model that can scale up hyper-graphs to real-world knowledge graph datasets.
Figure 4 in the main paper shows the number of non-zero entries (NNZ) in the intermediate groundings of SpaLoc, which has a linear relationship with the computation complexity. Theoretically, the baseline NLM has O(n^3) complexity on all three tasks, while our model is O(n^2). On small graphs, adding sparsity does not necessarily improve the running time on GPUs because of the overhead of handling sparse tensors due to the implementation. More efficient implementation of sparse operations is interesting future work.
Question: How to make movies?
Answer: To make movies, you need to have a few things - money, talent and contacts. First, pick a story. If you don’t like existing ones you can write one. Next, decide on starcast that fits the story.  Next you want to find a director for the movie. After that comes filming. Once the film is ready  after edits, you are ready to distribute and rake in some money.
Question: Tell me whether these people are DJs or singers: David Guetta, Avicii, Rihanna, Beyonce, Martin Garrix, Calvin Harris, Taylor Swift
Answer: David Guetta: DJ
Avicii: DJ
Rihanna: Singer
Beyonce: Singer
Martin Garrix: DJ
Calvin Harris: DJ
Taylor Swift: Singer
Question: Extract all the academic degrees from the text
Evidence: Schmidt was born in Falls Church, Virginia, and grew up in Falls Church and Blacksburg, Virginia. He is one of three sons of Eleanor, who had a master's degree in psychology, and Wilson Emerson Schmidt, a professor of international economics at Virginia Tech and Johns Hopkins University, who worked at the U.S. Treasury Department during the Nixon Administration. Schmidt spent part of his childhood in Italy as a result of his father's work and has stated that it had changed his outlook.

Schmidt graduated from Yorktown High School in the Yorktown neighborhood of Arlington County, Virginia, in 1972, after earning eight varsity letter awards in long-distance running. He attended Princeton University, starting as an architecture major and switching to electrical engineering, earning a Bachelor of Science in Engineering degree in 1976.

From 1976 to 1980, Schmidt resided at the International House Berkeley, where he met his future wife, Wendy Boyle.

In 1979, at the University of California, Berkeley, Schmidt earned an M.S. degree for designing and implementing a network (Berknet) linking the campus computer center with the CS and EECS departments. There, he also earned a PhD degree in 1982 in EECS, with a dissertation about the problems of managing distributed software development and tools for solving these problems.
Answer: Master's Degree in Psychology
Bachelor of Science in Engineering
Master's of Science
PhD in EECS
Question: Tell me whether each of these are green or blue: grass, water, leaf, sky, sapphire, kale
Answer: Grass: Green
Water: Blue
Leaf: Green
Sky: Blue
Sapphire: Blue
Kale: Green
Question: What is the Capitol
Answer: The United States Capitol, often called The Capitol or the Capitol Building, is the seat of the United States Congress, the legislative branch of the federal government. It is located on Capitol Hill at the eastern end of the National Mall in Washington, D.C. Though no longer at the geographic center of the federal district, the Capitol forms the origin point for the street-numbering system of the district as well as its four quadrants.

Central sections of the present building were completed in 1800. These were partly destroyed in the 1814 Burning of Washington, then were fully restored within five years. The building was enlarged in the 1850s by extending the wings for the chambers for the bicameral legislature, the House of Representatives in the south wing and the Senate in the north wing. The massive dome was completed around 1866 just after the American Civil War. Like the principal buildings of the executive and judicial branches, the Capitol is built in a neoclassical style and has a white exterior. Both its east and west elevations are formally referred to as fronts, though only the east front was intended for the reception of visitors and dignitaries.
Question: what is cricket and why is it popular in Asian countries?
Answer: Cricket is one of the most popular sports in Asian countries. One of the worlds biggest cricket teams are India. The reason for so much popularity is multifold. Top reason is when English invaded South east asia they brought this game to the continent. Originally this game was only played by upper class, and later adopted by other classes as well. To play cricket you need one bat and one ball. Bat will be made up of wood and ball can be either tennis ball or hard ball which is made up of leather. You need a wicket which is 3 sticks and 2 bails on top. One person throws the ball and other person try to protect his wickets with the bat. The length of the wicket is 22 yards. Batsman can hit the ball for runs. To make runs, there are two ways. First is to hit the boundary, which can fetch either 4 or 6 runs. Second way is to run between the wickets and make as many runs as you can. There are other ways to get runs as well, like if its a wide ball or no ball. For a team to win a match, they either have to make more runs then the other team or get them all out before they can make more runs then them. Most popular cricket format is T20, then One day internationals (ODI) and then Test matches. T20 is the shortest form and have 20 overs and 2 innings. ODI has 50 overs and 2 innings. Whereas Test is the longest format and can carry on for 5 days, with 90 overs every day and up to 2 innings per team. Both men and women play cricket. Worlds biggest cricket league is IPL in india. PSL in Pakistan is considered as the second best league. There are 2 cricket worldcups. T20 worldcup is played every 2 years and ODI worldcup is played every 4 years. There is also World Test and ODI championship as well, which is player every year.
Question: What is the state of the art?
Evidence: FLOAT SELECTED: Table 1: Results of RobBERT fine-tuned on several downstream tasks compared to the state of the art on the tasks. For accuracy, we also report the 95% confidence intervals. (Results annotated with * from van der Burgh and Verberne (2019), ** = from de Vries et al. (2019), *** from Allein et al. (2020))
We replicated the high-level sentiment analysis task used to evaluate BERTje BIBREF8 to be able to compare our methods. This task uses a dataset called Dutch Book Reviews Dataset (DBRD), in which book reviews scraped from hebban.nl are labeled as positive or negative BIBREF19. Although the dataset contains 118,516 reviews, only 22,252 of these reviews are actually labeled as positive or negative. The DBRD dataset is already split in a balanced 10% test and 90% train split, allowing us to easily compare to other models trained for solving this task. This dataset was released in a paper analysing the performance of an ULMFiT model (Universal Language Model Fine-tuning for Text Classification model) BIBREF19.
FLOAT SELECTED: Table 1: Results of RobBERT fine-tuned on several downstream tasks compared to the state of the art on the tasks. For accuracy, we also report the 95% confidence intervals. (Results annotated with * from van der Burgh and Verberne (2019), ** = from de Vries et al. (2019), *** from Allein et al. (2020))
This dataset was released in a paper analysing the performance of an ULMFiT model (Universal Language Model Fine-tuning for Text Classification model) BIBREF19.
Answer: The answers are shown as follows:
* BERTje BIBREF8
* an ULMFiT model (Universal Language Model Fine-tuning for Text Classification model) BIBREF19.
* mBERT
Question: How do they evaluate generated stories?
Evidence: We then ask a separate set of Turkers to rate the stories for overall quality and the three improvement areas. All ratings are on a five-point scale. We collect two ratings per story, and throw out ratings that disagree by more than 2 points. A total of 11% of ratings were thrown out, leaving four metrics across 241 stories for analysis.
We then ask a separate set of Turkers to rate the stories for overall quality and the three improvement areas. All ratings are on a five-point scale. We collect two ratings per story, and throw out ratings that disagree by more than 2 points. A total of 11% of ratings were thrown out, leaving four metrics across 241 stories for analysis.
Answer: The answers are shown as follows:
* separate set of Turkers to rate the stories for overall quality and the three improvement areas
Question: Tell me if these cities are in the United States or England: New York, Manchester, Bath, San Francisco, Oxford
Answer: New York: United States, Manchester: England, Bath: England, San Francisco: United States, Oxford: England
Question: Tell me whether these are cars or planes: Camaro, Piper Cub, Volvo S60, Shelby GT500, Boeing 737, Cessna, B707, Porsche, Beechcraft
Answer: Cars: Camaro, Volvo S60, Shelby GT500, Porsche
Planes: Piper Cub, Boeing 737, Cessna, B707, Beechcraft
Question: What did the authors train by using the aforementioned higher quality portions of the corpus?
Evidence: Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish.
Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish.
Answer: The authors trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish.
Question: What high school did Paul Allen and Bill Gates attend?
Answer: Lakeside High School in Seattle Washington
Question: why does daylight savings still exist in the United States?
Answer: In the United States, the economy used to be heavily based on agriculture.  Therefore, daylight savings was originally instituted to facilitate a farmer's workday so that much of the working hours could coincide with the hours where there was more daylight (daylight begins is later in the spring). Since then; however, the U.S. economy has become less agriculturally based and also with technological advancements that have drastically impacted American work patterns, the need to follow daylight windows has reduced dramatically. While daylight savings still allows many Americans to spend their working hours during the daylight, there is the popular opinion that daylight savings should no longer be observed in the United States, especially with the confusion that comes with changing hours during the spring and fall.  There are at least a couple states that do not change times for daylight savings which further adds to the confusion.
Question: What makes skiing so much fun?
Answer: Skiing is one of the best recreational activities. Skiing is not only accessible for beginners, but also has a large skill range that can take lifetimes to master. There are many disciplines for skiers to choose from, ranging from speed skiing, moguls, jumps, half pipe, to freeride. Ski resorts are often set in beautiful mountain landscapes and enjoyable mountain towns.
Question: How many years was Hutchinson's streak, and what caused it to end?
Evidence: Hutchinson attended 1,294 consecutive matches involving the Geelong Football Club between Round 11, 1963, and the 2019 preliminary final, inclusive. That streak came to an end when he and the general public were locked out of the Cats' 2020 Round 1 loss to Greater Western Sydney in Sydney due to the COVID-19 pandemic. If away games are excluded, Hutchinson's streak lasted from the early 1958 season to the 2019 preliminary final.
Answer: Hutchinson's streak was 62 years (2019-1958+1) and it ended because of the lock out caused by the COVID-19 pandemic.
Question: How many people live in California?
Answer: California is a state in the Western United States, located along the Pacific Coast. With nearly 39.2 million residents across a total area of approximately 163,696 square miles (423,970 km2), it is the most populous U.S. state and the third-largest by area. It is also the most populated subnational entity in North America and the 34th most populous in the world. The Greater Los Angeles and San Francisco Bay areas are the nation's second and fifth most populous urban regions respectively, with the former having more than 18.7 million residents and the latter having over 9.6 million. Sacramento is the state's capital, while Los Angeles is the most populous city in the state and the second most populous city in the country. San Francisco is the second most densely populated major city in the country. Los Angeles County is the country's most populous, while San Bernardino County is the largest county by area in the country. California borders Oregon to the north, Nevada and Arizona to the east, the Mexican state of Baja California to the south; and it has a coastline along the Pacific Ocean to the west.

California's economy is the largest of any state within the United States, with a $3.37 trillion gross state product (GSP) as of 2022. It is the largest sub-national economy in the world. If California were a sovereign nation, it would rank as the world's fifth-largest economy as of 2022, behind India and ahead of the United Kingdom, as well as the 37th most populous. The Greater Los Angeles area and the San Francisco area are the nation's second- and fourth-largest urban economies ($1.0 trillion and $0.6 trillion respectively as of 2020), following the New York metropolitan area's $1.8 trillion. The San Francisco Bay Area Combined Statistical Area had the nation's highest gross domestic product per capita ($106,757) among large primary statistical areas in 2018, and is home to five of the world's ten largest companies by market capitalization and four of the world's ten richest people. Slightly over 84 percent of the state's residents hold a high school degree, the lowest high school education rate of all 50 states.
Question: What is a side slide in aviation?
Answer: It is a technique used to compensate for a crosswind.  It is used primarily during takeoff and landing.  It is first established by lowering the upwind wing using aileron by moving the yoke or stick in the direction of the wind.  Then apply enough rudder to keep from turning.
Question: What is one formidable constraint of deep neural networks (DNN) mentioned in this paper?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train.
Answer: One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train.
Question: what are the baselines?
Evidence: As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default. As a comparison, we design a densely connected model with same number of layers, but the hidden size is set as 128 in order to keep the model size consistent. The models adopting DenseAtt-1, DenseAtt-2 are named as DenseNMT-4L-1 and DenseNMT-4L-2 respectively. In order to check the effect of dense connections on deeper models, we also construct a series of 8-layer models. We set the hidden number to be 192, such that both 4-layer models and 8-layer models have similar number of parameters. For dense structured models, we set the dimension of hidden states to be 96.
As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default.
Answer: The answers are shown as follows:
*  4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256
Question: How do you establish the ground truth of who won a debate?
Evidence: Trend Analysis: We also analyze some certain trends of the debates. Firstly, we look at the change in sentiments of the users towards the candidates over time (hours, days, months). This is done by computing the sentiment scores for each candidate in each of the debates and seeing how it varies over time, across debates. Secondly, we examine the effect of Washington Post on the views of the users. This is done by looking at the sentiments of the candidates (to predict winners) of a debate before and after the winners are announced by the experts in Washington Post. This way, we can see if Washington Post has had any effect on the sentiments of the users. Besides that, to study the behavior of the users, we also look at the correlation of the tweet volume with the number of viewers as well as the variation of tweet volume over time (hours, days, months) for debates.
Next, we investigate how the sentiments of the users towards the candidates change before and after the debate. In essence, we examine how the debate and the results of the debates given by the experts affects the sentiment of the candidates. Figure FIGREF25 shows the sentiments of the users towards the candidate during the 5th Republican Debate, 15th December 2015. It can be seen that the sentiments of the users towards the candidates does indeed change over the course of two days. One particular example is that of Jeb Bush. It seems that the populace are generally prejudiced towards the candidates, which is reflected in their sentiments of the candidates on the day of the debate. The results of the Washington Post are released in the morning after the debate. One can see the winners suggested by the Washington Post in Table TABREF35. One of the winners in that debate according to them is Jeb Bush. Coincidentally, Figure FIGREF25 suggests that the sentiment of Bush has gone up one day after the debate (essentially, one day after the results given by the experts are out).
Secondly, we examine the effect of Washington Post on the views of the users. This is done by looking at the sentiments of the candidates (to predict winners) of a debate before and after the winners are announced by the experts in Washington Post. This way, we can see if Washington Post has had any effect on the sentiments of the users.
One can see the winners suggested by the Washington Post in Table TABREF35. 
Answer: The answers are shown as follows:
* experts in Washington Post