DataHammer/scimrc huggingface dataset:
Question: How strong is the correlation between the prevalence of the #MeToo movement and official reports [of sexual harassment]?
Evidence: FLOAT SELECTED: Table 2: Linear regression results.
We examine other features regarding the characteristics of the studied colleges, which might be significant factors of sexual harassment. Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest). We also use the normalized rape-related cases count (number of cases reported per student enrolled) from the stated government resource as another attribute to examine the proximity of our dataset to the official one. This feature vector is then fitted in a linear regression to predict the normalized #metoo users count (number of unique users who posted #MeToo tweets per student enrolled) for each individual college.
FLOAT SELECTED: Table 2: Linear regression results.
We also use the normalized rape-related cases count (number of cases reported per student enrolled) from the stated government resource as another attribute to examine the proximity of our dataset to the official one. This feature vector is then fitted in a linear regression to predict the normalized #metoo users count (number of unique users who posted #MeToo tweets per student enrolled) for each individual college.
Answer: 0.9098 correlation
Question: How many languages do they experiment with?
Evidence: In this section we detail the discussions we use to test our metric and how we determine the ground truth (i.e. if the discussion is controversial or not). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.
In this section we detail the discussions we use to test our metric and how we determine the ground truth (i.e. if the discussion is controversial or not). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.


Answer: The answers are shown as follows:
* four different languages: English, Portuguese, Spanish and French
Question: What does generative text models good at according to the authors?
Evidence: Language models can be optimized to recognize syntax and semantics with great accuracy BIBREF0. However, the output generated can be repetitive and generic leading to monotonous or uninteresting responses (e.g “I don't know”) regardless of the input BIBREF1. While application of attention BIBREF2, BIBREF3 and advanced decoding mechanisms like beam search and variation sampling BIBREF4 have shown improvements, it does not solve the underlying problem. In creative text generation, the objective is not strongly bound to the ground truth—instead the objective is to generate diverse, unique or original samples. We attempt to do this through a discriminator which can give feedback to the generative model through a cost function that encourages sampling of creative tokens. The contributions of this paper are in the usage of a GAN framework to generate creative pieces of writing. Our experiments suggest that generative text models, while very good at encapsulating semantic, syntactic and domain information, perform better with external feedback from a discriminator for fine-tuning objectiveless decoding tasks like that of creative text. We show this by evaluating our model on three very different creative datasets containing poetry, metaphors and lyrics.
Previous work on handling the shortcomings of MLE include length-normalizing sentence probability BIBREF5, future cost estimation BIBREF6, diversity-boosting objective function BIBREF7, BIBREF1 or penalizing repeating tokens BIBREF8. When it comes to poetry generation using generative text models, Zhang and Lapata BIBREF9, Yi et al. BIBREF10 and Wang et al. BIBREF11 use language modeling to generate Chinese poems. However, none of these methods provide feedback on the quality of the generated sample and hence, do not address the qualitative objective required for creative decoding. For the task of text generation, MaskGAN BIBREF12 uses a Reinforcement Learning signal from the discriminator, FMD-GAN BIBREF13 uses an optimal transport mechanism as an objective function. GumbelGAN BIBREF14 uses Gumbel-Softmax distribution that replaces the non-differentiable sample from a categorical distribution with a differentiable sample to propagate stronger gradients. Li et al. BIBREF1 use a discriminator for a diversity promoting objective. Yu et al. BIBREF15 use SeqGAN to generate poetry and comment on the performance of SeqGAN over MLE in human evaluations, encouraging our study of GANs for creative text generation. However, these studies do not focus solely on creative text.
Our experiments suggest that generative text models, while very good at encapsulating semantic, syntactic and domain information, perform better with external feedback from a discriminator for fine-tuning objectiveless decoding tasks like that of creative text.
Answer: Encapsulating semantic, syntactic and domain information.
Question: What do you do when the engine in your single engine airplane fails in flight?
Answer: While it is rare every pilot should be prepared in the event.  The first step is to stay calm.  Remember you need to aviate, navigate and communicate in that order.  You should have the planes best glide speed memorized.  Next establish that glide speed.  Then consult the checklist for engine failure and restart procedures.  In event it does not restart look for suitable landing site.  If at all possible find a site that allows for a landing into the wind.  Declare an emergency, convey location if possible.  Refer to emergency landing procedures with a non-operating engine.
Question: In addition to datasets such as UCI and superconductivity, it would be good to show results on standard datasets such as CIFAR10, CIFAR100, and MNIST by treating one as source and the other one as target?
Evidence: We consider the experiment proposed in where a synthetic digits data set: SYNTH is used to learn a classification task for a data set of real digits pictures: SVHN (Street-View House Number). Both data sets are composed of around 65k images of size 28 × 28.. In both cases, the network is pre-trained on 30 epochs with a batch size of 128 and a learning rate of 0.001, for the adversarial training the trade-off parameter λ is set to 0.1 following the setup of. After the query process, a Balance Weighting training is performed with the source and target labeled data using the same optimization hyper-parameters than before. Experiments are conducted 8 times for K between 10 and 500.
Results: Figure.B and 3.C correspond to the evolution of accuracy with respect to K for the experiments conducted with the features obtained respectively without and with adversarial training. We observe that, for any K > 0, K-medoids+W provides improved results over other query strategies in both cases. This highlights the ability of the method to select informative target points in a variety of scenarios.
We consider the experiment proposed in (Ganin et al., 2016) where a synthetic digits data set: SYNTH is used to learn a classification task for a data set of real digits pictures: SVHN (Street-View House Number) (Netzer et al., 2011). Both data sets are composed of around 65k images of size 28 × 28.
Answer: Please notice that two experiments have been conducted on standard data sets for domain adaptation: Synth vs SVHN [Ganin et al., 2016] (which are digits data sets like MNIST) and Office [Saenko et al., 2010] (which is a data set of real images like CIFAR).
Question: Tell me whether these are nouns or proper nouns: United States of America, playground, rope, St. Petersburg, Miami, ocean
Answer: United States of America: Proper Noun
Playground: Noun
Rope: Noun
St. Petersburg: Proper Noun
Miami: Proper Noun
Ocean: Noun
Question: What are some reasons you might be late to a party?
Answer: Traffic, leaving work late, having to stop for gas
Question: A dialogue between two characters discussing the impact of social media on their lives. Both positive and negative points
Answer: Person 1: Hey, what happened? Why are you sad?
Person 2:  I'm sad because my grades are low.
Person 1: But you used always study on your iPad... where did you go
wrong?
Person 2: Yeh... I was using my iPad but didn't study…
I was engaging myself in other things.
Person 1: Ohh..! That isn't good. See, my friend, multitasking has become a common thing in our life.
But we should know how to manage it all. We are too dependent on smartphones for everything
like work, school, shopping and personal life. We are addicted to smartphones, the internet and social media
Social media is harming us in many ways like
* It is stealing our personal life
﻿﻿Increase of cybercrime and cyberbullying.
﻿﻿Depression anxiety in children
*Addiction to games on other social networking sites.
* Causes emotional damage to a person, which leads to suicide.
* Mental illness. , etc.

But on the other hand, there are some positive aspects to note as well
 Internet and social media help families stay connected
It helps in online banking and paying bills. And
It helps in online learning and remote working
It can be an effective tool for marketing
5. Information seeking
So, my dear friend internet and social media have their own merits and demerits. Use it wisely and for a good cause.
Question: Which model architecture do they use to obtain representations?
Evidence: To enable sentence-level classification we need to obtain a sentence representation from the word vectors INLINEFORM0 . We achieved this by using a BiLSTM with max pooling, which was shown to be a good universal sentence encoding mechanism BIBREF13 .
To enable sentence-level classification we need to obtain a sentence representation from the word vectors INLINEFORM0 . We achieved this by using a BiLSTM with max pooling, which was shown to be a good universal sentence encoding mechanism BIBREF13 .
Answer: The answers are shown as follows:
* BiLSTM with max pooling
Question: How many slices are in a large pizza?
Answer: There are 8 slices of pizza in a large pizza pie.  If you double-cut you can get 16 smaller slices.
Question: Do I need Hadoop to run Spark?
Answer: No, but if you run on a cluster, you will need some form of shared file system (for example, NFS mounted at the same path on each node). If you have this type of filesystem, you can just deploy Spark in standalone mode.
Question: Is success important?
Answer: Absolutely not. If you what you do (anything that is legal and ethical), then that's what's important. Love the process more and worry less about the result.
Question: How are discourse features incorporated into the model?
Evidence: CNN2-PV. This model (Figure FIGREF10 , left+center) featurizes discourse information into a vector of relation probabilities. In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). The GR features are represented as grammatical relation transitions derived from the entity grid, e.g., INLINEFORM0 . The RST features are represented as RST discourse relations with their nuclearity, e.g., INLINEFORM1 . The probability vectors are then distributions over relation types. For GR, the vector is a distribution over all the entity role transitions, i.e., INLINEFORM2 (see Table TABREF2 ). For RST, the vector is a distribution over all the RST discourse relations, i.e., INLINEFORM3 Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer.
In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). 
Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer.
Answer: They derive entity grid with grammatical relations and RST discourse relations and concatenate them with pooling vector for the char-bigrams before feeding to the resulting vector to the softmax layer.
Question: Who is Alexander McGuire?
Answer: Alexander McGuire (born 5 October 1932) is a New Zealand cricketer. He played in four first-class matches for Central Districts in 1957/58.
Question: How often should I walk my adult dog?
Answer: Depending on the age of your dog, you may find that your dog likes to take walks 3-6 times a day. To make sure your adult dog pees and poops regularly during the day, your dog would appreciate a walk at least every 6 hours. Walks usually last around 15 minutes but the longer the walk the better.
Question: Ms. Jefferson, a 47-year-old secretary, has contacted you to assess her body composition. Over the years, she has gradually gained weight because of her sedentary lifestyle and changing metabolism. Besides adopting healthy dietary and exercise habits, how can she build self-esteem and improve her body image?
Answer: Mental health plays a big factor in building self-esteem and improving her own body image. Tearing away from negative cycles of thoughts, such as the way she self-describes or visualizes her own body, can be powerful in improving her own self-esteem. For example, a shift from focusing on what her body looks like to what it can do is a popular strategy; instead of focusing on the quantitative metrics of how big her legs are, for example, she can focus on the fact that her legs are strong and allow her to swim, run, hike, and do many other things well. By extension, this also involves things that are outside of physical appearance, such as focusing on great qualities she has that have nothing to do with physical fitness nor appearance: this can help with self-esteem. Finally, she can be mindful of how she perceives not only herself, but other people: is she judging them also by their physical appearance? If so, she can work hard to change that mindset, and begin appreciating things beyond just how people look, and focus on the positive aspects.
Question: Can the leveraging role information enable more nuanced, on-topic and natural dialog generation?
Evidence: Large repositories of textual communications (e.g. forum and microblog posts) have gained recent popularity as proxies for dialog BIBREF0, BIBREF1, BIBREF2. However, conversations in these settings differ from natural dialog: turns may be sparsely scattered over a large temporal span, contain distinct syntax and vocabulary BIBREF3, and differ greatly in formality and focus BIBREF4. In this paper, we investigate how appropriate such data is for modeling natural dialog, and introduce Interview, a new high-quality large-scale open-domain conversational dataset grounded in interview settings with annotations for specific speaker roles.
We compare the performance of state-of-the-art language models fine-tuned on Interview and other popular conversational datasets, demonstrating that Interview contains more complex dialog and better models the characteristics of natural spoken conversations. Our dataset is an order of magnitude larger than existing high-quality natural dialog datasets and contains speaker role annotations for each turn, facilitating the development of conversational agents and assistive systems for settings involving specific speaker roles, such as doctor-patient interviews or hosted talk shows.
In particular, we explore the tasks of role modeling in media dialog and role change detection on Interview and find that leveraging role information can enable more nuanced, on-topic and natural dialog generation, as well as improve role change classification performance.
In summary, we present Interview, the first large-scale open-domain media dialog dataset. We explore two tasks for which it serves as a promising benchmark dataset: speaker role modeling and speaker change detection. We build simple yet strong models to show quantitatively that role labels from Interview improve performance on such tasks. Interview's scale, spoken origins, role diversity, and complex utterances make it a better source for grounded open-domain conversations.
In particular, we explore the tasks of role modeling in media dialog and role change detection on Interview and find that leveraging role information can enable more nuanced, on-topic and natural dialog generation, as well as improve role change classification performance.
Answer: Yes, it can.
Question: Based on this paragraph, what are the main applications of DNA Sequencing?
Evidence: DNA sequencing may be used to determine the sequence of individual genes, larger genetic regions (i.e. clusters of genes or operons), full chromosomes, or entire genomes of any organism. DNA sequencing is also the most efficient way to indirectly sequence RNA or proteins (via their open reading frames). In fact, DNA sequencing has become a key technology in many areas of biology and other sciences such as medicine, forensics, and anthropology.
Answer: DNA sequencing, can be used in many areas of healthcare and life sciences. For example, it can be used to identify the entire genome of an organism, or be used in areas such as medicine, forensics and anthropology.
Question: Given this paragraph about Mohamed Fourati, at what age was he appointed as hospital assistant?
Evidence: A year after getting his philosophy baccalaureate in his native town Sfax, Mohamed Fourati moved to Lille (France) to pursue his graduate education in the medical school of Lille. In order to validate his studies, he became an intern in the Farhat-Hachad Hospital of Sousse. Traveling between the two continents, he secured, in 1959, a medical doctorate at the medical school of Lille. He performed his military service, the following year, in the region of El Kef (in north-eastern Tunisia) and in Kébili (southern Tunisia). He continued to specialize in thoracic and cardiovascular surgery at the Saint-Joseph Hospital of Paris. He got assigned to the general surgery unit of doctors Saïd Mestiri and Zouhair Essafi at the Habib-Thameur Hospital in Tunis. In 1961, over the month of July, he worked as a surgeon in the battle of Bizerte, performing surgery on patients 20 hours a day for many days. He got appointed as hospital assistant in 1966, then earned the title of chief physician of the surgical ward in the Habib-Thameur Hospital in May 1968 and became, at the age of 35, the youngest chief physician in Tunisia. In November of the same year, he performed the first open-heart surgery, favored by a delegation of doctors of the IFTHD (led by Charles Hahn of the Cantonal hospital of Geneva (Switzerland) ).
Answer: He was appointed hospital assistant in 1966. In 1968, he was 35 years old. So in 1966, he was 33 years old.
Question: Why is the Cave of the Crystals or Giant Crystal Cave closed to the public?
Evidence: Cave of the Crystals or Giant Crystal Cave (Spanish: Cueva de los cristales) is a cave connected to the Naica Mine at a depth of 300 metres (980 ft), in Naica, Chihuahua, Mexico. It takes the form of a chamber within the limestone host rock of the mine, and is about 109-metre (358 ft) long with a volume of 5,000 to 6,000 cubic metres (180,000 to 210,000 cu ft).

The chamber contains giant selenite crystals (gypsum, CaSO4 · 2 H2O), some of the largest natural crystals ever found. The largest is 11.40 metres (37.4 ft), with a volume of about 5 cubic metres (180 cu ft), and an estimated mass of 12 tonnes. When not flooded, the cave is extremely hot, with air temperatures reaching up to 58 °C (136 °F) with 90 to 99 percent humidity. This is comparable to temperature records in Death Valley, but with much wetter air. The cave is relatively unexplored because of these factors. Without proper protection, people can only endure approximately ten minutes of exposure at a time.

The cave was discovered in April 2000 by brothers Juan and Pedro Sánchez while drilling in the mine. As of October 2015, the mine had reflooded and the cavern filled once more with the water rich in minerals required for the crystals to grow.

A group of scientists in the Naica Project have been studying these caverns.
Answer: The conditions in the cave are not hospitable, extreme heat and humidity, sharp slippery crystals are lethal for humans.
Question: List down some important points about Indore city in India from given text
Evidence: Indore is the largest and most populous city in the Indian state of Madhya Pradesh. It serves as the headquarters of both Indore District and Indore Division. It is also considered as an education hub of the state and is the only city to encompass campuses of both the Indian Institute of Technology and the Indian Institute of Management. Located on the southern edge of Malwa Plateau, at an average altitude of 553 meters (1,814 ft) above sea level, it has the highest elevation among major cities of Central India. The city is 190 km (120 mi) west of the state capital of Bhopal. Indore had a census-estimated 2011 population of 1,994,397 (municipal corporation) and 3,570,295 (urban agglomeration). The city is distributed over a land area of just 530 square kilometres (200 sq mi), making Indore the most densely populated major city in the central province. Indore is the cleanest city in India according to Swachh Survekshan Report 2022 sixth time in a row, conducted by MoHUA the world's largest urban sanitation and cleanliness survey.
Answer: 1.	Indore is a the largest and most populated city in Madhya Pradesh, India.
2.	Indore is the education hub of the state with IIT and IIM.
3.	It is located on the southern edge of Malwa Plateau, at an average altitude of 553 meters (1,814 ft) above sea level, The city is 190 km (120 mi) west of the state capital of Bhopal.
4.	Indore has population of 1,994,397  based on census 2011 population and 3,570,295 (urban agglomeration).
5.	Indore city is distributed over a area of 530 square kilometres (200 sq mi). 
6.	Sixth time in a row Indore is the cleanest city in India according to Swachh Survekshan Report 2022.
Question: Classify each of the following as either a dog breed or a cat breed: Himalayan, snowshoe, Norwegian Forest, American Eskimo, keeshond, Persian, Jack Russell terrier
Answer: Dog breed: American Eskimo, keeshond, Jack Russell terrier
Cat breed: Himalayan, snowshoe, Norwegian Forest, Persian
Question: Please classify the following as either cities or states: Chicago, Massachusetts, Boston, Alabama, Alaska, and Phoenix.
Answer: Chicago, Phoenix, and Boston are cities. Massachusetts, Alabama, and Alaska are states.
Question: Categorize these world leaders as male or female: Angela Merkel, Margaret Thatcher, Ronald Reagan, Vladimir Putin, Mao Tse Tung, Winston Churchill, Queen Elizabeth I
Answer: Angela Merkel - Female
Margaret Thatcher - Female
Ronald Reagan - Male
Vladimir Putin - Male
Mao Tse Tung - Male
Winston Churchill - Male
Queen Elizabeth I - Female