DataHammer/scimrc huggingface dataset:
Question: What are the top 10 suggestions for fillings in Mexican street tacos?
Answer: 1.	Steak (carne asada)
2.	Chicken (pollo)
3.	Beef Cheeks (cabeza)
4.	Braised Beef (barbacoa)
5.	Braised Pork (carnitas)
6.	Pork Marinated with Pineapple (al pastor)
7.	Shrimp (camaron)
8.	Sausage (chorizo)
9.	Fish (pescado)
10.  Beans (frijoles)
Question: Of the following, which are colors of a rainbow: black, red, white, gold, purple, orange, green, blue, yellow, gray
Answer: Red, purple, orange, green, blue, yellow
Question: Is this style generator compared to some baseline?
Evidence: We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7. To generate in a specific style, we simply set the appropriate embedding. We use genre embeddings of size 850 which is equivalent to the total size of the $K$ feature embeddings in the StyleEQ model.
We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7.
Answer: Yes.
Question: Identify which instrument is string or woodwind: Bandol, Soprano clarinet
Answer: Soprano clarinet is woodwind, Bandol is string.
Question: How do you determine the severity of vulnerabilities.
Answer: The severity of vulnerabilities are commonly determined using the common vulnerability scoring system (CVSS). CVSS scores vulnerabilities from 0-10, with 10 being the most critical severity score.
Question: What US embassy was abandoned on April 12th, 1975
Answer: Phnom Penh was abandoned after being besieged by the Khmer Rouge on April 12th 1975
Question: What is a good park in Redmond, WA
Answer: Marymoor Park in Redmond, WA
Question: Tell me whether each airline is American or European: British Airways, United Airlines, Southwest Airlines, Jet Blue, Easy Jet, Ryan Air
Answer: British Airways: European
United Airlines: American
Southwest Airlines: American
Jet Blue: American
Easy Jet: European
Ryan Air: European
Question: Is it possible to perform in local theater while also pursuing a demanding career?
Answer: Yes, however there are many implications that should be thought through, as theater (even at the local, unpaid volunteer level) is a time-consuming and rigorous hobby. 1) Career choice. Restaurants, retail and other careers that required nights and weekends regularly often conflict with rehearsal and performance schedules, which are often all on nights and weekends. 2) Travel. Planned, infrequent travel can usually be planned around. But if your career requires spontaneous travel on a moments notice, it may be difficult for you to commit to the rehearsal and performance schedule, which is often planned months in advance.
Question: Queen Elizabeth II was born on April 21, 1926 in
Answer: Mayfair, London
Question: How does the author verify the results?
Evidence: DNN models learn word embeddings over the training data. These learned embeddings across multiple datasets show the difference in nature and style of bullying across cyberbullying topics and SMPs. Here we report results for BLSTM with attention model. Results for other models are similar. We first verify that important words for each topic of cyberbullying form clusters in the learned embeddings. To enable the visualization of grouping, we reduced dimensionality with t-SNE BIBREF16 , a well-known technique for dimensionality reduction particularly well suited for visualization of high dimensional datasets. Please refer to Table TABREF22 . This table shows important clusters observed in t-SNE projection of learned word embeddings. Each cluster shows that words most relevant to a particular topic of bullying form cluster.
We also observed changes in the meanings of the words across topics of cyberbullying. Table TABREF23 shows most similar words for a given query word for two datasets. Twitter dataset which is heavy on sexism and racism, considers word slave as similar to targets of racism and sexism. However, Wikipedia dataset that is about personal attacks does not show such bias.
We first verify that important words for each topic of cyberbullying form clusters in the learned embeddings. To enable the visualization of grouping, we reduced dimensionality with t-SNE , a well-known technique for dimensionality reduction particularly well suited for visualization of high dimensional datasets.
Answer: They first verify that important words for each topic of cyberbullying form clusters in the learned embeddings. To enable the visualization of grouping, they reduced dimensionality with t-SNE , a well-known technique for dimensionality reduction particularly well suited for visualization of high dimensional datasets.
Question: Which one was manually annotated? Language tags or sentiment tags?
Evidence: The past decade witnessed rapid growth and widespread usage of social media platforms by generating a significant amount of user-generated text. The user-generated texts contain high information content in the form of news, expression, or knowledge. Automatically mining information from user-generated data is unraveling a new field of research in Natural Language Processing (NLP) and has been a difficult task due to unstructured and noisy nature. In spite of the existing challenges, much research has been conducted on user-generated data in the field of information extraction, sentiment analysis, event extraction, user profiling and many more.
According to Census of India, there are 22 scheduled languages and more than 100 non scheduled languages in India. There are 462 million internet users in India and most people know more than one language. They express their feelings or emotions using more than one languages, thus generating a new code-mixed/code-switched language. The problem of code-mixing and code-switching are well studied in the field of NLP BIBREF0 , BIBREF1 . Information extraction from Indian internet user-generated texts become more difficult due to this multilingual nature. Much research has been conducted in this field such as language identification BIBREF2 , BIBREF3 , part-of-speech tagging BIBREF4 . Joshi et al. JoshiPSV16 have performed sentiment analysis in Hindi-English (HI-EN) code-mixed data and almost no work exists on sentiment analysis of Bengali-English (BN-EN) code-mixed texts. The Sentiment Analysis of Indian Language (Code-Mixed) (SAIL _Code-Mixed) is a shared task at ICON-2017. Two most popular code-mixed languages namely Hindi and Bengali mixed with English were considered for the sentiment identification task. A total of 40 participants registered for the shared task and only nine teams have submitted their predicted outputs. Out of nine unique submitted systems for evaluation, eight teams submitted fourteen runs for HI-EN dataset whereas seven teams submitted nine runs for BN-EN dataset. The training and test dataset were provided after annotating the languages and sentiment (positive, negative, and neutral) tags. The language tags were automatically annotated with the help of different dictionaries whereas the sentiment tags were manually annotated. The submitted systems are ranked using the macro average f-score.
The paper is organized as following manner. Section SECREF2 describes the NLP in Indian languages mainly related to code-mixing and sentiment analysis. The detailed statistics of the dataset and evaluation are described in Section SECREF3 . The baseline systems and participant's system description are described in Section SECREF4 . Finally, conclusion and future research are drawn in Section SECREF5 .
The language tags were automatically annotated with the help of different dictionaries whereas the sentiment tags were manually annotated.
Answer: The sentiment tags.
Question: Why do toddlers wake up so early?
Answer: A toddlers sleep patterns are a product of how they are trained as a sleeper from early on in life. Toddlers who were removed from a crib regularly in their early months tend to be inconsistent sleepers into the toddler years. The brain development in a toddlers brain is so malleable, they rely heavily on their inherent circadian rhythms to wake and fall asleep.
Question: How do you play the game of LoL?
Evidence: League of Legends (LoL), commonly referred to as League, is a 2009 multiplayer online battle arena video game developed and published by Riot Games. Inspired by Defense of the Ancients, a custom map for Warcraft III, Riot's founders sought to develop a stand-alone game in the same genre. Since its release in October 2009, League has been free-to-play and is monetized through purchasable character customization. The game is available for Microsoft Windows and macOS.

In the game, two teams of five players battle in player-versus-player combat, each team occupying and defending their half of the map. Each of the ten players controls a character, known as a "champion", with unique abilities and differing styles of play. During a match, champions become more powerful by collecting experience points, earning gold, and purchasing items to defeat the opposing team. In League's main mode, Summoner's Rift, a team wins by pushing through to the enemy base and destroying their "Nexus", a large structure located within.
Answer: League of legends (LoL) is a multiplayer online battle game with a team of 5 players on each side of the arena playing against each other. Each player controls a "champion" with unique abilities and styles of the play. During the play, the champion of each player will become stronger by leveling up and collecting gold, which can be used to buy powerful items. The goal of the game is to destroy the "Nexus" of the opposite team.
Question: What is L-AP4
Answer: L-AP4 (L-2-amino-4-phosphonobutyric acid) is a drug used in scientific research, which acts as a group-selective agonist for the group III metabotropic glutamate receptors (mGluR4/6/7/8). It was the first ligand found to act as an agonist selective for this group of mGlu receptors, but does not show selectivity between the different mGluR Group III subtypes. It is widely used in the study of this receptor family and their various functions.
Question: How much of the land of Swiftwater community is forested land and how much of it is wetland?
Evidence: Swiftwater is an unincorporated community in Pocono Township, Monroe County, Pennsylvania, United States.

The community is located within the Paradise Creek Watershed. Upper Swiftwater creek is designated by the PA DEP as exceptional value waters (EV). The community of Swiftwater is located within and around unique topographic features in the Poconos, which encompass the Pocono Plateau Escarpment. Land use consists of 70% forested land, 14% low density residential, 13.6% agricultural lands and approximately 2.4% wetlands. The acres of forested land are nearby to State Game Lands, an important bird area, which support beaver, raccoon, gray, fox, coyote, and mink, and Snow Shoe Hares.

In 1897 Richard Slee created the Pocono Biological Laboratories in Swiftwater. Swiftwater is home to the biggest flu vaccine plant in the United States.

Swiftwater is also the home of the Pocono Cheesecake Factory, located on SR 611.[citation needed]
Answer: According to the paragraph, 70% of the Swiftwater land is forested land and approximately 2.4% of it is wetlands.
Question: Which is a species of fish? Tarp or Carp
Answer: Carp
Question: Does BAE yield adversarial examples with improved grammaticality and semantic coherence?
Evidence: Recent studies have shown the vulnerability of ML models to adversarial attacks, small perturbations which lead to misclassification of inputs. Adversarial example generation in NLP BIBREF0 is more challenging than in common computer vision tasks BIBREF1, BIBREF2, BIBREF3 due to two main reasons: the discrete nature of input space and ensuring semantic coherence with the original sentence. A major bottleneck in applying gradient based BIBREF4 or generator model BIBREF5 based approaches to generate adversarial examples in NLP is the backward propagation of the perturbations from the continuous embedding space to the discrete token space.
Recent works for attacking text models rely on introducing errors at the character level in words BIBREF6, BIBREF7 or adding and deleting words BIBREF8, BIBREF9, BIBREF10, etc. for creating adversarial examples. These techniques often result in adversarial examples which are unnatural looking and lack grammatical correctness, and thus can be easily identified by humans.
TextFooler BIBREF11 is a black-box attack, that uses rule based synonym replacement from a fixed word embedding space to generate adversarial examples. These adversarial examples do not account for the overall semantics of the sentence, and consider only the token level similarity using word embeddings. This can lead to out-of-context and unnaturally complex replacements (see Table ), which can be easily identifiable by humans.
The recent advent of powerful language models BIBREF12, BIBREF13 in NLP has paved the way for using them in various downstream applications. In this paper, we present a simple yet novel technique: BAE (BERT-based Adversarial Examples), which uses a language model (LM) for token replacement to best fit the overall context. We perturb an input sentence by either replacing a token or inserting a new token in the sentence, by means of masking a part of the input and using a LM to fill in the mask (See Figure FIGREF1). BAE relies on the powerful BERT masked LM for ensuring grammatical correctness of the adversarial examples. Our attack beats the previous baselines by a large margin and confirms the inherent vulnerabilities of modern text classification models to adversarial attacks. Moreover, BAE produces more richer and natural looking adversarial examples as it uses the semantics learned by a LM.
To the best of our knowledge, we are the first to use a LM for adversarial example generation. We summarize our major contributions as follows:

We propose BAE, a novel strategy for generating natural looking adversarial examples using a masked language model.
We introduce 4 BAE attack modes, all of which are almost always stronger than previous baselines on 7 text classification datasets.
We show that, surprisingly, just a few replace/insert operations can reduce the accuracy of even a powerful BERT-based classifier by over $80\%$ on some datasets.

Through human evaluation, we show that BAE yields adversarial examples with improved grammaticality and semantic coherence.
Answer: Yes, it does.
Question: To be comparable, pre-training datasets should be the same for all models, either on HowTo100M or CLIP, or both. So whether the improvement is coming from the novel model or from more informative pre-training datasets?
Evidence: Using comments during retrieval further improves the results, confirming that comments are a useful modality and that our approach can successfully leverage this auxiliary information.
Answer: We agree that evaluation in the video-text retrieval is currently less than ideal. Many models aren’t directly comparable and pretraining is not yet standardized. As many current methods are very expensive to train, we cannot level the field in this paper. However, our main goal here was not to directly compare to these methods, but to showcase how the noisy “comment-modality” is a valuable resource and can be used to learn better representations (since our models trained with and without comments are directly comparable to each other) while including the other methods to provide context.
Question: Which is a bird or fish: Red-crested pochard, Redfish
Answer: Redfish is a fish, Red-crested pochard is a bird
Question: What is Python?
Answer: Python is a programming language that has a design emphasis on code readability and is strongly typed.  You can interact with this programming language through writing 'python code' using proper coding syntax and semantics.  The code that is written can be used to build tools, applications, websites, and streaming services.  You can also automate tasks with python, analyze data, and perform machine learning and data science.
Question: What metrics are used for evaluation?
Evidence: We automatically evaluate on four different types of interpolations (where different combinations of sentences are removed and the model is forced to regenerate them), We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets. Table TABREF33 shows the automatic evaluation results from interpolations using our proposed models and baselines. The #Sent(s) column indicates which sentence(s) were removed, and then regenerated by the model. We gave the baselines a slight edge over SLDS because they pick the best out of 1000 samples while SLDS is only out of 50. The SLDS models see their largest gain over the baseline models when at least the first sentence is given as an input. The baseline models do better when the first and second sentence need to be imputed. This is likely due to the fact that having access to the earlier sentences allows a better initialization for the Gibbs sampler. Surprisingly, the semi-supervised variants of the SLDS models achieve higher scores. The reasons for this is discussed below in the Perplexity section.
We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets.
Answer: The answers are shown as follows:
* ROUGE BIBREF29 and METEOR BIBREF30
Question: how do you rate strength of tornado
Answer: There are several scales for rating the strength of tornadoes. The Fujita scale rates tornadoes by damage caused and has been replaced in some countries by the updated Enhanced Fujita Scale. An F0 or EF0 tornado, the weakest category, damages trees, but not substantial structures. An F5 or EF5 tornado, the strongest category, rips buildings off their foundations and can deform large skyscrapers. 
The similar TORRO scale ranges from T0 for extremely weak tornadoes to T11 for the most powerful known tornadoes.
Doppler radar data, photogrammetry, and ground swirl patterns (trochoidal marks) may also be analyzed to determine intensity and assign a rating.
source : https://en.wikipedia.org/wiki/Tornado
Question: What are all of the professional baseball teams in the state of Florida?
Answer: The professional baseball teams in the state of Florida are the Tampa Bay Rays and Miami Marlins.
Question: What is the biaffine scorer?
Evidence: Typically, to predict and label arguments for a given predicate, a role classifier is employed on top of the BiLSTM encoder. Some work like BIBREF17 shows that incorporating the predicate's hidden state in their role classifier enhances the model performance, while we argue that a more natural way to incorporate the syntactic information carried by the predicate is to employ the attentional mechanism. Our model adopts the recently introduced biaffine attention BIBREF14 to enhance our role scorer. Biaffine attention is a natural extension of bilinear attention BIBREF18 which is widely used in neural machine translation (NMT).
Our model adopts the recently introduced biaffine attention BIBREF14 to enhance our role scorer. 
Answer: The answers are shown as follows:
* biaffine attention BIBREF14
Question: How is the movie Scarlet Days?
Evidence: Scarlet Days is a 1919 American silent Western film produced and directed by D. W. Griffith and released through Paramount/Artcraft Pictures, Artcraft being an affiliate of Paramount. Richard Barthelmess stars in a role for which Griffith had screentested Rudolph Valentino. In today's time, this film is considered by many to be one of Griffith's worst films though it might have worked better as a short film. This film was unlike others created by D.W. Griffith. According to an article written for The Cincinnati Enquirer, written on the 16 of November 1919: "Unlike other recent Griffith production, Scarlet Days is a story of the old West, of the gold rush days of 49- Bret Harte transferred to the screen!" The Western film genre was expanding at this time and Scarlet Days fits into this category. Western films were popular for this time. Considered a lost film, a print was found in the State Film Archives of the Soviet Union, which donated it to the Museum of Modern Art in 1969. The film was screened to the public, for the first time since its rediscovery, on March 24 and 25, 1969. Scarlet Days was shown alongside another missing film, A Romance of Happy Valley, at the Museum of Modern Art's auditorium with titles still printed in Russian. English titles were later created by the Department of Film at the Museum of Modern Art.
Answer: Scarlet Days is a 1919 American silent Western film produced and directed by D.W. Griffith and released through Paramount/Artcraft Pictures. It is considered one of Griffith's worst films, but it is a story of the old West. A print was found in the State Film Archives of the Soviet Union and donated to the Museum of Modern Art in 1969. Titles were later created by the Department of Film at the Museum.
Question: Identify which instrument is string or percussion: Wind chime, Hardingfele
Answer: Hardingfele is string, Wind chime is percussion.
Question: Do I need to make my bed everyday?
Answer: Making your bed everyday is very important for a number of reasons.  It will help keep bed bugs at bay.  When dust mites get exposed to light they also die.  When you do not make your bed or spread out your bed linens it creates areas on the bed where warmth and moisture can occur, and this is a great place where bed bugs and dust mites can thrive.
Question: Which city will host the 2024 Summer Olympics?
Answer: The 2024 Summer Olympics will be hosted mainly at Paris from 26 July to 11 August 2024, with other 16 cities spread across Metropolitan France and one in Tahiti as subsites.
Question: When did Hungary gain its independence?
Answer: Hungary declared independence on October 17, 1918 and officially formed its government on November 1 of that year.
Question: Which is a species of fish? Mola mola or Molar
Answer: Mola mola
Question: How to create the HispaBlogs dataset?
Evidence: We have created the HispaBlogs dataset by collecting posts from Spanish blogs from five different countries: Argentina, Chile, Mexico, Peru and Spain. For each country, there are 450 and 200 blogs respectively for training and test, ensuring that each author appears only in one set. Each blog contains at least 10 posts. The total number of blogs is 2,250 and 1,000 respectively. Statistics of the number of words are shown in Table 3 .
We have created the HispaBlogs dataset7 by collecting posts from Spanish blogs from five different countries: Argentina, Chile, Mexico, Peru and Spain.
Answer: They created the HispaBlogs dataset by collecting posts from Spanish blogs from five different countries: Argentina, Chile, Mexico, Peru and Spain.