DataHammer/scimrc huggingface dataset:
Question: What template does the existing research fit?
Evidence: Cyberbullying is recognized as a phenomenon at least since 2003 BIBREF5 . Use of social media exploded with launching of multiple platforms such as Wikipedia (2001), MySpace (2003), Orkut (2004), Facebook (2004), and Twitter (2005). By 2006, researchers had pointed that cyberbullying was as serious phenomenon as offline bullying BIBREF6 . However, automatic detection of cyberbullying was addressed only since 2009 BIBREF7 . As a research topic, cyberbullying detection is a text classification problem. Most of the existing works fit in the following template: get training dataset from single SMP, engineer variety of features with certain style of cyberbullying as the target, apply a few traditional machine learning methods, and evaluate success in terms of measures such as F1 score and accuracy. These works heavily rely on handcrafted features such as use of swear words. These methods tend to have low precision for cyberbullying detection as handcrafted features are not robust against variations in bullying style across SMPs and bullying topics. Only recently, deep learning has been applied for cyberbullying detection BIBREF8 . Table TABREF27 summarizes important related work.
Most of the existing works fit in the following template: get training dataset from single SMP, engineer variety of features with certain style of cyberbullying as the target, apply a few traditional machine learning methods, and evaluate success in terms of measures such as F1 score and accuracy.
Answer: Get training dataset from single SMP, engineer variety of features with certain style of cyberbullying as the target, apply a few traditional machine learning methods, and evaluate success in terms of measures such as F1 score and accuracy.
Question: What is the accuracy of the proposed technique?
Evidence: FLOAT SELECTED: Table 2: TUPLEINF is significantly better at structured reasoning than TABLEILP.9
FLOAT SELECTED: Table 2: TUPLEINF is significantly better at structured reasoning than TABLEILP.9
Answer: 51.7 and 51.6 on 4th and 8th grade question sets with no curated knowledge. 47.5 and 48.0 on 4th and 8th grade question sets when both solvers are given the same knowledge
Question: Where is the data they use from?
Evidence: The data for the project was acquired from Airdna, a data processing service that collaborates with Airbnb to produce high-accuracy data summaries for listings in geographic regions of the United States. For the sake of simplicity, we focus our analysis on Airbnb listings from Manhattan, NY, during the time period of January 1, 2016, to January 1, 2017. The data provided to us contained information for roughly 40,000 Manhattan listings that were posted on Airbnb during this defined time period. For each listing, we were given information of the amenities of the listing (number of bathrooms, number of bedrooms …), the listing’s zip code, the host’s description of the listing, the price of the listing, and the occupancy rate of the listing. Airbnb defines a home's occupancy rate, as the percentage of time that a listing is occupied over the time period that the listing is available. This gives us a reasonable metric for defining popular versus less popular listings.
The data for the project was acquired from Airdna, a data processing service that collaborates with Airbnb to produce high-accuracy data summaries for listings in geographic regions of the United States.
Answer: Airdna.
Question: Identify the bird from the list: Chough, Cough, Clough
Answer: Chough
Question: Give me a list of different berries that I could put in a smoothie
Answer: Blackberries, strawberries, blueberries, and raspberries would all be good in a smoothie
Question: What is the dataset created by the authors in this paper?
Evidence: We have created a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes believed to be associated with risk of recurrent Intensive Care Unit (ICU) readmission per our domain experts (co-authors LAC, PAT, DAG) as well as the literature. BIBREF10 BIBREF11 BIBREF12
Each entry in this database of consists of a Subject Identifier (integer), a Hospital Admission Identifier (integer), Category (string), Text (string), 15 Phenotypes (binary) including “None” and “Unsure”, Batch Date (string), and Operators (string). These variables are sufficient to use the data set alone, or to join it to the MIMIC-III database by Subject Identifier or Hospital Admission Identifier for additional patient-level or admission-level data, respectively. The MIMIC database BIBREF8 was utilized to extract Subject Identifiers, Hospital Admission Identifiers, and Note Text.
Annotated discharge summaries had a median token count of 1417.50 (Q1-Q3: 1046.75 - 1926.00) with a vocabulary of 26454 unique tokens, while nursing notes had a median count of 208 (Q1-Q3: 120 - 312) with a vocabulary of 12865 unique tokens.
Table defines each of the considered clinical patient phenotypes. Table counts the occurrences of these phenotypes across patient notes and Figure contains the corresponding correlation matrix. Lastly, Table presents an overview of some descriptive statistics on the patient notes' lengths.
We have created a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes believed to be associated with risk of recurrent Intensive Care Unit (ICU) readmission per our domain experts (co-authors LAC, PAT, DAG) as well as the literature.
Answer: It is a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes.
Question: Are the tweets location-specific?
Evidence: FLOAT SELECTED: Figure 1: The location of geotagged tweets
FLOAT SELECTED: Figure 1: The location of geotagged tweets
Answer: Yes.
Question: I want to buy a work monitor which is to be used along with the laptop. What features of monitor should be considered?
Answer: Based on type of work 
For coding or text content creation 
 - size 24-28 inch monitor would suffice
 - resolution 1920 * 1080 or lower 
 - can be swirled to landscape or portrait mode
 - refresh rate of 60-90 hz
For video or game content creation 
 - size 28 or higher curved monitors
 - resolution QHD, UHD, 4K
 - refresh rate 120 hz
 - preferable having speakers
Question: What is Ancient Girl's Frame
Answer: Ancient Girl's Frame (Japanese: 闘神機ジーズフレーム, Hepburn: Tōshinki Jīzu Furēmu) is a Japanese-Chinese original net animation series which aired in Chinese on streaming platforms from October 11 to December 27, 2021, and on Japanese television on Tokyo MX the following day. The series is licensed in North America by Funimation.
Question: Which characters belong to DC or Marvel Universe? Silver Surfer, Martian Manhunter
Answer: Martin Manhunter is DC, Silver Surfer is Marvel
Question: Can you at least present results with more standard MNIST architectures, like conv+pool layers and/or 3-4+ layers?
Evidence: Finally, we performed preliminary tests on two more difficult datasets, namely Fashion-MNIST (Xiao et al., 2017), which contains grey-scale images of clothing products, and CIFAR-10 (Krizhevsky et al., 2009), which contains RGB images of animals and vehicles. We did not tune the Hebbian networks’ hyper-parameters extensively, so accuracies on these tasks are not definitive but do give a good indication. On F-MNIST, the SoftHebb model achieved a top accuracy of 87.46% whereas a hard WTA reached a similar accuracy of 87.49%. A supervised MLP of the same size achieved a test accuracy of 90.55%. SoftHebb’s generative interpolations (Fig. 3B) are reconfirmed on the F-MNIST dataset, as is its robustness to attacks, whereas, with very small adversarial perturbations, the MLP drops to an accuracy lower than the SoftHebb model (dashed lines in Fig. 2). On CIFAR-10’s preliminary results, the hard WTA and SoftHebb achieved an accuracy of 49.78% and 50.27% respectively. In every tested dataset, it became clear that SoftHebb learns faster than either backpropagation or a hard WTA, by observing the loss and the learned features as in Fig. 1 C & D. The fully-connected SoftHebb layer is applicable on MNIST because the data classes are well-clustered directly in the feature-space of pixels. That is, SoftHebb’s probabilistic model’s assumptions (Def. 2.1) are quite valid for this feature space, and increasing the number of neurons for discovering more refined sub-clusters does help. However, for more complex datasets, this approach alone has diminishing returns and multilayer networks will be needed. Towards this, we implemented a first version of a convolutional SoftHebb, with an added supervised classifier. In these early results, conv-SoftHebb achieved 98.63% on MNIST, and 60.30% on CIFAR-10. This confirms that conv-SoftHebb is functional, but we did not systematically optimize its accuracy or explore its other strengths. This feasibility is evidence that SoftHebb’s strengths may soon also apply to larger networks and datasets.
Answer: We have now indeed performed additional experiments, testing the algorithm on larger problems and implementing more complex architectures.
Question: Identify which car manufacturer is German or American: Canoo, Pegasus
Answer: Pegasus is German, Canoo is American
Question: Can you give me a bulleted list of notable physical features of the beaver?
Evidence: Beavers (genus Castor) are large, semiaquatic rodents of the Northern Hemisphere. There are two extant species: the North American beaver (Castor canadensis) and the Eurasian beaver (C. fiber). Beavers are the second-largest living rodents, after capybaras. Beavers have stout bodies with large heads, long chisel-like incisors, brown or gray fur, hand-like front feet, webbed back feet, and tails that are flat and scaly. The two species differ in skull and tail shape and fur color. Beavers can be found in a number of freshwater habitats, such as rivers, streams, lakes and ponds. They are herbivorous, consuming tree bark, aquatic plants, grasses and sedges.
Answer: Beaver Physical Features: 
- stout bodies
- large heads
- long chisel-like incisors
- brown or gray fur
- hand-like front feet
- webbed back feet
- flat and scaly tails
Question: Will any country learn from their own mistakes and not repeat history?
Answer: After the World War I, we can imagine countries around the globe tell themselves - this will not repeat itself and we have to do everything we can do prevent it.  World War II took place just about a decade later.  The devastation caused by World War II scarred so many people that each country is doing its best to prevent the next World War.  But here we are - in the middle of another World War.  

As long as the desire for one person wishing to domination over another, we will always think of ways to build power and subjugate ones that are seemingly less than. them.
Question: What did the authors use to validate all experiments?
Evidence: For all of our experiments, we used 5-fold cross validation with identical folds for all experiments. Table TABREF27 reports on the results of using lexical features, static pre-trained embeddings with an SVM classifier, embeddings trained on our data with fastText classifier, and BERT over a dense layer with softmax activation. As the results show, using Mazajak/SVM yielded the best results overall with large improvements in precision over using BERT. We suspect that the Mazajak/SVM combination performed better than the BERT setup due to the fact that the Mazajak embeddings, though static, were trained on in-domain data, as opposed to BERT. Perhaps if BERT embeddings were trained on tweets, they might have outperformed all other setups. For completeness, we compared 7 other classifiers with SVM using Mazajak embeddings. As results in Table TABREF28 show, using SVM yielded the best results.
For all of our experiments, we used 5-fold cross validation with identical folds for all experiments.
Answer: For all experiments, the authors used 5-fold cross validation with identical folds.
Question: List the names of several laundry detergent brands
Answer: Tide, Oxy, Kirkland, Persil, Gain, Downy, 7th Generation, and Arm & Hammer
Question: How to survive the zombie apocalypse ?
Answer: -Picking the right group of collaborative survivors is key to success 
-Have an open mind, learn to work better with strangers   
-Learn a new survival skill - Sewing , cooking , fishing, hunting , farming 
-Have a well stocked survival bag that has food and water for at least a week
-Learn how to use a firearm 
-Take a self defense class 
-Mark out several safe areas you can seek shelter in 
-Learn basic DIY skills, plumbing, electrical maintenance and carpentry
-Pick the right shoes, comfortable for daily usage but also provide protection from nails, rocks and other dangerous objects
Question: Where is the Stade Louis-ll located?
Evidence: The Stade Louis-II, also known as Louis II (French pronunciation: ​[stad(ə) lwi dø]) is a stadium located in the Fontvieille district of Monaco. It serves primarily as a venue for football, being the home of AS Monaco and the Monaco national football team. The stadium is most notable for its distinctive nine arches at the away end of the ground. The arena is also used for the Herculis, a track and field meet of the Diamond League. The stadium hosted the 1986 and 1998–2012 UEFA Super Cup matches. Due to Monaco's small size, the stadium is the only football and athletics stadium in the country.
Answer: The Stade Louis-ll is located in the Fontvielle district of Monaco.
Question: What experiments do the authors present to validate their system?
Evidence: QnAMaker is not domain-specific and can be used for any type of data. To support this claim, we measure our system's performance for datasets across various domains. The evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs (binary labels). Each query-QA pair is judged by two judges. We filter out data for which judges do not agree on the label. Chit-chat in itself can be considered as a domain. Thus, we evaluate performance on given KB both with and without chit-chat data (last two rows in Table TABREF19), as well as performance on just chit-chat data (2nd row in Table TABREF19). Hybrid of deep learning(CDSSM) and machine learning features give our ranking model low computation cost, high explainability and significant F1/AUC score. Based on QnAMaker usage, we observed these trends:
 To support this claim, we measure our system's performance for datasets across various domains. The evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs (binary labels). Each query-QA pair is judged by two judges. We filter out data for which judges do not agree on the label. Chit-chat in itself can be considered as a domain. Thus, we evaluate performance on given KB both with and without chit-chat data (last two rows in Table TABREF19), as well as performance on just chit-chat data (2nd row in Table TABREF19).
Answer: The answers are shown as follows:
*  we measure our system's performance for datasets across various domains
* evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs
Question: How many reviews in total (both generated and true) do they evaluate on Amazon Mechanical Turk?
Evidence: We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \emph{F-score of only 56\%}, with 53\% F-score for fake review detection and 59\% F-score for real review detection. The results are very close to \emph{random detection}, where precision, recall and F-score would each be 50\%. Results are recorded in Table~\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random.
We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews).
Answer: The answers are shown as follows:
* 1,006 fake reviews and 994 real reviews
Question: What kind of model do they build to expand abbreviations?
Evidence: We use word2vec BIBREF0 to train the word embeddings. The dimension of embeddings is empirically set to 100.
We use word2vec BIBREF0 to train the word embeddings.
Answer: The answers are shown as follows:
* word2vec BIBREF0
Question: Who established the Illinois Humane Society?
Evidence: John Clark Dore (March 22, 1822 – December 14, 1900) was an American educator and politician from New Hampshire. A graduate of Dartmouth College, Dore briefly taught in Boston, Massachusetts before he was recruited to come to Chicago, Illinois to serve as its first superintendent of school. Dore was also a successful merchant, serving as the vice president of the Chicago Board of Trade. He was elected to the Illinois Senate in 1868, where he helped to establish the Illinois Humane Society.

He assumed the presidency of the Commercial Insurance Company, then was named president of the Chicago Board of Underwriters in 1869. Following the Great Chicago Fire of 1871, he was named the president of the State Savings Institution, resigning in 1873. Dore also presided over the Newsboys' and Bootblacks' Home and donated a building to them in 1884.
Answer: John Clark Dore
Question: Identify which animal species is alive or extinct: Elasmosaurus, Blue Whale
Answer: Blue Whale is alive, Elasmosaurus is extinct.
Question: What are some of the differences between English and Mandarin Chinese languages?
Answer: There are quite few differences.
1. Chinese characters are hieroglyphic; English words are phonic.
2. Chinese is character based; English is word based.
3. Chinese verbs don't have tenses; English verbs do
4. Chinese characters have tones; English words don't
5. Chinese adverbs and adjectives are indistinguishable; English adverbs and adjectives are different words most of the time
6. Chinese questions are sentences with a special character added to the end; English question are sentences with a special word added to the front
Question: What are the words of House Penrose?
Answer: "Set Down Our Deeds"
Question: Tell me if these vegetables are or are not green: carrot, courgette, bell pepper, radish, spaghetti squash, onion.
Answer: carrot - not green
courgette - green
bell pepper - may be green
radish - not green
spaghetti squash - not green
onion - may be green
Question: Best way to buy a new or used car?
Answer: Before you visit a dealership, get financing from your bank so that you have leverage at the dealership and can compare interest rates. Do research online to identify cars you would like to see in person. The more you look online, the more you will understand pricing, incentives, and upgrades available. Line up a few options that you would like to see in person. Always test drive any car you are considering. Know before you go to the dealership how much you want to offer for the car you like. If the dealership does not accept the offer, be willing to walk out and onto the next car. Do not let the dealership force you into optional packages or services you do not want. They can not force you to buy the extras. You will get the best deal if you are patient and do your research to know when you find a good deal.
Question: Which Targaryen dragon is the biggest?
Answer: The largest Targaryen dragon was Balerion the Black Dread.
Question: What is cost-sensitivity incorporated for?
Evidence: The challenges of imbalanced classification—in which the proportion of elements in each class for a classification task significantly differ—and of the ability to generalise on dissimilar data have remained important problems in Natural Language Processing (NLP) and Machine Learning in general. Popular NLP tasks including sentiment analysis, propaganda detection, and event extraction from social media are all examples of imbalanced classification problems. In each case the number of elements in one of the classes (e.g. negative sentiment, propagandistic content, or specific events discussed on social media, respectively) is significantly lower than the number of elements in the other classes.
The recently introduced BERT language model for transfer learning BIBREF0 uses a deep bidirectional transformer architecture to produce pre-trained context-dependent embeddings. It has proven to be powerful in solving many NLP tasks and, as we find, also appears to handle imbalanced classification well, thus removing the need to use standard methods of data augmentation to mitigate this problem (see Section SECREF11 for related work and Section SECREF16 for analysis).
BERT is credited with the ability to adapt to many tasks and data with very little training BIBREF0. However, we show that BERT fails to perform well when the training and test data are significantly dissimilar, as is the case with several tasks that deal with social and news data. In these cases, the training data is necessarily a subset of past data, while the model is likely to be used on future data which deals with different topics. This work addresses this problem by incorporating cost-sensitivity (Section SECREF19) into BERT.
We test these methods by participating in the Shared Task on Fine-Grained Propaganda Detection for the 2nd Workshop on NLP for Internet Freedom, for which we achieve the second rank on sentence-level classification of propaganda, confirming the importance of cost-sensitivity when the training and test sets are dissimilar.
However, we show that BERT fails to perform well when the training and test data are significantly dissimilar, as is the case with several tasks that deal with social and news data. In these cases, the training data is necessarily a subset of past data, while the model is likely to be used on future data which deals with different topics. This work addresses this problem by incorporating cost-sensitivity (Section 4.2) into BERT.
Answer: To improve BERT performance when training and test data are significantly dissimilar, which is common in dealing with social and news data.