DataHammer/scimrc huggingface dataset:
Question: What tool do they use to extract words?
Evidence: Our datasets are scientific articles that were published on arXiv. The sets contain articles (in LaTeX format) from four computer science domains: NLP, IR, AI, and ML. They were created by filtering arXiv articles based on their primary and secondary categories. We used the following categories for the four collections: cs.cl for NLP; cs.ir for IR; cs.ai for AI and stat.ml, stat.co, stat.me or cs.lg for ML.
Table TABREF22 shows the number of documents along with the number of unique words, equations and equation units for each collection. The equations are display equations that were enumerated in the LaTeX version of the articles. Unlike inline equations, which in many instances represent variables with general meaning (e.g. INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , etc.) and even numerical values, display equations typically represent mathematical concepts with more specific semantics. For the empirical study we used a random subset of 2k singletons from the total collection, along with all equations that occur more than once. For the qualitative analysis, we used all equations.
We extracted words by tokenizing articles using the NLTK package BIBREF12 and restricted the vocabulary to noun phrases and adjectives. The vocabulary was selected by:
removing common stopwords
treating the top 25 most frequent words as stop words and removing them
including words whose term frequency is greater than or equal to 10 and whose character length is greater than or equal to 4
including the top 50 most frequent abbreviations whose character length is 3 (an exception to our previous rule)
When tokenizing equations, we first create an effective vocabulary of equation units. We convert equations into SLT format and collect collection wide frequency statistics over the equation units. The vocabulary contains all equation units whose frequency count is greater than INLINEFORM0 .
We extracted words by tokenizing articles using the NLTK package (Bird et al., 2009) and restricted the vocabulary to noun phrases and adjectives. We extracted words by tokenizing articles using the NLTK package (Bird et al., 2009) and restricted the vocabulary to noun phrases and adjectives.
Answer: The NLTK package.
Question: What types of various community texts have been investigated for exploring global structure of binomials?
Evidence: The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names — a key setting for word ordering which has been difficult to study by other means.
We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time
Answer: The answers are shown as follows:
* news publications, wine reviews, and Reddit
Question: What magazine was Alex Prager the director of Photography for?
Evidence: In 2011 Kathy Ryan, director of photography for The New York Times Magazine commissioned Prager to shoot 12, 1 minute films with some film actors from that year, inspired by "cinematic villainy". Prager won a News and Documentary Emmy Award for New Approaches to News & Documentary Programming: Arts, Lifestyle & Culture for her Touch of Evil short films.
Answer: Alex Prager was the director of photography for the New York Times Magazine.
Question: Do dogs have a third eye?
Answer: No, dogs only have two eyes. But some say they may perceive more of the world than humans do, giving them an extra sensory-like trait.
Question: Give me a bulleted list of the last 10 UK Prime Ministers
Answer: - Rishi Sunak
- Liz Truss
- Boris Johnson
- Theresa May
- David Cameron
- Gordon Brown
- Tony Blair
- John Major
- Margaret Thatcher
- James Callaghan
Question: Given a reference about the creation of the UFC, tell me the member of the Gracie family  who co-created the UFC and the city they hosted the first UFC event.
Evidence: The UFC was founded by businessman Art Davie and Brazilian martial artist Rorion Gracie, and the first event was held in 1993 at the McNichols Sports Arena in Denver, Colorado. The purpose of the early Ultimate Fighting Championship competitions was to identify the most effective martial art in a contest with minimal rules and no weight classes between competitors of different fighting disciplines.
Answer: Rorion Gracie is the co-creator. The first event was held in Denver. Colorado.
Question: Why is it hard to have a baby and keep a job?
Answer: It's hard to have a baby and keep a job because babies need a lot of time and attention. One solution is to find a good caregiver, whether it is a nanny or a daycare or some other form of group care. While your baby is with that care, you can do your work for your job. Beware that babies who have care as part of larger groups may get sick very often, so your schedule needs to be flexible to accommodate for sick days (yours or your baby's).
Question: What is an adverb?
Answer: An adverb is a word or a combination of words that qualified an adjective (e.g. a describing word).  Adverbs can also restrict and limit the meaning of verbs.  An example of how to use an adverb in a sentence is the following: "the roads are very steep and dangerous."  In this example 'very' is the adverb.  Another example is "they are a happily married couple."  Happily is the adverb in this second example.
Question: Which Alanis Morissette song contains the phrase "jagged little pill" (the title of her debut album)?
Answer: "You learn".
Question: I have a list of Herbs and Spices. Please divide this list into those categories: basil, bay leaf, chives, cardamon, cinnamon, fennel, cloves, lemon grass, nutmeg, pepper, oregano, turmeric, parsley, ginger, saffron, vanilla rosemary, cumin, sage, and thyme.
Answer: Herbs: basil, bay leaf, chives, fennel, lemon grass, oregano, parsley, rosemary, sage, thyme. Spices: cardamom, cinnamon, cloves, nutmeg, pepper, turmeric, ginger, saffron, vanilla, cumin.
Question: From the given text, Provide some info regarding consistory
Evidence: In the Roman Catholic Church a consistory is a formal meeting of the College of Cardinals called by the pope. There are two kinds of consistories, extraordinary and ordinary. An "extraordinary" consistory is held to allow the pope to consult with the entire membership of the College of Cardinals. An "ordinary" consistory is ceremonial in nature and attended by cardinals resident in Rome. For example, the pope elevates new cardinals to the College at a consistory; Pope Francis has called consistories for ceremonies of canonization.
Answer: 1. A consistory is a formal gathering of the College of Cardinals in the Roman Catholic Church that is summoned by the pope.
2. Consistories come in two varieties: remarkable and ordinary.
3. The pope can consult with the full College of Cardinals by calling a "extraordinary" consistory.
4. Cardinals who live in Rome attend "ordinary" consistories, which are ceremonial in nature.
5. Pope Francis has convened consistories for canonization ceremonies, for instance, where he elevates new cardinals to the College of Cardinals.
Question: What years did the Patriots win a Super Bowl?
Answer: The Patriots have won six Super Bowls, in the years, 2001, 2003, 2004, 2014, 2016, and 2018.
Question: Classify each of the following as root or shoot vegetables: tomato, potato, carrot, brinjal, onion, lady finger, cucumber, beets, sweet potato
Answer: tomato, brinjal, lady finger, cucumber are shoot vegetables and potato, carrot, onion, beets, sweet potato are root vegetables.
Question: Where is Cabomba sometimes used for decoration but also utility?
Evidence: Cabomba is an aquatic plant genus, one of two belonging to the family Cabombaceae. It has divided submerged leaves in the shape of a fan (hence the vernacular name fanwort) and is much favoured by aquarists as an ornamental and oxygenating plant for fish tanks. Use in the aquarium trade has led to some species being introduced to other parts of the world, such as Australia, where they have become weeds.
Answer: fish tanks
Question: When was the first Reading railway station opened?
Evidence: Reading railway station is a major transport hub in Reading, Berkshire, England. It is on the northern edge of the town centre, near the main retail and commercial areas and the River Thames, 36 miles (58 km) from London Paddington. The first Reading station was opened on 30 March 1840 as the temporary western terminus of the original line of the Great Western Railway (GWR). Reading is the ninth-busiest station in the UK outside London and the second busiest interchange station outside London with over 3.8 million passengers changing trains at the station annually.
Answer: The first Reading railway station was opened on the 30th of March, 1840.
Question: Who is the best ever President of the United States ?
Answer: There are many factors that should be considered when determining the best President of the United States.  Many would say there is no "right answer" on this question, since people hold different values on what is or isn't important.

But, generally speaking, Presidents who have successfully led the United States through times of great national or global crises tend to receive the highest marks.  The short list of Presidents typically are the following: George Washington, Abraham Lincoln, Franklin Delano Roosevelt (FDR) and Ronald Reagan.  

Washington was key to winning the independence of the country from the British as the wartime General during the Revolutionary War, and was the first President during a very tumultuous first 8 years of the United States.

Lincoln kept the country together through the American Civil War, and eradicated slavery - a great moral achievement in finally realizing the principles of the Declaration of Independence for all people no matter the color of their skin.

FDR led the country through the Great Depression, and also was a world leader in fighting German and Japanese aggression during World War II.

Ronald Reagan managed the end of the Cold War, without firing a shot, and avoiding nuclear catastrophe, through shrewd maneuvering and negotiation with Mikhail Gorbachev.
Question: By how much do they outperform existing state-of-the-art VQA models?
Evidence: Table TABREF10 reports our main results. Our models are built on top of prior works with the additional Attention Supervision Module as described in Section SECREF3 . Specifically, we denote by Attn-* our adaptation of the respective model by including our Attention Supervision Module. We highlight that MCB model is the winner of VQA challenge 2016 and MFH model is the best single model in VQA challenge 2017. In Table TABREF10 , we can observe that our proposed model achieves a significantly boost on rank-correlation with respect to human attention. Furthermore, our model outperforms alternative state-of-art techniques in terms of accuracy in answer prediction. Specifically, the rank-correlation for MFH model increases by 36.4% when is evaluated in VQA-HAT dataset and 7.7% when is evaluated in VQA-X. This indicates that our proposed methods enable VQA models to provide more meaningful and interpretable results by generating more accurate visual grounding.
Table TABREF10 reports our main results. Our models are built on top of prior works with the additional Attention Supervision Module as described in Section SECREF3 . Specifically, we denote by Attn-* our adaptation of the respective model by including our Attention Supervision Module. We highlight that MCB model is the winner of VQA challenge 2016 and MFH model is the best single model in VQA challenge 2017. In Table TABREF10 , we can observe that our proposed model achieves a significantly boost on rank-correlation with respect to human attention. Furthermore, our model outperforms alternative state-of-art techniques in terms of accuracy in answer prediction. Specifically, the rank-correlation for MFH model increases by 36.4% when is evaluated in VQA-HAT dataset and 7.7% when is evaluated in VQA-X. This indicates that our proposed methods enable VQA models to provide more meaningful and interpretable results by generating more accurate visual grounding.
Answer: The answers are shown as follows:
* the rank-correlation for MFH model increases by 36.4% when is evaluated in VQA-HAT dataset and 7.7% when is evaluated in VQA-X
Question: Which language models do they compare against?
Evidence: We compare against the following document representation baselines: bag-of-words (BoW); Denoising Autoencoders (DEA) BIBREF14 , a representation learned from reconstructing original document INLINEFORM0 using corrupted one INLINEFORM1 . SDAs have been shown to be the state-of-the-art for sentiment analysis tasks BIBREF29 . We used Kullback-Liebler divergence as the reconstruction error and an affine encoder. To scale up the algorithm to large vocabulary, we only take into account the non-zero elements of INLINEFORM2 in the reconstruction error and employed negative sampling for the remainings; Word2Vec BIBREF1 +IDF, a representation generated through weighted average of word vectors learned using Word2Vec; Doc2Vec BIBREF2 ; Skip-thought Vectors BIBREF16 , a generic, distributed sentence encoder that extends the Word2Vec skip-gram model to sentence level. It has been shown to produce highly generic sentence representations that apply to various natural language processing tasks. We also include RNNLM BIBREF11 , a recurrent neural network based language model in the comparison. In the semantic relatedness task, we further compare to LSTM-based methods BIBREF18 that have been reported on this dataset.
We also include RNNLM BIBREF11 , a recurrent neural network based language model in the comparison.
Answer: The answers are shown as follows:
* RNNLM BIBREF11
Question: What different classes of features are considered by authors?
Evidence: In this paper, we have presented high level overview of a feature specification language for ML based TA applications and an approach to enable reuse of feature specifications across semantically related applications. Currently, there is no generic method or approach, which can be applied during TA applications' design process to define and extract features for any arbitrary application in an automated or semi-automated manner primarily because there is no standard way to specify wide range of features which can be extracted and used. We considered different classes of features including linguistic, semantic, and statistical for various levels of analysis including words, phrases, sentences, paragraphs, documents, and corpus. As a next step, we presented an approach for building a recommendation system for enabling automated reuse of features for new application scenarios which improves its underlying similarity model based upon user feedback.
To take this work forward, it is essential to have it integrated to a ML platform, which is being used by large user base for building TA applications so that to be able to populate a repository of statistically significant number of TA applications with details as specified in Section SECREF5 and thereafter refine the proposed approach so that eventually it rightly enables reuse of features across related applications.
We considered different classes of features including linguistic, semantic, and statistical for various levels of analysis including words, phrases, sentences, paragraphs, documents, and corpus.
Answer: The authors considered different classes of features including linguistic, semantic, and statistical for various levels of analysis including words, phrases, sentences, paragraphs, documents, and corpus.
Question: What are some of the best things to do in Fort Collins, Colorado?
Answer: Fort Collins has many fun things to do with its proximity to the foothills of the Rocky Mountains, a vibrant downtown area, and the many world class breweries in and around town. Outdoors highlights include hikes in Lorry State Park, Horsetooth Reservoir, and a climb to Horsetooth Rock that overlooks the city. Be sure to take a walk out Old Town, where you can grab a coffee at Little Bird and shop at the many boutiques. Lastly, be sure to check out New Belgium or Odell Brewing, which are just two of the more than twenty breweries in Fort Collins.
Question: Name different kinds of cheese
Answer: mozzarella, cheddar, gorgonzola, swiss, bleu, provolone, gouda, pepper jack, parmesan, cottage, asiago, brie, devil's gulch, ricotta, Gruyère, stilton, feta, & havarti
Question: Do the authors give any examples of major events which draw the public's attention and the impact they have on stock price?
Evidence: Figure FIGREF6 shows the relationship between Tesla stock return and stock sentiment score. According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period. The price has been increased significantly during the testing period, which reflected the positive sentiment. The predicting power of sentiment score is more significant when the sentiment is more extreme and less so when the sentiment is neutral.
According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period.
Answer: Yes.
Question: what standard speech transcription pipeline was used?
Evidence: The goal of reaching “human parity” in automatic CTS transcription raises the question of what should be considered human accuracy on this task. We operationalized the question by submitting the chosen test data to the same vendor-based transcription pipeline that is used at Microsoft for production data (for model training and internal evaluation purposes), and then comparing the results to ASR system output under the NIST scoring protocol. Using this methodology, and incorporating state-of-the-art convolutional and recurrent network architectures for both acoustic modeling BIBREF9 , BIBREF10 , BIBREF7 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 and language modeling BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 with extensive use of model combination, we obtained a machine error rate that was very slightly below that of the human transcription process (5.8% versus 5.9% on Switchboard data, and 11.0% versus 11.3% on CallHome English data) BIBREF19 . Since then, Saon et al. have reported even better results, along with a separate transcription experiment that puts the human error rate, on the same test data, at a lower point than measured by us (5.1% for Switchboard, 6.8% for CallHome) BIBREF20 .
We operationalized the question by submitting the chosen test data to the same vendor-based transcription pipeline that is used at Microsoft for production data (for model training and internal evaluation purposes), and then comparing the results to ASR system output under the NIST scoring protocol. 
Answer: The answers are shown as follows:
* pipeline that is used at Microsoft for production data
Question: What was the first British instrumental to top the USA charts
Answer: Telstar by The Tornados
Question: Where does the word plumbing originate from?
Answer: "Plumbing" originates from the original name for Lead (Pb), Plumbum, which is what most pipes were made from at the time.
Question: Do aliens exist?
Answer: Although there is no definitive evidence for the existence of extraterrestrial life, the immense number of planets that most likely exist in the universe suggest that, although the development of life can be difficult to start, there should be other life forms in the universe.  

The discrepancy between the expected existence of life in the universe and the lack of any evidence is called the Fermi-Paradox.
Question: Give me a list of animals you might raise for milk
Answer: Sheep, Goats, Cows
Question: Who was Loku Nilame Adikaram Ratwatte
Answer: Loku Nilame Adikaram Ratwatte ( - 1827) (known as Ratwatte Nilame) was a courtier of the Kingdom of Kandy. He was the 2nd Adigar (Adigaram) from 1825 to 1827 during the British rule. He was one of the signatories of the Kandyan Convention which made the Kandyan Kingdom part of the British Empire.
Question: Ramon Magsaysay Award 2022 was given to 4 persons
Answer: Sotheara Chhim(Cambodia), Bernadette Madrid(Philippines), Tadashi Hattori(Japan) & Gary Benchehib (Indonesia)
Question: Is it instance-wise vs domain-wise validation?
Evidence: Having demonstrated control of generalisation with linear model regularisation (Sec. 4.1), and explained DomainBed performance post-hoc via measuring complexity of neural models (Sec. 4.2), we next evaluate algorithms for practical complexity tuning. From Fig. we saw that within-domain and cross-domain evaluation have different optimal tuning. If we consider automating the search for a good regularisation parameter, these two final evaluation criteria correspond respectively to hyperparameter selection based on a validation set from the seen source domains, vs based on a validation set drawn from a held out source domain. The latter validation criterion corresponds to a unbiased estimator of expected cross-domain generalisation performance R E (f ), which our theorem bounds.
Setup We performed DG evaluation on DomainBed using ERM with linear models using the same DINO features as Section 4.1. For each held out domain, we performed hyperparameter tuning with either instance-wise cross-validation (where validation folds are drawn from the same source domains used for training) or domain-wise cross-validation (where validation folds are drawn from held-out domains not seen during training).
The results in Table report the average accuracy across held out domains, and the average selected log C regularisation strength (See Supplementary for the full breakdown of results for each held-out domain). We can see that the domain-wise cross-validation objective leads to similar or better accuracy, and similar or smaller C value selection (i.e., stronger regularisation). This outcome is opposite to the observation made by. Given the theoretical support for our domain-wise objective, and our clear empirical outcome when freed of confounding factors such as stochasticity in neural network training, we consider our result to be decisive and attribute the different observation to the inaccuracy and stochasticity of hyperparameter tuning in neural networks.
Table: Comparing model selection criteria using LinearSVC. Accuracy and selected SVM regularisation parameter 'C'. Domain-wise validation outperforms instance-wise and selects stronger regularisation (lower C).
For each held out domain, we performed hyperparameter tuning with either instance-wise cross-validation (where validation folds are drawn from the same source domains used for training) or domain-wise cross-validation (where validation folds are drawn from held-out domains not seen during training).
Answer: Yes, this corresponds to using in-domain validation data vs. cross-domain data. We have revised the paper to explain this more clearly.