DataHammer/scimrc huggingface dataset:
Question: Give me a list of the best Romantic Comedy Movies
Answer: My Big Fat Greek Wedding, About Time, P.S. I Love You, Somethings Gotta Give, How To Lose A Guy in Ten Days, Never Been Kissed, Annie Hall, The Big Sick, My Best Friend's Wedding, Sleepless in Seattle, Notting Hill, 10 Things I Hate About You, Bridget Jones's Diary, Clueless, You've Got Mail, When Harry Met Sally
Question: Given this paragraph about the climate of Seville, Spain, what's the average temperature during the day, at night, and overall?
Evidence: Seville has a Mediterranean climate (Köppen climate classification Csa), featuring very hot, dry summers and mild winters with moderate rainfall. Seville has an annual average of 19.2 °C (67 °F). The annual average temperature is 25.4 °C (78 °F) during the day and 13.0 °C (55 °F) at night. Seville is located in the lower part of the Guadalquivir Valley, which is often referred to as "the frying pan of Spain", as it features the hottest cities in the country.

Seville is the warmest city in Continental Europe. It is also the hottest major metropolitan area in Europe, with summer average high temperatures of above 35 °C (95 °F) and also the hottest in Spain. After the city of Córdoba (also in Andalusia), Seville has the hottest summer in continental Europe among all cities with a population over 100,000 people, with average daily highs of 36.0 °C (97 °F) in July.
Answer: Seville's average temperature is 19.2 °C (67 °F).

The average temperature during the day is 25.4 °C (78 °F) and the average temperature at night is 13.0 °C (55 °F).
Question: What are some good yoga poses for beginners?
Answer: The practice of yoga can be a beneficial addition to a healthy lifestyle. Yoga is considered to increase flexibility and muscle strength. A few positions for beginners include :
1. Downward facing dog
2. Mountain pose
3. Warrior I
4. Warrior II
5. Triangle pose

These gentler positions provide a safe introduction to the practice of yoga for beginners
Question: List the major internet search engines
Answer: Bing
Google
Yahoo
DuckDuckGo
Ask.com
AOL
Question: Classify the celebrities based on whether they were born in 1990 or 1991.
Emma Watson, Jennifer Lawrence, Ed Sheeran, Charlie Puth
Answer: Born in 1990 - Emma Watson, Jennifer Lawrence
Born in 1991 - Ed Sheeran, Charlie Puth
Question: How to be a good software engineer?
Answer: A diversity of industry experience is required to be a good software engineer. Practice industry-standard principles of writing high-quality production-grade code, understand and debug a complicated production system, and effectively communicate with other engineers to scale the team.
Question: What are the best National Parks in the United States
Answer: Yosemite, Glacier, Zion, Yellowstone, Great Smoky Mountains, Rocky Mountain, Grand Teton, Acadia, Olympic, Bryce Canyon, Grand Canyon, Denali, Kenai Fjords, Arches...
Question: Is Python better than R?
Answer: It depends on what the language is being used to accomplish. For example, people tend to start counting at 1, so R begins counts at 1. Computer scientists, however, prefer zero-indexing, so Python begins counts at 0. R syntax is catered more to statistics, data parsing, and machine learning. Python is more typically used for production applications, including machine learning.
Question: Why Bangalore is so popular
Answer: It's the best IT sector in India
It has some awesome colleges and pubs and brews
Question: Identify which animal is domesticated or wild: Goldfish, Sika deer
Answer: Sika deer is wild, Goldfish is domesticated.
Question: Tell me if these teams are in or out of the NBA Playoffs for this year, Orlando Magic, Detroit Pistons, Boston Celtics, Dallas Mavericks, and Phoenix Suns
Answer: The teams from your list that are in the Playoffs are Boston Celtics & Phoenix Suns.  The remainder, is not in this years playoffs, those are the Orlando Magic, Detroit Pistons, & Dallas Mavericks.
Question: Which field hockey team has won the Stanley Cup twice in a row?
Answer: In a stifling game on Sunday, the Pittsburgh Penguins made the difference with a minute and a half left against Nashville (2-0) to pocket their second consecutive Stanley Cup (4-2).
Question: What is a headlamp?
Answer: A headlamp is a light that attaches to a person’s head, usually with a headband and shines light ahead of the person wearing it. Headlamps are usually powered by batteries.
Question: What dataset do the authors use in this paper?
Evidence: We study human-human negotiations in the buyer-seller bargaining scenario, which has been a key research area in the literature BIBREF0. In this section, we first describe our problem setup and key terminologies by discussing the dataset used. Later, we formalize our problem definition.
Dataset: For our explorations, we use the Craigslist Bargaining dataset (CB) introduced by BIBREF4. Instead of focusing on the previously studied game environments BIBREF5, BIBREF6, the dataset considers a more realistic setup: negotiating the price of products listed on Craigslist. The dataset consists of 6682 dialogues between a buyer and a seller who converse in natural language to negotiate the price of a given product (sample in Table TABREF1). In total, 1402 product ad postings were scraped from Craigslist, belonging to six categories: phones, bikes, housing, furniture, car and electronics. Each ad posting contains details such as Product Title, Category Type and a Listing Price. Moreover, a secret target price is also pre-decided for the buyer. The final price after the agreement is called the Agreed Price, which we aim to predict.
Defining the problem: Say we are provided with a product scenario $S$, a tuple: (Category, Title, Listing Price, Target Price). Define the interactions between a buyer and seller using a sequence of $n$ events $E_n:<e_{1}, e_{2}, ..., e_{n}>$, where $e_{i}$ occurs before $e_{j}$ iff $i<j$. Event $e_{i}$ is also a tuple: (Initiator, Type, Data). Initiator is either the Buyer or Seller, Type can be one of (message, offer, accept, reject or quit) and Data consists of either the corresponding natural language dialogue, offer price or can be empty. Nearly $80\%$ of events in CB dataset are of type `message', each consisting a textual message as Data. An offer is usually made and accepted at the end of each negotiation. Since the offers directly contain the agreed price (which we want to predict), we only consider `message' events in our models. Given the scenario $S$ and first $n$ events $E_n$, our problem is then to learn the function $f_{n}$: $A = f_{n}(S, E_n)$ where $A$ refers to the final agreed price between the two negotiating parties.

We use the Craigslist Bargaining dataset (CB) introduced by He et al. (2018).
Answer: The Craigslist Bargaining dataset (CB).
Question: What do you mean trekking?
Answer: Trekking - or long-distance hiking - consists of walking for several days or several weeks and being entirely or partially self-sufficient.
Question: Do they use any knowledge base to expand abbreviations?
Evidence: The overview of our approach is shown in Figure FIGREF6 . Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates. We train word embeddings using the clinical notes data with task-oriented resources such as Wikipedia articles of candidates and medical scientific papers and compute the semantic similarity between an abbreviation and its candidate expansions based on their embeddings (vector representations of words).
Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates.
Answer: Yes.
Question: How do I cook spaghetti?
Answer: Spaghetti is a popular noodle dish. To cook spaghetti, fill a pot with water and add 1-2 teaspoons of salt. Heat the water until it is boiling and then place the spaghetti into the pot. Wait 10-15 minutes, take one noodle out with a fork, and bite into it to see if the noodle is soft. If not wait 2 minutes are repeat until the spaghetti noodles become soft. At this point, turn off the heat under the pot, and strain the spaghetti to separate it from the water. Place the spaghetti onto a plate or bowl, and add any sauce if desired. Some people add additional seasoning, including salt, pepper, meat, garlic, and mushrooms.
Question: Given a reference text about Megan Reid, American professional soccer player, what college did she attend and how many soccer games did she start in her senior year of college?
Evidence: Reid attended the University of Virginia, where she made 83 appearances and scored two goals. Her senior year, she started all 21 games, scored a golden goal at No. 2 West Virginia, and had two game-winning assists. Also in her senior year, she was named to the All-ACC second team and the ACC Women's Soccer All-Academic team.
Answer: Reid attended the University of Virginia and during her senior year she started all 21 games.
Question: What are some of the major cities in Australia?
Answer: Some of the major cities in Australia are Sydney, Melbourne, Brisbane, and Perth.
Question: What will the author do about his future work?
Evidence: In this work, we have proposed an automated approach for the novel task of suggesting news articles to Wikipedia entity pages to facilitate Wikipedia updating. The process consists of two stages. In the first stage, article–entity placement, we suggest news articles to entity pages by considering three main factors, such as entity salience in a news article, relative authority and novelty of news articles for an entity page. In the second stage, article–section placement, we determine the best fitting section in an entity page. Here, we remedy the problem of incomplete entity section profiles by constructing section templates for specific entity classes. This allows us to add missing sections to entity pages. We carry out an extensive experimental evaluation on 351,983 news articles and 73,734 entities coming from 27 distinct entity classes. For the first stage, we achieve an overall performance with P=0.93, R=0.514 and F1=0.676, outperforming our baseline competitors significantly. For the second stage, we show that we can learn incrementally to determine the correct section for a news article based on section templates. The overall performance across different classes is P=0.844, R=0.885 and F1=0.860.
In the future, we will enhance our work by extracting facts from the suggested news articles. Results suggest that the news content cited in entity pages comes from the first paragraphs. However, challenging task such as the canonicalization and chronological ordering of facts, still remain.
In the future, we will enhance our work by extracting facts from the suggested news articles.
Answer: They will enhance their work by extracting facts from the suggested news articles.
Question: What is deep dish pizza?
Answer: Deep dish pizza is a variant of traditional pizza where the dough ends are rolled and pushed to the ends of a deep pan similar to a dessert pie crust. The crust is then filled with cheese and other toppings and spread with chunky tomato sauce. This type of pizza is also referred to as Chicago-style pizza.
Question: Which of the following are considered to be baked goods? A cake, a pie, chicken fingers, scone, and a scoop of ice cream
Answer: A cake, a pie, and a scone
Question: Classify the following Greek philosophers by their schools of thought: Democritus, Anaximander, Protagoras, Epictetus, Chrysippus, Theano, Pythagorus and Heraclides.
Answer: There were many diverse schools of thought in Ancient Greek philosophy. Democritus was a pre-socratic philosopher who founded the discipline of Atomism. Anaximander was another pre-socratic thinker who was a member of the Milesian school. Epictetus and Chrysippus were both stoic philosophers. Protagoras was a famous sophist who was referenced by Plato. Heraclides was a Platonist and direct student of Plato. Pythagoras and Theano were both of the Pythagorean school, and are believed to have been married.
Question: Do they compare DeepER against other approaches?
Evidence: FLOAT SELECTED: Table 3. Question answering accuracy of RAFAEL with different entity recognition strategies: quantities only (Quant), traditional NER (Nerf, Liner2 ), deep entity recognition (DeepER) and their combination (Hybrid).
FLOAT SELECTED: Table 3. Question answering accuracy of RAFAEL with different entity recognition strategies: quantities only (Quant), traditional NER (Nerf, Liner2 ), deep entity recognition (DeepER) and their combination (Hybrid).
Answer: Yes.
Question: Why is the Republican Party against gun control legislation?
Answer: There isn’t one answer to this question, and the answer isn’t strictly associated with the political party a person is affiliated with. But one theory, is many individuals assign more value to the right to own guns for sport or self-defense, than the lives lost by gun violence, because most individuals against gun control legislation are not impacted by gun violence.
Question: how to do a self-diagnose of burning out?
Answer: Examples to look out for burning out including: Inability to stop thinking about work even when you’re not at work; Relationships with either co-workers or families and friends suffer; Feeling unexplained aches and pains; You spend lots of money outsourcing various personal tasks; Feeling mentally strained- with no plans for vacations; You wonder where you’ll be in the future.
Question: Who is the greatest college football quarterback of all time?
Answer: Different people have different opinions when ranking college football players. Both individual and team metrics are considered when determining the best quarterback. Key individual metrics include individual passing metrics (such as passer rating, number of passing yards, and number of touchdowns thrown) and individual awards won (such as the Heisman trophy). Key team metrics include the team's win percentage with the individual as the starting quarterback and the number of conference and nation championships won. A quarterback that excels at passing the football and running the football is commonly referred to as a "dual threat" quarterback. Individual rushing statistics may also be considered when determining their rankings. Many people consider Sam Bradford, Matt Leinart, and Peyton Manning to be among the best pure passing quarterbacks in college football history. Many people consider Cam Newton, Vince Young, and Johnny Manziel to be among the best dual threat quarterbacks in college football history. Opinions vary, but many people consider Tim Tebow to be the greatest college football quarterback of all-time due to his individual accomplishments, his team's accomplishments and revolutionary offensive play style, and his popularity amongst fans and the sports media.
Question: How well does their system perform on the development set of SRE?
Evidence: In this section we present the results obtained on the protocol provided by NIST on the development set which is supposed to mirror that of evaluation set. The results are shown in Table TABREF26 . The first part of the table indicates the result obtained by the primary system. As can be seen, the fusion of MFCC and PLP (a simple sum of both MFCC and PLP scores) resulted in a relative improvement of almost 10%, as compared to MFCC alone, in terms of both INLINEFORM0 and INLINEFORM1 . In order to quantify the contribution of the different system components we have defined different scenarios. In scenario A, we have analysed the effect of using LDA instead of NDA. As can be seen from the results, LDA outperforms NDA in the case of PLP, however, in fusion we can see that NDA resulted in better performance in terms of the primary metric. In scenario B, we analysed the effect of using the short-duration compensation technique proposed in Section SECREF7 . Results indicate superior performance using this technique. In scenario C, we investigated the effects of language normalization on the performance of the system. If we replace LN-LDA with simple LDA, we can see performance degradation in MFCC as well as fusion, however, PLP seems not to be adversely affected. The effect of using QMF is also investigated in scenario D. Finally in scenario E, we can see the major improvement obtained through the use of the domain adaptation technique explained in Section SECREF16 . For our secondary submission, we incorporated a disjoint portion of the labelled development set (10 out of 20 speakers) in either LN-LDA and in-domain PLDA training. We evaluated the system on almost 6k out of 24k trials from the other portion to avoid any over-fitting, particularly important for the domain adaptation technique. This resulted in a relative improvement of 11% compared to the primary system in terms of the primary metric. However, the results can be misleading, since the recording condition may be the same for all speakers in the development set.
FLOAT SELECTED: Table 2. Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016.
In this section we present the results obtained on the protocol provided by NIST on the development set which is supposed to mirror that of evaluation set. The results are shown in Table TABREF26 .
FLOAT SELECTED: Table 2. Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016.
Answer: EER 16.04, Cmindet 0.6012, Cdet 0.6107
Question: Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}
Evidence: Conisbrough Castle is a medieval fortification in Conisbrough, South Yorkshire, England. The castle was initially built in the 11th century by William de Warenne, the Earl of Surrey, after the Norman conquest of England in 1066. Hamelin Plantagenet, the illegitimate, parvenu brother of Henry II, acquired the property by marriage in the late 12th century. Hamelin and his son William rebuilt the castle in stone, including its prominent 28-metre (92 ft)-high keep. The castle remained in the family line into the 14th century, despite being seized several times by the Crown. The fortification was then given to Edmund of Langley, passing back into royal ownership in 1461.
Answer: • William de Warenne
• Hamelin Plantagenet
• Henry II
• William Plantagenet
• Edmund of Langley
Question: What are the three major components of CEQE?
Evidence: The QE module receives as input a tuple INLINEFORM0 , where INLINEFORM1 is the source sentence, INLINEFORM2 is the translated sentence, and INLINEFORM3 is a set of word alignments. It predicts as output a sequence INLINEFORM4 , with each INLINEFORM5 . The overall architecture is shown in Figure FIGREF2 
CEQE consists of three major components: (1) embedding layers for words and part-of-speech (POS) tags in both languages, (2) convolution encoding of the local context for each target word, and (3) encoding the global context by the recurrent neural network.
CEQE consists of three major components: (1) embedding layers for words and part-of-speech (POS) tags in both languages, (2) convolution encoding of the local context for each target word, and (3) encoding the global context by the recurrent neural network.
Answer: (1) embedding layers for words and part-of-speech (POS) tags in both languages, (2) convolution encoding of the local context for each target word, and (3) encoding the global context by the recurrent neural network.
Question: What are the four hierarchical groups of the RCV21?
Evidence: The Reuters Corpus Volume 2 BIBREF2 , in short RCV2, is a multilingual corpus with a collection of 487,000 news stories. Each news story was manually classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). Topic codes were assigned to capture the major subject of the news story. The entire corpus covers thirteen languages, i.e. Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish, written by local reporters in each language. The news stories are not parallel. Single-label stories, i.e. those labeled with only one topic out of the four top categories, are often used for evaluations. However, the class distributions vary significantly across all the thirteen languages (see Table 1 ). Therefore, using random samples to extract evaluation corpora may lead to very imbalanced test sets, i.e. undesired and misleading variability among the languages when the main focus is to evaluate cross-lingual transfer.
Each news story was manually classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets).
Answer: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets).
Question: Could Wiki(GOLD) results be improved?
Evidence: The results for each class type are shown in Table TABREF19 , with some specific examples shown in Figure FIGREF18 . For the Wiki(gold) we quote the micro-averaged F-1 scores for the entire top level entity category. The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. It is worth noting that one could improve Wiki(gold) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset. The results in Table TABREF19 (OntoNotes) only show the main 7 categories in OntoNotes which map to Wiki(gold) for clarity. The other categories (date, time, norp, language, ordinal, cardinal, quantity, percent, money, law) have F-1 scores between 80-90%, with the exception of time (65%)
It is worth noting that one could improve Wiki(GOLD) results by training directly using this dataset.
Answer: Yes.
Question: Who is David Oliver Siegmund
Evidence: David Oliver Siegmund (born November 15, 1941) is an American statistician who has worked extensively on sequential analysis.

Biography
Siegmund grew up in Webster Groves, Missouri. He received his baccalaureate degree, in mathematics, from Southern Methodist University in 1963, and a doctorate in statistics from Columbia University in 1966. His Ph.D. advisor was Herbert Robbins. After being an assistant and then a full professor at Columbia, he went to Stanford University in 1976, where he is currently a professor of statistics. He has served twice as the chair of Stanford's statistics department. He has also held visiting positions at Hebrew University of Jerusalem, the University of Zurich, the University of Oxford, and the University of Cambridge.
Answer: David Oliver Siegmund (born November 15, 1941) is an American statistician who has worked extensively on sequential analysis.

Siegmund grew up in Webster Groves, Missouri. He received his baccalaureate degree, in mathematics, from Southern Methodist University in 1963, and a doctorate in statistics from Columbia University in 1966. His Ph.D. advisor was Herbert Robbins. After being an assistant and then a full professor at Columbia, he went to Stanford University in 1976, where he is currently a professor of statistics. 

He has served twice as the chair of Stanford's statistics department. He has also held visiting positions at Hebrew University of Jerusalem, the University of Zurich, the University of Oxford, and the University of Cambridge.