DataHammer/scimrc huggingface dataset:
Question: Classify these as healthy or not healthy food options or not a food: french fries, KFC, apple, fruit salad, steak, beer, wine, whisky, potato chips, baked cauliflower, wrench, gravel, McDonald's, Turkish kebab, pear, bricks, aloe vera, sand.
Answer: Healthy food options are: apple, fruit salad, steak, baked cauliflower, pear.
Not healthy food options are: french fries, KFC, beer, wine, whisky, potato chips, McDonald's, Turkish kebab.
Not a food: wrench, gravel, bricks, aloe vera, sand.
Question: What representations are presented by this paper?
Evidence: Following work by doukhan2018open, we wanted to explore the corpora looking at the number of speakers of each gender category as well as their speech duration, considering both variables as good features to account for gender representation. After the download, we manually extracted information about gender representation in each corpus.
Following work by doukhan2018open, we wanted to explore the corpora looking at the number of speakers of each gender category as well as their speech duration, considering both variables as good features to account for gender representation. 
Answer: The answers are shown as follows:
* the number of speakers of each gender category
* their speech duration
Question: What types of things can be plugged in and powered by electricity?
Answer: A hairdryer, television, computer and fridge are all examples of things that can be plugged in and powered by electricity.
Question: What is Delta Live Tables?
Answer: Delta Live Tables (DLT) is a declarative managed framework for building reliable, maintainable, and testable data pipelines.
Question: From the given text, list me details about parity plot
Evidence: A parity plot is a scatterplot that compares a set of results from a computational model against benchmark data. Each point has coordinates (x, y), where x is a benchmark value and y is the corresponding value from the model.

A line of the equation y = x, representing perfect model performance, is sometimes added as a reference. Where the model successfully reproduces a benchmark, that point will lie on the line.

Parity plots are found in scientific papers and reports, when the author wishes to validate a model in a visual way. However, when the data have a wide range, the large scale makes important discrepancies invisible and the model appears better than it actually is. In that case, a plot of model errors [(y-x) vs. x] is better for evaluating the performance of the model.
Answer: 1. A parity plot is a scatterplot that contrasts the output of a computer model with reference data.
2. Each point is identified by its coordinates (x, y), where x represents a benchmark value and y is the matching value from the model.
3. To serve as a reference, a line drawn from the equation y = x is occasionally included. Where the model accurately reproduces a benchmark, that point will fall on the line.
4. When an author wants to visually validate a model in a paper or report, parity plots are used. On the other hand, when the data span a wide range, the big scale obscures significant differences, giving the model the appearance of being better than it actually is. In that situation, a model error plot [(y-x) vs. x] is preferable for assessing the model's performance.
Question: What was the purpose of the Security Exchange Act of 1934?
Evidence: The Securities Exchange Act of 1934 (also called the Exchange Act, '34 Act, or 1934 Act) (Pub. L. 73–291, 48 Stat. 881, enacted June 6, 1934, codified at 15 U.S.C. § 78a et seq.) is a law governing the secondary trading of securities (stocks, bonds, and debentures) in the United States of America. A landmark of wide-ranging legislation, the Act of '34 and related statutes form the basis of regulation of the financial markets and their participants in the United States. The 1934 Act also established the Securities and Exchange Commission (SEC), the agency primarily responsible for enforcement of United States federal securities law.

Companies raise billions of dollars by issuing securities in what is known as the primary market. Contrasted with the Securities Act of 1933, which regulates these original issues, the Securities Exchange Act of 1934 regulates the secondary trading of those securities between persons often unrelated to the issuer, frequently through brokers or dealers. Trillions of dollars are made and lost each year through trading in the secondary market.
Answer: The purpose of the Security Exchange Act (SEA) of 1934 in the United States was to regulate the secondary trading market of securities like stocks and bonds between persons unrelated to the issuer.  This act also created the Securities and Exchange commission (SEC) that enforces United States federal securities law.
Question: Tell me about the football club Unione Sassuolo
Evidence: Unione Sportiva Sassuolo Calcio, commonly referred to as Sassuolo (Italian pronunciation: [sasˈswɔːlo]), is an Italian football club based in Sassuolo, Emilia-Romagna. Their colours are black and green, hence the nickname Neroverdi (literally "black and green", in Italian).

Sassuolo have played in Serie A since the 2013–14 season, joining a select group of teams playing in the Serie A but not belonging to a provincial capital city: U.S. Savoia 1908 (Torre Annunziata), Empoli, Legnano, Pro Patria (Busto Arsizio), Carpi and Casale.
Answer: Unione Sportiva Sassuolo Calcio, commonly referred to as Sassuolo is an Italian football club based in Sassuolo, Emilia-Romagna. Their colours are black and green, hence the nickname Neroverdi (literally "black and green", in Italian).

Sassuolo have played in Serie A since the 2013–14 season, joining a select group of teams playing in the Serie A but not belonging to a provincial capital city: U.S. Savoia 1908 (Torre Annunziata), Empoli, Legnano, Pro Patria (Busto Arsizio), Carpi and Casale.
Question: How many total soldiers sustained non-fatal injuries during WWI?
Evidence: World War I or the First World War (28 July 1914 – 11 November 1918), often abbreviated as WWI, was one of the deadliest global conflicts in history. It was fought between two coalitions, the Allies (primarily France, the United Kingdom, Russia, Italy, Japan, and the United States) and the Central Powers (led by Germany, Austria-Hungary, and the Ottoman Empire). Fighting occurred throughout Europe, the Middle East, Africa, the Pacific, and parts of Asia. An estimated 9 million soldiers were killed in combat, plus another 23 million wounded, while 5 million civilians died as a result of military action, hunger, and disease. Millions more died as a result of genocide, while the 1918 Spanish flu pandemic was exacerbated by the movement of combatants during the war.
Answer: 23 million
Question: Identify which instrument is string or percussion: Scabellum, Nyckelharpa
Answer: Nyckelharpa is string, Scabellum is percussion.
Question: How does Spark relate to Apache Hadoop?
Answer: Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Question: What are the names of the Stark children's direwolves?
Answer: Grey Wind, Lady, Nymeria, Summer, and Shaggydog.
Question: Give me three reasons why I can't attend Karen's party on Friday night as a bulleted list.
Answer: - Sorry, Karen, I was excited to come to your party but unfortunately I caught COVID and can no longer attend. I don't want to get anybody else sick!

- Karen, I'm going to be out of town this weekend and won't be able to make your party. But I hope you have fun!

- Unfortunately I won't be able to make it to the party of Friday, Karen. My in-laws will be in town this weekend and I'm going to have my hands full entertaining them.
Question: Who is the mercedes formula 1 team principal?
Answer: Toto Wolff is the team principal and CEO of the Mercedes-AMG PETRONAS F1 team
Question: Are tequila and mezcal basically the same thing?
Evidence: Mezcal is made from over 30 agave species, varieties, and subvarieties, in contrast with tequila, which is made only with blue agave. Of many agave species that can be used to make mezcal, seven are particularly notable. There is no exhaustive list, as the regulations allow any agaves, provided that they are not used as the primary material in other governmental Denominations of Origin. However, the interpretation of this regulation to mean that mezcal cannot be made from blue agave may be a mistranslation. The term silvestre "wild" is sometimes found, but simply means that the agaves are wild (foraged, not cultivated); it is not a separate variety.
Answer: Tequila and mezcal are similar in some ways, but they also have one distinct difference. Both liquors are made from agave. Tequila, however, is restricted to using only blue agave in its production process. Technically tequila is a type of mezcal because mezcal encompasses all liquors made from any type of agave. But unless blue agave is used to produce the liquor, it is not tequila.
Question: What are some of the common elements of visual design?
Evidence: Design elements are the basic units of any visual design which form its structure and convey visual messages. Painter and design theorist Maitland E. Graves (1902-1978), who attempted to gestate the fundamental principles of aesthetic order in visual design, in his book, The Art of Color and Design (1941), defined the elements of design as line, direction, shape, size, texture, value, and color, concluding that "these elements are the materials from which all designs are built."
Answer: Line, shape, size, texture, color and direction are some of the common visual design elements.
Question: When did the American Civil War end?
Answer: The American Civil War formally concluded on August 20, 1866 when President Andrew Johnson issued a proclamation declaring that civil authority was restored in all of the states.
Question: Which SOTA models are outperformed?
Evidence: Attention mechanism aligns and performs well with bi-sense embedding. MATT-BiE-LSTM and WATT-BiE-LSTM obtain similar performances when tested on both Vader and human annotated samples, though their ways of computing the attention (weights and vectors) are different that WATT computes attention weights and the senti-emoji embeddings guided by each word, and MATT obtains the senti-emoji embedding based on the LSTM encoder on the whole contexts and computes the attention weights of the senti-emoji embedding across all words. Both models outperforms the state-of-the-art baseline models including ATT-E-LSTM. The proposed attention-based LSTM can be further extended to handle tasks involving multi-sense embedding as inputs, such as the word-sense embedding in NLP, by using context-guide attention to self-select how much to attend on each sense of the embeddings each of which correspond to a distinct sense of semantics or sentiments. In this way we are able to take advantage of the more robust and fine-grained embeddings.
Attention-based LSTM with emojis:We also use the word-emoji embedding to calculate the emoji-word attention following Equation EQREF20 and EQREF21 , and the only difference is that we replace the attention-derived senti-emoji embedding with the pre-trained word-emoji embedding by fasttext, denoted as ATT-E-LSTM.
LSTM with bi-sense emoji embedding (proposed): As we have introduced in Section SECREF13 , we propose two attention-based LSTM networks based on bi-sense emoji embedding, denoted as MATT-BiE-LSTM and WATT-BiE-LSTM.
MATT-BiE-LSTM and WATT-BiE-LSTM obtain similar performances when tested on both Vader and human annotated samples, though their ways of computing the attention (weights and vectors) are different that WATT computes attention weights and the senti-emoji embeddings guided by each word, and MATT obtains the senti-emoji embedding based on the LSTM encoder on the whole contexts and computes the attention weights of the senti-emoji embedding across all words. Both models outperforms the state-of-the-art baseline models including ATT-E-LSTM
Attention-based LSTM with emojis:We also use the word-emoji embedding to calculate the emoji-word attention following Equation EQREF20 and EQREF21 , and the only difference is that we replace the attention-derived senti-emoji embedding with the pre-trained word-emoji embedding by fasttext, denoted as ATT-E-LSTM.
LSTM with bi-sense emoji embedding (proposed): As we have introduced in Section SECREF13 , we propose two attention-based LSTM networks based on bi-sense emoji embedding, denoted as MATT-BiE-LSTM and WATT-BiE-LSTM.
Answer: The answers are shown as follows:
* Attention-based LSTM with emojis
Question: Why is it important to lift weights?
Answer: Lifting weights is important for many reasons. Lifting weights can impact a body's physical appearance and strength, has anti-aging benefits, helps prevent osteoporosis, improves joint health, and improves cardiovascular health. Additionally, there are many popular competitions centered around strength and conditioning which has indirectly promoted various strength sports throughout the world.
Question: What language are the conversations in?
Evidence: The language is Chinese, which is not easy for non-Chinese-speaking researchers to work on.
The language is Chinese, which is not easy for non-Chinese-speaking researchers to work on.
Answer: The answers are shown as follows:
* The language is Chinese, which is not easy for non-Chinese-speaking researchers to work on.
Question: What is the list of top hybrid cars in the US market in 2023
Answer: Toyota RAV4 Hybrid.
Kia Sportage Hybrid.
Honda CR-V Hybrid.
Toyota RAV4 Prime.
Hyundai Tucson Plug-in Hybrid.
Mitsubishi Outlander Hybrid.
Hyundai Santa Fe Plug-in Hybrid.
Jeep Grand Cherokee 4xe.
Question: What do propaganda techniques include?
Evidence: Propaganda uses psychological and rhetorical techniques to achieve its objective. Such techniques include the use of logical fallacies and appeal to emotions. For the shared task, we use 18 techniques that can be found in news articles and can be judged intrinsically, without the need to retrieve supporting information from external resources. We refer the reader to BIBREF10 for more details on the propaganda techniques; below we report the list of techniques:
Propaganda uses psychological and rhetorical techniques to achieve its objective. Such techniques include the use of logical fallacies and appeal to emotions.
Answer: The use of logical fallacies and appeal to emotions.
Question: Tell me whether these auto manufacturers are located in Germany or Japan: Volkswagen, Toyota, BMW, Honda, Adam Opel, Mazda, Daimler, Subaru, Continental, Hino
Answer: Germany - Volkwagen, BMW, Adam Opel, Daimler, Continental | Japan - Toyota, Honda, Mazda, Subaru, Hino
Question: Is the proposed method compared to previous methods?
Evidence: Following the algorithms above, with the consideration of both the advantages and disadvantages of them, in this project, I am going to use a modified method: sound-class based skip-grams with bipartite networks (BipSkip). The whole procedure is quite straightforward and could be divided into three steps. First step: the word pair and their skip-grams are two sets of the bipartite networks. The second step is optional, which is to refine the bipartite network. Before I run the program, I will be asked to input a threshold, which determines if the program should delete the skip-gram nodes linked to fewer word nodes than the threshold itself. According to the experiment, even though I did not input any threshold as one of the parameters, the algorithm could still give the same answer but with more executing time. In the last step, the final generated bipartite graph would be connected to a monopartite graph and partitioned into cognate sets with the help of graph partitioning algorithms. Here I would use Informap algorithm BIBREF12. To make a comparison to this method, I am using CCM and SCA for distance measurement in this experiment, too. UPGMA algorithm would be used accordingly in these two cases.
Here I would use Informap algorithm BIBREF12. To make a comparison to this method, I am using CCM and SCA for distance measurement in this experiment, too. UPGMA algorithm would be used accordingly in these two cases.
Answer: Yes.
Question: What caused Covid 19?
Answer: The novel virus was first identified in an outbreak in the Chinese city of Wuhan in December 2019. Attempts to contain it there failed, allowing the virus to spread to other areas of Asia and later worldwide. The World Health Organization (WHO) declared the outbreak a public health emergency of international concern on 30 January 2020, and a pandemic on 11 March 2020. As of 10 March 2023, the pandemic had caused more than 676 million cases and 6.88 million confirmed deaths, making it one of the deadliest in history.
Question: Who is the winningest coach in Seattle Seahawks history?
Answer: Pete Carrol
Question: What are the author's plans for his future work?
Evidence: The paper studies the effectiveness of various affect lexicons word embeddings to estimate emotional intensity in tweets. A light-weight easy to use affect computing framework (EmoInt) to facilitate ease of experimenting with various lexicon features for text tasks is open-sourced. It provides plug and play access to various feature extractors and handy scripts for creating ensembles.
Few problems explained in the analysis section can be resolved with the help of sentence embeddings which take the context information into consideration. The features used in the system are generic enough to use them in other affective computing tasks on social media text, not just tweet data. Another interesting feature of lexicon-based systems is their good run-time performance during prediction, future work to benchmark the performance of the system can prove vital for deploying in a real-world setting.
Another interesting feature of lexicon-based systems is their good run-time performance during prediction, future work to benchmark the performance of the system can prove vital for deploying in a real-world setting.
Answer: Future work to benchmark the performance of the system can prove vital for deploying in a real-world setting.
Question: What are the components of the multilingual framework?
Evidence: In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier. B4MSA uses a number of text transformations that are categorized in cross-language features (see Subsection SECREF3 ) and language dependent features (see Subsection SECREF9 ). It is important to note that, all the text-transformations considered are either simple to implement or there is a well-known library (e.g. BIBREF6 , BIBREF7 ) to use them. It is important to note that to maintain the cross-language property, we limit ourselves to not use additional knowledge, this include knowledge from affective lexicons or models based on distributional semantics.
In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier.
Answer: The answers are shown as follows:
* text-transformations to the messages
* vector space model
* Support Vector Machine
Question: What is the name of Frankenstein's monster?
Answer: Contrary to what many people think, the monster is not named Frankenstein. Dr. Frankenstein is the name of the scientist who created the monster in Mary Shelley's 1818 novel, "Frankenstein; or, The Modern Prometheus." The monster does not have a name and is simply referred to as "the Creature."
Question: Which singer’s real name is Stefani Joanne Angelina Germanotta?
Answer: Lady Gaga
Question: When was the first idea of mobile phone developed, and was it created or commercialized at that time?
Evidence: A handheld mobile radio telephone service was envisioned in the early stages of radio engineering. In 1917, Finnish inventor Eric Tigerstedt filed a patent for a "pocket-size folding telephone with a very thin carbon microphone". Early predecessors of cellular phones included analog radio communications from ships and trains. The race to create truly portable telephone devices began after World War II, with developments taking place in many countries. The advances in mobile telephony have been traced in successive "generations", starting with the early zeroth-generation (0G) services, such as Bell System's Mobile Telephone Service and its successor, the Improved Mobile Telephone Service. These 0G systems were not cellular, supported few simultaneous calls, and were very expensive.
Answer: The early idea of mobile phone goes back to the early stages of radio engineering. It was in 1917 that a Finnish inventor Eric Tigerstedt filed a patent for a "pocket-size folding telephone with a very thin carbon microphone". 
No, the real handheld mobile radio telephones were not created or commercialized at that time. It was after World War II that the race to create truly portable telephone devices began with developments taking place in many countries.
Question: How they measure robustness in experiments?
Evidence: First, the Plackett-Luce models boost the training BLEU very greatly, even up to 2.5 points higher than MIRA. This verifies our assumption, richer features benefit BLEU, though they are optimized towards a different objective.
Second, the over-fitting problem of the Plackett-Luce models PL($k$) is alleviated with moderately large $k$. In PL(1), the over-fitting is quite obvious, the portion in which the curve overpasses MIRA is the smallest compared to other $k$, and its convergent performance is below the baseline. When $k$ is not smaller than 5, the curves are almost above the MIRA line. After 500 L-BFGS iterations, their performances are no less than the baseline, though only by a small margin.
First, the Plackett-Luce models boost the training BLEU very greatly, even up to 2.5 points higher than MIRA. This verifies our assumption, richer features benefit BLEU, though they are optimized towards a different objective.

Second, the over-fitting problem of the Plackett-Luce models PL($k$) is alleviated with moderately large $k$. In PL(1), the over-fitting is quite obvious, the portion in which the curve overpasses MIRA is the smallest compared to other $k$, and its convergent performance is below the baseline. When $k$ is not smaller than 5, the curves are almost above the MIRA line.
Answer: The answers are shown as follows:
* boost the training BLEU very greatly
* the over-fitting problem of the Plackett-Luce models PL($k$) is alleviated with moderately large $k$