DataHammer/scimrc huggingface dataset:
Question: Write a grocery list of typical items a person might get from the grocery store.
Answer: The items a person might need from the grocery store will vary on the dietary needs of the person, as well as the amount of people in the household the person is shopping for, and if they're shopping for a special event or just for regular daily meals. With that in mind, here are some typical grocery shopping items that a person might get from a typical grocery shopping trip: milk, bread, eggs, coffee, olive oil, rice, chicken, steak, apples, oranges, salad mix, soda, sugar, flour, salt.
Question: Which of these are ski mountains and which are deserts: Killington, Stowe, Sahara, Kalahari, Palisades, Gobi, Northstar, and Kirkwood.
Answer: The deserts include: Sahara, Kalahari, and Gobi. The ski resorts are: Killington, Stowe, Palisades, Northstar, and Kirkwood.
Question: How many players are in a cricket team?
Answer: A cricket team is formally comprised of eleven players
Question: What is the difference between a bike and an electric bike
Answer: An electric bike is very similar to a traditional bike with a few additional components. 
They have a similar style of wheels, handlebars, and frame style as a regular bike. In addition, they have an electric motor, battery, and controller for the user. The battery powers the motors which offer assistance while pedaling. An e-bike does not change the pedaling action, it just makes it feel simpler.
Question: According to the below, what is the Markov decision process?
Evidence: In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, Dynamic Programming and Markov Processes. They are used in many disciplines, including robotics, automatic control, economics and manufacturing. The name of MDPs comes from the Russian mathematician Andrey Markov as they are an extension of Markov chains.
Answer: A Markov decision process (MDP) is a mathematical framework  for modeling decision making in cases where outcomes are not entirely under the control of decision makers. MDPs are used in a variety of disciplines, such as robotics and economics.
Question: Define judicial review and explain John Marshall’s role in establishing judicial review.
Answer: Judicial review is a power the courts have to declare that a federal or state law violates the Constitution thereby nullifying the law making it enforceable. Just before President Adams left office he appointed new justices of the peace for Washington D.C., Jefferson, the next President refused to deliver the commissions.  This led to the case Marbury vs Madison.  The basis of the case was that the Judiciary Act of 1789 allowed for the appointed to request a court order to grant their appointments with original jurisdiction given to the Supreme Court. John Marshall saw that siding with Jefferson would make it seem like the Supreme Court was his lackey.  If they sided with Adams, there was no way to make Jefferson enforce the ruling.  Both decisions would reduce the Supreme Court's legitimacy. John Marshall side-stepped this problem by striking down the Judiciary Act on the grounds that it was unconstitutional. This was the establishment of judicial review, a Supreme Court power that was only hinted at in the Constitution.
Question: Why is healthcare so bad in US?
Answer: Let's talk what is bad mean? Bad here probably refers to the cost of drugs and how the insurance industry operates. It also refers to not everyone having universal health care. Ask yourself what is the goal of the pharma and insurance industry. Dig deep and you will find that it is not driven to improve the health of the citizen and society but make money at the cost of people lives. Someone rightfully said US pharma industry is only interested in diseases of the rich like heart and diabetes and not of the poor. We have seen changes in the auto industry, software industry and it is time we bring change in healthcare industry where it is affordable and everyone has right to good healthcare. Lobbying and exclusive control (yes, they invest billions) needs change.
Question: What method does this paper propose?
Evidence: Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3 . Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4 . Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios.
However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child). On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs. However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En). Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9 .
In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. Furthermore, the amount of indirect in-domain parallel corpora, i.e., Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small. As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9 .
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows:
To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling and domain adaptation.
Answer: A novel multistage fine-tuning method for NMT.
Question: What did the experiment show?
Evidence: Building effective machine learning models for text requires data and different resources such as pre-trained word embeddings and reusable lexicons. Unfortunately, most of these resources are not entirely transferable to different domains, tasks or languages. Sentiment analysis is one such task that requires additional effort to transfer knowledge between languages.
In this paper, we studied the research question: Can we build reusable sentiment analysis models that can be utilized for making inferences in different languages without requiring separate models and resources for each language? To that end, we built a recurrent neural network model in the language that had largest data available. We took a general-to-specific model building strategy where the larger corpus that had reviews from different domains was first used to train the RNN model and a smaller single-domain corpus of sentiment reviews was used to specialize the model on the given domain. During scoring time, we used corpora for the given domain in different languages and translated them to English to be able to classify sentiments with the trained model. Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline.
In this paper we made the sentiment analysis model specific to a single domain. For future work, we would like to investigate the effectiveness of our model on different review domains including hotel reviews and on different problems such as detecting stance.
Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline.
Answer: Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline.
Question: According to the paragraph below, what is Generative AI?
Evidence: A generative artificial intelligence or generative AI is a type of AI system capable of generating text, images, or other media in response to prompts. Generative AI systems use generative models such as large language models to statistically sample new data based on the training data set that was used to create them.
Answer: Generative Artificial Intelligence (AI) refers to AI systems that respond to user prompts to generate text, images and other media.
Question: Given this reference text about the Flex Computer System, why was it created?
Evidence: The Flex Computer System was developed by Michael Foster and Ian Currie of Royal Signals and Radar Establishment in Malvern, England, during the late 1970s and 1980s. It used a tagged storage scheme to implement a capability architecture, and was designed for the safe and efficient implementation of strongly typed procedures.
Answer: It was designed for the safe and efficient implementation of strongly typed procedures.
Question: Tell me whether these are dogs or cats: bulldogs, poodles, huskies, collies, shepherds
Answer: Dogs
Question: Who was the ruling member of the Sharabhapuriya dynasty in 575 CE?
Evidence: A. M. Shastri estimates the reigns of the Sharabhapuriya rulers as follows: based on Sharabha's identification with the Sharabharaja mentioned in the 510 CE Eran inscription, he assigns Sharabha's reign to 475–500 CE (assuming a 25-year reign). Similarly, he assigns 25 years to the next two kings. Since Jayaraja's latest inscription is dated to 9th regnal year, Shastri assumes a 10-year reign for Jayaraja, and some subsequent kings.

Sharabha (Śarabha), c. 475–500 CE
Narendra, c. 500–525 CE
Prasanna, c. 525–550 CE
Jayarāja, c. 550–560 CE
Sudevarāja, c 560–570 CE
Manamatra alias Durgarāja, c. 570–580 CE
Sudevarāja, c. 570–580 CE
Pravarāja, c. 580–590 CE
Alternative dates have been proposed by other historians. For example, D. C. Sircar estimated Sharabha's reign as 465–480 CE, while V. V. Mirashi estimated it as 460–480 CE.
Answer: Based on that paragraph, the ruler during 575 CE was Sudevarāja, who ruled from c. 570–580 CE. Note that this may be an estimate, since all of the time periods listed have a "c." (short for "circa"), which indicates some uncertainty about the time ranges listed.
Question: List five titles by Robert Jordan.
Answer: The Great Hunt, Towers of Midnight, Lord of Chaos, A Crown of Swords, Crossroads of Twilight
Question: Which models are best for learning long-distance movement?
Evidence: We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations.
We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations.
We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations.
Answer: The answers are shown as follows:
* the transformer models
Question: What is the difference between men's and women's lacrosse
Answer: Men's lacrosse is a contact sport and the players wear helmets, shoulder pads, chest protection and elbow pads.  They also have a position of defense called Long Pole where they can defend from far away.  Women's lacrosse does not allow contact between players and the only required equipment is a mouth guard and goggles.  Women's lacrosse relies on the shooting space rule to protect players from potential injury instead of requiring them to wear additional equipment. Another difference is the field dimensions. The women’s field is 120 yards long and 70 yards wide, compared to the men’s field, which is slightly smaller at 110 yards long and 60 yards wide.  Lastly, men play with 9 field players whereas women play with 11.
Question: Which language family does Mboshi belong to?
Evidence: Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17. On the Mboshi side, we consider alphabetic representation with no tonal information. On the French side,we simply consider the default segmentation into words.
Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17.
Answer: The answers are shown as follows:
* Bantu
Question: According to their experiment, what kind of method combination achieves the best result?
Evidence: Table TABREF5 shows the results of various methods on the hate speech detection task. Part A shows results for baseline methods. Parts B and C focus on the proposed methods where part B contains methods using neural networks only, while part C uses average of word embeddings learned by DNNs as features for GBDTs. We experimented with multiple classifiers but report results mostly for GBDTs only, due to lack of space.
As the table shows, our proposed methods in part B are significantly better than the baseline methods in part A. Among the baseline methods, the word TF-IDF method is better than the character n-gram method. Among part B methods, CNN performed better than LSTM which was better than FastText. Surprisingly, initialization with random embeddings is slightly better than initialization with GloVe embeddings when used along with GBDT. Finally, part C methods are better than part B methods. The best method is “LSTM + Random Embedding + GBDT” where tweet embeddings were initialized to random vectors, LSTM was trained using back-propagation, and then learned embeddings were used to train a GBDT classifier. Combinations of CNN, LSTM, FastText embeddings as features for GBDTs did not lead to better results. Also note that the standard deviation for all these methods varies from 0.01 to 0.025.
To verify the task-specific nature of the embeddings, we show top few similar words for a few chosen words in Table TABREF7 using the original GloVe embeddings and also embeddings learned using DNNs. The similar words obtained using deep neural network learned embeddings clearly show the “hatred” towards the target words, which is in general not visible at all in similar words obtained using GloVe.
The best method is “LSTM + Random Embedding + GBDT” where tweet embeddings were initialized to random vectors, LSTM was trained using back-propagation, and then learned embeddings were used to train a GBDT classifier.
Answer: The best method is “LSTM + Random Embedding + GBDT”.
Question: Propose a text about young boy that wanted to fly his kite over the skyscrapers. Make this a very positive story with a twist
Answer: Bob is a fun a loving kid that spends lots of his free time outdoors. Whenever there is a good wind he takes his kite, a gift from his dad, and goes out to play and fly. One day his dad was going to a very big city to talk with some of his partners. Bob got immediately very excited. It was his dream to take his kite and fly it from one of the very big and tall buildings. His dad made this all happen. Together with his son they went on the roof and played with the kite.
Question: What linguistic quality aspects are addressed?
Evidence: FLOAT SELECTED: Figure 1: SUM-QE rates summaries with respect to five linguistic qualities (Dang, 2006a). The datasets we use for tuning and evaluation contain human assigned scores (from 1 to 5) for each of these categories.
FLOAT SELECTED: Figure 1: SUM-QE rates summaries with respect to five linguistic qualities (Dang, 2006a). The datasets we use for tuning and evaluation contain human assigned scores (from 1 to 5) for each of these categories.
Answer: Grammaticality, non-redundancy, referential clarity, focus, structure & coherence
Question: What are the common personality traits of cats?
Answer: Cats are all individuals, they each may have different personality traits. Some common personality traits that humans typically ascribe to cats include loving, standoffish, active, aloof, playful, and compassionate.
Question: What is the largest of the North American Great Lakes?
Answer: Lake Superior is the largest of the Great Lakes. The water volume from the other four Great Lakes (Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario) could all fit within Lake Superior.
Question: What did Stevie Ray Vaughan name his most beloved guitar?
Answer: Jimbo.
Question: Who is Robert Mitchum?
Evidence: Robert Mitchum (1917–1997) was an American actor who appeared in over 110 films and television series over the course of his career. He is ranked 23rd on the American Film Institute's list of the 50 greatest American screen legends of all time. His first credited named role was as Quinn in the 1943 western Border Patrol. That same year he appeared in the films Follow the Band, Beyond the Last Frontier, Cry 'Havoc' and Gung Ho! as well as several Hopalong Cassidy films including Colt Comrades, Bar 20, False Colors, and Riders of the Deadline. In 1944, he starred in the western Nevada as Jim "Nevada" Lacy, and a year later in the film West of the Pecos as Pecos Smith. During the 1940s, he was also cast in the film noirs Undercurrent (1946), Crossfire (1947), Out of the Past (1947) and The Big Steal (1949). Mitchum was nominated for the Academy Award for Best Supporting Actor for his role as a world-weary soldier in the 1945 film The Story of G.I. Joe, which received critical acclaim and was a commercial success.
Answer: Robert Mitchum was a prolific American actor, appearing in over 110 films and television shows.  He was an Academy Award nominee and his primary film genre was Western.
Question: Where was Old French language spoken?
Evidence: Old French (franceis, françois, romanz; Modern French: ancien français) was the language spoken in most of the northern half of France from approximately the 8th to the 14th centuries. Rather than a unified language, Old French was a linkage of Romance dialects, mutually intelligible yet diverse, spoken in the northern half of France. These dialects came to be collectively known as the langue d'oïl, contrasting with the langue d'oc in the south of France. The mid-14th century witnessed the emergence of Middle French, the language of the French Renaissance in the Île de France region; this dialect was a predecessor to Modern French. Other dialects of Old French evolved themselves into modern forms (Poitevin-Saintongeais, Gallo, Norman, Picard, Walloon, etc.), each with its own linguistic features and history.
Answer: Northern half of France in the 8th to 14th centuries.
Question: What is RSU?
Answer: Restricted stock units (RSU), also known as restricted securities, is stock of a company that is not fully transferable (from the stock-issuing company to the person receiving the stock award) until certain conditions (restrictions) have been met. Upon satisfaction of those conditions, the stock is no longer restricted, and becomes transferable to the person holding the award. Restricted stock is often used as a form of employee compensation, in which case it typically becomes transferable ("vests") upon the satisfaction of certain conditions, such as continued employment for a period of time or the achievement of particular product-development milestones, earnings per share goals or other financial targets. Restricted stock is a popular alternative to stock options, particularly for executives, due to favorable accounting rules and income tax treatment.
Question: Write a paragraph to convince someone to visit Porto Portugal.
Answer: Porto Portugal is an amazing travel destination that is easily accessible by train from the capital of Lisbon.  Porto is coastal town with great beaches and is the primary distributer of port wine that is grown in vineyards from the Douro Valley.  In Porto you can explore the many distributors that age the port wine and take a trip on the Douro River in one of the boats that used to be used to transport port barrels from the vineyards to Porto.  You can also take a day trip by bus to the Douro Valley and visit the many vineyards and sample the port wine.
Question: List five titles by J.K. Rowling.
Answer: Harry Potter and the Sorcerer's Stone, The Ickabog, Fantastic Beasts and Where to Find Them, The Cuckoo's Calling, Harry Potter and the Order of the Phoenix
Question: In geology, what are the main classification for rocks?
Answer: There are three general classes of rocks: Igneous, Sedimentary, and Metamorphic.
Question: How do I find the best interior decorator and best price?
Answer: There are many ways to find the best interior decorator.  You can use tools like Angie's List, Yelp, or Google to first search for an interior decorator.  After you find someone evaluate their qualifications based on other projects they have completed.  If you like their portfolio ask them about pricing and share your requirements for your interior design project requirements.  After an agreement is made the interior project can be executed successfully between both parties for the best cost and best choice for interior designer.
Question: Identify which instrument is string or percussion: Ranat ek, Sasando
Answer: Sasando is string, Ranat ek is percussion.