DataHammer/scimrc huggingface dataset:
Question: Where is the Lighthouse Point, Bahamas
Evidence: Lighthouse Point, Bahamas, or simply Lighthouse Point, is a private peninsula in The Bahamas which serves as an exclusive port for the Disney Cruise Line ships. It is located in the south-eastern region of Bannerman Town, Eleuthera. In March 2019, The Walt Disney Company purchased the peninsula from the Bahamian government, giving the company control over the area.
Answer: The Lighthouse Point, Bahamas, or simply Lighthouse Point, is a private peninsula in the Bahamas which serves as an exclusive port for the Disney Cruise Line ships. It is located in the south-eastern region of Bannerman Town, Eleuthera.
Question: Which systems perform the best?
Evidence: In this section, we show the model performances of our proposed crowdsourcing learning system (ALCrowd), and meanwhile compare it with the other systems mentioned above. Table 2 shows the experimental results on the DL-PS datasets and Table 3 shows the experiment results on the EC-MT and EC-UQ datasets, respectively.
The results of CRF and LSTM-CRF mean that the crowd annotation is an alternative solution with low cost for labeling data that could be used for training a NER system even there are some inconsistencies. Compared with CRF, LSTM-CRF achieves much better performances on all the three data, showing +6.12 F1 improvement on DL-PS, +4.51 on EC-MT, and +9.19 on EC-UQ. This indicates that LSTM-CRF is a very strong baseline system, demonstrating the effectiveness of neural network.
Interestingly, when compared with CRF and LSTM-CRF, CRF-VT and LSTM-CRF-VT trained on the voted version perform worse in the DL-PS dataset. This trend is also mentioned in BIBREF4 nguyen2017aggregating. This fact shows that the majority voting method might be unsuitable for our task. There are two possible reasons accounting for the observation. On the one hand, simple character-level voting based on three annotations for each sentence may be still not enough. In the DL-PS dataset, even with only two predefined entity types, one character can have nine NE labels. Thus the majority-voting may be incapable of handling some cases. While the cost by adding more annotations for each sentence would be greatly increased. On the other hand, the lost information produced by majority-voting may be important, at least the ambiguous annotations denote that the input sentence is difficult for NER. The normal CRF and LSTM-CRF models without discard any annotations can differentiate these difficult contexts through learning.
Three crowd-annotation learning systems provide better performances than their counterpart systems, (CRF-MA VS CRF) and (LSTM-Crowd/ALCrowd VS LSTM-CRF). Compared with the strong baseline LSTM-CRF, ALCrowd shows its advantage with +1.08 F1 improvements on DL-PS, +1.24 on EC-MT, and +2.38 on EC-UQ, respectively. This indicates that adding the crowd-annotation learning is quite useful for building NER systems. In addition, ALCrowd also outperforms LSTM-Crowd on all the datasets consistently, demonstrating the high effectiveness of ALCrowd in extracting worker independent features. Among all the systems, ALCrowd performs the best, and significantly better than all the other models (the p-value is below $10^{-5}$ by using t-test). The results indicate that with the help of adversarial training, our system can learn a better feature representation from crowd annotation.
Among all the systems, ALCrowd performs the best, and significantly better than all the other models (the p-value is below 10−5 by using t-test).
Answer: Among all the systems, ALCrowd performs the best, and significantly better than all the other models (the p-value is below 10−5 by using t-test).
Question: What are the names of some of the cities in the Boston Metro West area?
Answer: Boston Metro West cities include Newton, Natick, Framingham, Wellesley, Dedham, and Needham, among others.
Question: Based on the provided text, In what year did the National Gallery of Ireland open its doors to the public and what kind of art is it known for?
Evidence: The National Gallery of Ireland (Irish: Gailearaí Náisiúnta na hÉireann) houses the national collection of Irish and European art. It is located in the centre of Dublin with one entrance on Merrion Square, beside Leinster House, and another on Clare Street. It was founded in 1854 and opened its doors ten years later. The gallery has an extensive, representative collection of Irish paintings and is also notable for its Italian Baroque and Dutch masters painting. The current director is Caroline Campbell.
Answer: The National Gallery of Ireland opened in 1864 and is known for its Irish, Italian Baroque, and Dutch masters paintings
Question: Plan a weekend long vacation to Madrid, and mention multiple specific places you would visit and how you would travel in the city
Answer: On Saturday we would start off by visiting the Reina Sofia museum, which we would get to by taking the metro system. We would then continue to the Prado art museum in the afternoon, which we would also get to by metro and walking from the Reina Sofia. We'd then grab a late lunch at a nearby cafe, before walking over to the nearby Parque Retiro, which is one of the main parks in Madrid. We'd spend the afternoon there and take out a boat for a row on the lake, and then in the evening we'd grab empanadas for dinner from a bakery. The next day, we'd start off the day with a casual coffee and slow morning breakfast. We'd then check out the botanical gardens, and then the National Anthropological Museum in the afternoon. Finally, we'd finish our weekend with a visit to the Madrid Opera house.
Question: The federated learning setting is realistic because it can extend to scenarios with many clients. However, this paper mainly considers one client. The multiple clients in the appendix all come from one target domain, but the realistic setting is that different clients come from different domains. Do the author consider it?
Evidence: When a single edge device only has limited number of target data, it is hard to learn the fine-grained representations of the target data even for the large model. As a result, the compact model trained by Co-KD still suffers low inference accuracy on the target data. To tackle this challenging, Co-KD can be extended to unify the target data of the multiple edge devices for training a global compact model.****Two group of experiments are conducted in the case of multiple edge devices. First, the target data on the multiple edge devices come from the same target domain. Second, the target data on the multiple edge devices come from different target domains. More specifically, in the second experiments, the edge devices are divided into two groups. The devices from the same group have the target data from the same domain, while the target domains of the two groups are different.
Answer: Indeed we mainly consider one client in the main text. The reason is as follows. If the clients are assumed to come from the same target domain, our method is applicable to either a single client or multiple clients. For the clarity of presentation,  in the main text our method is presented under the setup of a single client. Furthermore, the scenario of multiple clients is presented in the appendix. For the scenario where multiple clients come from different target domains, we will conduct additional experiments to evaluate the performance of our scheme. It should be noted that our scheme is not specifically designed for different target domains, so the model may not perform equally well on all the target domains. Tackling the issue of different target domains is an interesting topic for further study.
Question: Given a reference text about Ihor Lapin, tell me what he did before the war.
Evidence: Ihor Lapin (born 28 May 1969 in Ivanychi) is a Ukrainian lawyer and politician who is a former (2014-2019) Member of the Parliament of Ukraine.

During the War in Donbass (eastern Ukraine) he was commander of the 2nd assault squadron "West" of the 24th Battalion of Territorial Defense "Aidar" of the Armed Forces of Ukraine, formed mainly from volunteers from Volyn Region.

In the Russian-Ukrainian war, he was the commander of the Special Forces Battalion of the First Separate Special Forces Brigade, named after Ivan Bohun.

Before the war, he worked as a lawyer, and was a member of the Bar Qualification-Disciplinary Commission of Volyn Region.
Answer: Before the war, Ihor Lapin was a lawyer and a member of the Bar Qualification-Disciplinary Commission.
Question: How many atomic bombs have been used in combat in human history?
Answer: Two atomic bombs have been deployed against populations in human history
Question: Identify which instrument is string or woodwind: Harpsichord, Quena
Answer: Quena is woodwind, Harpsichord is string.
Question: What are some interesting facts about tigers?
Answer: 1. Tiger stripes are unique.
2. Tigers can use their ears to communicate.
3. India has over half the world's population of tigers.
4. Only one in ten hunts are successful.
Question: Classify each of the following as either a city or a country:  Seoul, Shanghai, Sao Paolo, Singapore, Sweden.
Answer: Seoul is the largest city in South Korea and also the capital.  Shanghai is the most populous city in China and is also known as "The Pearl of Asia."  Sao Paolo is a city in the Brazilian state of the same name, Sao Paolo and is named after an Apostle, Saint Paul of Tarsus.  Singapore is simultaneously a city, state, and country.  Sweden is a country which has produced many world-renowned pop singers and bands including Abba.
Question: What are some ways to care for a new tree
Answer: There are many ways to care for a young tree to ensure its future health and success.  Some ways you may want to care for a young tree include:
 - Frequent watering.  The amount of water and frequency will vary depending on the tree and climate but frequent, deep watering is essential for early development.
 - Nutrients.  Fertilizers may aid in the early stages.  Be sure to read the manufacturers label and instructions before application.
 - Mulch.  Applying mulch to the base of the tree aids in weeds suppression and moisture retention. 
 - Remove nearby weeds/plants.  Weeds and plants can provide competition to your tree for important resources.  Remove any near the root system of your tree.

These are just some ways you can ensure a healthy start to your tree!
Question: Tell me which following countries are in Asia: United States, Canada, Britain, France, Australia, Korea, China, Egypt, Israel, Japan, Brazil
Answer: Given your list, only Korea, China, and Japan are countries in Asia.
Question: What family was well known for their power in Renaissance Italy?
Answer: The Medici family was a famously influential presence in Renaissance Italy
Question: Think of some family rules to promote a healthy family relationship
Answer: Use Kind words
Be grateful
Think of others before yourself
Work hard
Be Honest
Be thankful
Keep your promises
Choose to be happy
Always say 'I Love You'
Be kind
Encourage each other
Question: What is an example of a computational social science NLP task?
Evidence: I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period. The embedded topics (gray) allow us to interpret the space. The George W. Bush addresses are embedded near a “war on terror” topic (“weapons, war...”), and the Barack Obama addresses are embedded near a “stimulus” topic (“people, work...”).
I also performed several case studies. I obtained document embeddings, in the same latent space as the topic embeddings, by summing the posterior mean vectors INLINEFORM0 for each token, and visualized them in two dimensions using INLINEFORM1 -SNE BIBREF24 (all vectors were normalized to unit length). The state of the Union addresses (Figure FIGREF27 ) are embedded almost linearly by year, with a major jump around the New Deal (1930s), and are well separated by party at any given time period. 
Answer: Visualization of State of the union addresses
Question: What does the "E" stand for in Chuck E. Cheese?
Answer: The "E" stands for "Entertainment"
Question: What does their analysis reveal?
Evidence: Deep learning systems have shown a lot of promise for extractive Question Answering (QA), with performance comparable to humans when large scale data is available. However, practitioners looking to build QA systems for specific applications may not have the resources to collect tens of thousands of questions on corpora of their choice. At the same time, state-of-the-art machine reading systems do not lend well to low-resource QA settings where the number of labeled question-answer pairs are limited (c.f. Table 2 ). Semi-supervised QA methods like BIBREF0 aim to improve this performance by leveraging unlabeled data which is easier to collect.
In this work, we present a semi-supervised QA system which requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents. Our proposed system consists of three stages. First, we construct cloze-style questions (predicting missing spans of text) from the unlabeled corpus; next, we use the generated clozes to pre-train a powerful neural network model for extractive QA BIBREF1 , BIBREF2 ; and finally, we fine-tune the model on the small set of provided QA pairs.
Our cloze construction process builds on a typical writing phenomenon and document structure: an introduction precedes and summarizes the main body of the article. Many large corpora follow such a structure, including Wikipedia, academic papers, and news articles. We hypothesize that we can benefit from the un-annotated corpora to better answer various questions – at least ones that are lexically similar to the content in base documents and directly require factual information.
We apply the proposed system on three datasets from different domains – SQuAD BIBREF3 , TriviaQA-Web BIBREF4 and the BioASQ challenge BIBREF5 . We observe significant improvements in a low-resource setting across all three datasets. For SQuAD and TriviaQA, we attain an F1 score of more than 50% by merely using 1% of the training data. Our system outperforms the approaches for semi-supervised QA presented in BIBREF0 , and a baseline which uses the same unlabeled data but with a language modeling objective for pretraining. In the BioASQ challenge, we outperform the best performing system from previous year's challenge, improving over a baseline which does transfer learning from the SQuAD dataset. Our analysis reveals that questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes.
Our analysis reveals that questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes.
Answer: Questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes.
Question: Which English domains do they evaluate on?
Evidence: FLOAT SELECTED: Table 2.3: Labelled attachment scores achieved by the MST, Malt, and Mate parsers trained on the Conll training set and tested on different domains.
We further evaluate our approach on our main evaluation corpus. The method is tested on both in-domain and out-of-domain parsing. Our DLM-based approach achieved large improvement on all five domains evaluated (Conll, Weblogs, Newsgroups, Reviews, Answers). We achieved the labelled and unlabelled improvements of up to 0.91% and 0.82% on Newsgroups domain. On average we achieved 0.6% gains for both labelled and unlabelled scores on four out-of-domain test sets. We also improved the in-domain accuracy by 0.36% (LAS) and 0.4% (UAS).
FLOAT SELECTED: Table 2.3: Labelled attachment scores achieved by the MST, Malt, and Mate parsers trained on the Conll training set and tested on different domains.
We further evaluate our approach on our main evaluation corpus. The method is tested on both in-domain and out-of-domain parsing. Our DLM-based approach achieved large improvement on all five domains evaluated (Conll, Weblogs, Newsgroups, Reviews, Answers). 
Answer: The answers are shown as follows:
* Conll, Weblogs, Newsgroups, Reviews, Answers
Question: How much is the BLEU score?
Evidence: FLOAT SELECTED: Table 1: BLEU scores and computation times with varyingK and sequence length compared to baseline models with and without attention.
FLOAT SELECTED: Table 1: BLEU scores and computation times with varyingK and sequence length compared to baseline models with and without attention.
Answer: Ranges from 44.22 to 100.00 depending on K and the sequence length.
Question: List all the years in order of appearance, separated by commas.
Evidence: Grosvenor Museum is a museum in Chester, Cheshire, in the United Kingdom. It is recorded in the National Heritage List for England as a designated Grade II listed building. Its full title is The Grosvenor Museum of Natural History and Archaeology, with Schools of Science and Art, for Chester, Cheshire and North Wales. It takes its name from the family name of the Dukes of Westminster, who are major landowners in Cheshire. The museum opened in 1886, it was extended in 1894, and major refurbishments took place between 1989 and 1999. Its contents include archaeological items from the Roman period, paintings, musical instruments, and a room arranged as a Victorian parlour.
Answer: 1886, 1894, 1989, 1999
Question: What are five totally distinct, creative ways to describe the usefulness of the police?
Answer: 1. The police serve as a "watchful guardian" to keep us safe.
2. The police are a "protector of the law" who protects us from harm.
3. The police are an "impartial enforcer" of the law.
4. The police are an "intermediary of justice" who helps us get justice.
5. The police are a "counselor of the law" who helps us understand the law.
Question: Why do humans like dogs?
Answer: Humans like dogs because they have evolved together over at least 18,000 years. During this time, dogs have learned to identify and understand complex human expressions, including facial cues and vocal language and pitch. In turn, dog physiology and cognitive development has evolved the facial muscle sets and vocalizations and cognitive communication skills which enable them to communicate their states of emotional and physical being.
Question: From the passage note down the various celebrities who joined TikTok . List the results in comma separated format.
Evidence: ByteDance planned on Douyin expanding overseas. The founder of ByteDance, Zhang Yiming, stated that "China is home to only one-fifth of Internet users globally. If we don't expand on a global scale, we are bound to lose to peers eyeing the four-fifths. So, going global is a must."

The app was launched as TikTok in the international market in September 2017. On 23 January 2018, the TikTok app ranked first among free application downloads on app stores in Thailand and other countries.

TikTok has been downloaded more than 130 million times in the United States and has reached 2 billion downloads worldwide, according to data from mobile research firm Sensor Tower (those numbers exclude Android users in China).

In the United States, celebrities, including Jimmy Fallon and Tony Hawk, began using the app in 2018. Other celebrities, including Jennifer Lopez, Jessica Alba, Will Smith, and Justin Bieber joined TikTok as well as many others.

In January 2019, TikTok allowed creators to embed merchandise sale links into their videos.

On 3 September 2019, TikTok and the U.S. National Football League (NFL) announced a multi-year partnership. The agreement occurred just two days before the NFL's 100th season kick-off at Soldier Field, where TikTok hosted activities for fans in honor of the deal. The partnership entails the launch of an official NFL TikTok account, which is to bring about new marketing opportunities such as sponsored videos and hashtag challenges. In July 2020, TikTok, excluding Douyin, reported close to 800 million monthly active users worldwide after less than four years of existence.
Answer: Jimmy Fallon, Tony Hawk, Jennifer Lopez, Jessica Alba, Will Smith, Justin Bieber
Question: What is brackish water and where does it occur?
Answer: Brackish water is water which is a mixture of freshwater and seawater, and is often found in bays or estuaries
Question: What is the role of the coxswain?
Evidence: The coxswain (or simply the cox) is the member who steers the boat using rudder strings, and coordinates the power and rhythm of the rowers, by communicating to the crew, often through a device called a cox box and speakers. The cox usually sits in the stern of the boat facing the rowers but in bowloaders, usually seen in the coxed four and coxed pair types of boat, the coxswain lies in the bow.
Answer: A coxswain sits in the stern of a rowing boat. A coxswain steers using rudder strings and coordinates the rowers through speakers located throughout the boat.