DataHammer/scimrc huggingface dataset:
Question: What languages are used in the datasets created by the IberEval?
Evidence: In the context of the IberEval shared tasks, two related multilingual datasets have been created BIBREF2, BIBREF3. Both are a collection of annotated Spanish and Catalan tweets. Crucially, the tweets in both languages focus on the same issue (Catalan independence); given this fact they are the first truly multilingual stance detection datasets known to us.
With regard to the languages covered by x-stance, only monolingual datasets seem to be available. For French, a collection of tweets on French presidential candidates has been annotated with stance BIBREF6. Similarly, two datasets of Italian tweets on the occasion of the 2016 constitutional referendum have been created BIBREF7, BIBREF6. For German, there is no known stance detection dataset, but an aspect-based sentiment dataset has been created for a GermEval 2017 shared task BIBREF8.
The SemEval-2016 task on detecting stance in tweets BIBREF9 offers data concerning multiple targets (Atheism, Climate Change, Feminism, Hillary Clinton, and Abortion). In the supervised subtask A, participants tended to develop a target-specific model for each of those targets. In subtask B cross-target transfer to the target “Donald Trump” was tested, for which no annotated training data were provided. While this required the development of more universal models, their performance was generally much lower.
BIBREF4 introduced a multi-target stance dataset which provides two targets per instance. For example, a model designed in this framework is supposed to simultaneously classify a tweet with regard to Clinton and with regard to Trump. While in theory the framework allows for more than two targets, it is still restricted to a finite and clearly defined set of targets. It focuses on modeling the dependencies of multiple targets within the same text sample, while our approach focuses on learning stance detection from many samples with many different targets.
In a target-specific setting, BIBREF10 perform a systematic evaluation of stance detection approaches. They also evaluate Bert BIBREF5 and find that it consistently outperforms previous approaches.
However, they only experimented with a single-segment encoding of the input, preventing cross-target transfer of the model. BIBREF11 propose a conditional encoding approach to encode both the target and the tweet as sequences. They use a bidirectional LSTM to condition the encoding of the tweets on the encoding of the target, and then apply a nonlinear projection on the conditionally encoded tweet. This allows them to train a model that can generalize to previously unseen targets.
In the context of the IberEval shared tasks, two related multilingual datasets have been created (Taulé et al., 2017, 2018). Both are a collection of annotated Spanish and Catalan tweets.
Answer: Spanish and Catalan.
Question: What fruits and vegetables are grown in lower-lying areas of Kenya?
Evidence: Tea, coffee, sisal, pyrethrum, corn, and wheat are grown in the fertile highlands, one of the most successful agricultural production regions in Africa. Livestock predominates in the semi-arid savanna to the north and east. Coconuts, pineapples, cashew nuts, cotton, sugarcane, sisal, and corn are grown in the lower-lying areas. Kenya has not attained the level of investment and efficiency in agriculture that can guarantee food security, and coupled with resulting poverty (53% of the population lives below the poverty line), a significant portion of the population regularly starves and is heavily dependent on food aid. Poor roads, an inadequate railway network, under-used water transport, and expensive air transport have isolated mostly arid and semi-arid areas, and farmers in other regions often leave food to rot in the fields because they cannot access markets. This was last seen in August and September 2011, prompting the Kenyans for Kenya initiative by the Red Cross.
Answer: Coconuts, pineapples and corn are grown in lower-lying areas of Kenya.
Question: Name five MLB teams in the National League.
Answer: Five MLB teams in the National League are the Atlanta Braves, Chicago Cubs, San Diego Padres, Philadelphia Phillies, and Milwaukee Brewers.
Question: When did the new building open?
Evidence: The new building opened on November 13, 2001, with a dedication ceremony held on November 17. The opening of the Fairview branch marked the first of many mixed use buildings in the county system. The library, with a floor area of 4,000 square feet (370 m2), is on the ground floor, and four apartments are on the upper floor.
Answer: The new building opened on November 13, 2001.
Question: Why do people like to travel?
Answer: Not all people like to travel, but many do.  Traveling to new places allows people to see parts of the world they've never seen before, try new food, and experience cultures that are new to them.  Travel is also a good way to get away from the day-to-day habits that can become boring or cumbersome over time.  People who do not like travel often say it's because travel is stressful and requires a lot of planning.
Question: What taxonomy does the author present?
Evidence: The Flickr30K dataset BIBREF0 is a collection of over 30,000 images with 5 crowdsourced descriptions each. It is commonly used to train and evaluate neural network models that generate image descriptions (e.g. BIBREF2 ). An untested assumption behind the dataset is that the descriptions are based on the images, and nothing else. Here are the authors (about the Flickr8K dataset, a subset of Flickr30K):
“By asking people to describe the people, objects, scenes and activities that are shown in a picture without giving them any further information about the context in which the picture was taken, we were able to obtain conceptual descriptions that focus only on the information that can be obtained from the image alone.” BIBREF1 
What this assumption overlooks is the amount of interpretation or recontextualization carried out by the annotators. Let us take a concrete example. Figure FIGREF1 shows an image from the Flickr30K dataset.
This image comes with the five descriptions below. All but the first one contain information that cannot come from the image alone. Relevant parts are highlighted in bold:
We need to understand that the descriptions in the Flickr30K dataset are subjective descriptions of events. This can be a good thing: the descriptions tell us what are the salient parts of each image to the average human annotator. So the two humans in Figure FIGREF1 are relevant, but the two soap dispensers are not. But subjectivity can also result in stereotypical descriptions, in this case suggesting that the male is more likely to be the manager, and the female is more likely to be the subordinate. rashtchian2010collecting do note that some descriptions are speculative in nature, which they say hurts the accuracy and the consistency of the descriptions. But the problem is not with the lack of consistency here. Quite the contrary: the problem is that stereotypes may be pervasive enough for the data to be consistently biased. And so language models trained on this data may propagate harmful stereotypes, such as the idea that women are less suited for leadership positions.
This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices. I will build on earlier work on linguistic bias in general BIBREF3 , providing examples from the Flickr30K data, and present a taxonomy of unwarranted inferences. Finally, I will discuss several methods to analyze the data in order to detect biases.
I will build on earlier work on linguistic bias in general (Beukeboom, 2014), providing examples from the Flickr30K data, and present a taxonomy of unwarranted inferences. Finally, I will discuss several methods to analyze the data in order to detect biases.
Answer: A taxonomy of unwarranted inferences.
Question: What are places that should be on everyone's bucket list?
Answer: Everyone's bucket list should include Acadia National Park in Maine, Arches National Park in Utah, Venice (Italy), Petra (Jordan), Santorini (Greece), Rothenburn ob der Tauber (Germany), New York City, Sydney (Australia), Hong Kong, Califoria's Big Sur coastline, London, Paris, Rome, Florence and the Amalfi Coast.
Question: List the people who voiced Yogi Bear in the 1980s.
Evidence: From the time of the character's debut until 1988, Yogi was voiced by voice actor Daws Butler. Butler died in 1988; his last performance as Yogi was in the television film Yogi and the Invasion of the Space Bears.

In 1983, a Yogi Bear balloon made its first appearance in the Macy's Thanksgiving Day Parade, becoming the final balloon that year. That same year, he appeared on a float named A Hanna-Barbera Christmas alongside many other Hanna-Barbera characters, as they cleaned up the streets of Broadway. The performance was bookended with animated segments featuring Yogi and Boo-Boo, voiced by Mel Blanc and Butler, respectively.

After Butler's death in 1988, Greg Burson stepped in to perform the role; Butler had taught Burson personally how to voice Yogi as well as his other characters. Worsening alcoholism and a legal incident led to Burson's firing in 2004 and eventually his death in 2008.

Yogi's current voice actor is Jeff Bergman. Bergman and Billy West also performed the character throughout the 1990s and early to mid-2000s for various Cartoon Network and Boomerang commercials and bumpers.
Answer: Daws Butler voiced Yogi Bear until 1988. Greg Burson took over for the rest of the 1980s.
Question: Who are the little Japanese girls who find Totoro?
Evidence: In 1950s Japan, university professor Tatsuo Kusakabe and his daughters Satsuki and Mei (approximately ten and four years old, respectively) move into an old house close to the hospital where the girls' mother Yasuko is recovering from a long-term illness. The house is inhabited by small, dark, dust-like house spirits called susuwatari, which can be seen when moving from bright places to dark ones.[note 1] When the girls become comfortable in their new house, the susuwatari leave to find another empty house. Mei later discovers two small spirits who lead her into the hollow of a large camphor tree. She befriends a larger spirit, which identifies itself using a series of roars she interprets as "Totoro". Mei thinks Totoro is the troll from her illustrated book Three Billy Goats Gruff, with her mispronouncing troll. Mei falls asleep atop Totoro but when Satsuki finds her, she is on the ground. Despite many attempts, Mei cannot show her family Totoro's tree. Tatsuo comforts her by telling her Totoro will reveal himself when he wants to.
Answer: Mei and Satsuki are the two Japanese girls who discover Totoro in the famous Japanese anime My Neighbour Totoro.
Question: What are some good mystery/suspense novels?
Answer: Hardy Boys 
Agatha Christie
Sherlock Holmes
Tintin
Nancy Drew
Famous Five
Question: find the most famous historical places in Egypt
Evidence: Egypt has one of the longest histories of any country, tracing its heritage along the Nile Delta back to the 6th–4th millennia BCE. Considered a cradle of civilisation, Ancient Egypt saw some of the earliest developments of writing, agriculture, urbanisation, organised religion and central government. Iconic monuments such as the Giza Necropolis and its Great Sphinx, as well the ruins of Memphis, Thebes, Karnak, and the Valley of the Kings, reflect this legacy and remain a significant focus of scientific and popular interest. Egypt's long and rich cultural heritage is an integral part of its national identity, which reflects its unique transcontinental location being simultaneously Mediterranean, Middle Eastern and North African. Egypt was an early and important centre of Christianity, but was largely Islamised in the seventh century and remains a predominantly Sunni Muslim country, albeit with a significant Christian minority, along with other lesser practiced faiths.
Answer: the Giza Necropolis and its Great Sphinx, as well the ruins of Memphis, Thebes, Karnak, and the Valley of the Kings, reflect this legacy and remain a significant focus of scientific and popular interest
Question: Why Rock Climbing so popular? Is rock climbing a good sport?
Answer: Rock Climbing became mainstream in the last decade after the release of the movie Free Solo and Dawn Wall. It is a fun sport, where you team up with a partner who will belay you as you climb up. Rock climbing is a physically and mentally demanding sport, one that often tests a climber's strength, endurance, agility, and balance along with his or her mental control. It is a great sport to get in shape as well as to bui
Question: What math classes are offered at high schools in the United States?
Answer: Math classes that are offered at high schools within the United States are Algebra 1, Algebra 2, Geometry, Statistics, Pre-Calculus, and Calculus.
Question: Given a reference text about Tujhse He Raabta, tell me who played the main role in the film.
Evidence: Tujhse He Raabta is a 2015 Pakistani television film directed by Nivdeita Basu and written by Moomal Shunaid. This television film was produced by Saurabh Pandey and co-produced by Yasir Shah under the banner of White Light Army Pictures. It was first aired on Geo Television on 15 February 2015 at 7:00 p.m. in Pakistan.

It features Sara Khan as the lead role in this television film. She rose to fame with her appearance in the Indian soap “Bidaai” while her co-lead, Yasir Shah, is a Pakistani actor, producer, screenwriter and model who started his acting career in India. The special feature of this telefilm is the high-red production camera due to which it has feature film quality.
Answer: Sara Khan played the lead role in the film Tujhse He Raabta.
Question: How to remove grease on clothes after a bike trip?
Answer: One of the ways to remove grease marks on your clothes is to use a combination of dishwashing liquid, sodium carbonate, and hot water and to rub it on the marks using a toothbrush for example. You may need to repeat this operation multiple times to fully get rid of the marks.
Question: Give me an overview of the different types of SOC reports and what they are used for
Evidence: System and Organization Controls (SOC), (also sometimes referred to as service organizations controls) as defined by the American Institute of Certified Public Accountants (AICPA), is the name of a suite of reports produced during an audit. It is intended for use by service organizations (organizations that provide information systems as a service to other organizations) to issue validated reports of internal controls over those information systems to the users of those services. The reports focus on controls grouped into five categories called Trust Service Principles. The AICPA auditing standard Statement on Standards for Attestation Engagements no. 18 (SSAE 18), section 320, "Reporting on an Examination of Controls at a Service Organization Relevant to User Entities' Internal Control Over Financial Reporting", defines two levels of reporting, type 1 and type 2. Additional AICPA guidance materials specify three types of reporting: SOC 1, SOC 2, and SOC 3.

Trust Service Principles
SOC 2 reports focus on controls addressed by five semi-overlapping categories called Trust Service Principles which also support the CIA triad of information security:

Security
Firewalls
Intrusion detection
Multi-factor authentication
Availability
Performance monitoring
Disaster recovery
Incident handling
Confidentiality
Encryption
Access controls
Firewalls
Processing Integrity
Quality assurance
Process monitoring
Adherence to principle
Privacy
Access control
Multi-factor authentication
Encryption
Reporting
Levels
There are two levels of SOC reports which are also specified by SSAE 18:

Type I, which describes a service organization's systems and whether the design of specified controls meet the relevant trust principles. (Are the design and documentation likely to accomplish the goals defined in the report?)
Type II, which also addresses the operational effectiveness of the specified controls over a period of time (usually 9 to 12 months). (Is the implementation appropriate?)
Types
There are three types of SOC reports.

SOC 1 – Internal Control over Financial Reporting (ICFR)
SOC 2 – Trust Services Criteria
SOC 3 – Trust Services Criteria for General Use Report
Additionally, there are specialized SOC reports for Cybersecurity and Supply Chain.

SOC 1 and SOC 2 reports are intended for a limited audience – specifically, users with an adequate understanding of the system in question. SOC 3 reports contain less specific information and can be distributed to the general public.
Answer: The different types of SOC reports are SOC 1, SOC 2, and SOC 3. Each report is used for a third party organization to understand the controls of another organization. SOC 1 reports cover controls over financial reporting, SOC 2 reports cover the trust services criteria (security, availability, confidentiality, processing integrity, and privacy), and SOC 3 reports are  general use SOC 2 reports.
Question: Tell me which of these are titles from William Gibson: The Peripheral, Do Androids Dream of Electric Sheep?, Foundation, Neuromancer, Mona Lisa Overdrive, Count Zero, Pattern Recognition, The Last Question, Jennifer Government
Answer: The Peripheral, Neuromancer, Mona Lisa Overdrive, Count Zero, Pattern Recognition
Question: Who were the ministers of the cabinet of Tatarascu's government?
Evidence: Ministers
The ministers of the cabinet were as follows:

President of the Council of Ministers:
Gheorghe Tătărăscu (11 May - 3 July 1940)
Minister of the Interior:
Mihail Ghelmegeanu (11 May - 3 July 1940)
Minister of Foreign Affairs:
Grigore Gafencu (11 May - 1 June 1940)
Ion Gigurtu (1 - 28 June 1940)
Constantin Argetoianu (28 June - 3 July 1940)
Minister of Finance:
Mitiță Constantinescu (11 May - 3 July 1940)
Minister of Justice:
Aurelian Bentoiu (11 May - 3 July 1940)
Minister of National Defence:
Gen. Ioan Ilcuș (11 May - 3 July 1940)
Minister of Air and Marine:
(interim) Gen. Ioan Ilcuș (11 May - 3 July 1940)
Minister of Materiel:
Victor Slăvescu (11 May - 3 July 1940)
Minister of National Economy:
Mircea Cancicov (11 May - 3 July 1940)
Minister of Agriculture and Property
Gheorghe Ionescu-Sisești (11 May - 3 July 1940)
Minister of Public Works and Communications:
Ion Gigurtu (11 May - 1 June 1940)
Ion Macovei (1 June - 3 July 1940)
Minister of Foreign Trade:
Ion Christu (11 May - 28 June 1940)
(interim) Mircea Cancicov (28 June - 3 July 1940)
Minister of National Education:
Petre Andrei (11 May - 3 July 1940)
Minister of Religious Affairs and the Arts:
Ștefan Ciobanu (11 May - 28 June 1940)
Constantin C. Giurescu (28 June - 3 July 1940)
Minister of Labour:
Mihail Ralea (11 May - 3 July 1940)
Minister of Health and Social Security
Nicolae Hortolomei (11 May - 3 July 1940)
Minister of Public Wealth:
Traian Pop (11 May - 3 July 1940)
Minister of Propaganda:
Constantin C. Giurescu (11 May - 28 June 1940)
Teofil Sidorovici (28 June - 3 July 1940)
Minister of State for Minorities:
Silviu Dragomir (11 May - 3 July 1940)
Minister Secretary of State of the Presidency of the Council of Ministers:
Radu Portocală (11 May - 28 June 1940)
Minister Secretary of State:
Ernest Urdăreanu (11 May - 28 June 1940)
Alexandru Vaida-Voevod (28 June - 3 July 1940)
Ion Inculeț (28 June - 3 July 1940)
Ion Nistor (28 June - 3 July 1940)
References
Answer: The cabinet members were Gheorghe Tătărăscu, Mihail Ghelmegeanu, Grigore Gafencu, Ion Gigurtu, Constantin Argetoianu, Mitiță Constantinescu, Aurelian Bentoiu, Gen. Ioan Ilcus, Victor Slăvescu, Mircea Cancicov, Gheorghe Ionescu-Sisești, Ion Gigurtu, Ion Macovei, Ion Christu, Mircea Cancicov, Petre Andrei, Ștefan Ciobanu, Constantin C. Giurescu, Mihail Ralea, Nicolae Hortolomei, Traian Pop, Constantin C. Giurescu, Teofil Sidorovici, Silviu Dragomir, Radu Portocală, Ernest Urdăreanu, Alexandru Vaida-Voevod, Ion Inculeț, and Ion Nistor.
Question: The initial probabilities were obtained from?
Evidence: Furthermore, let us recall that under i.i.d assumption we obtain from the definition of likelihood and following the negative likelihood coupled with the knowledge that the log-likelihood of yi is the log of a particular entry of yˆi.
Answer: We apologize for the somewhat misleading formulation. We assume throughout the paper our queries being true and following the i.i.d assumption.
Question: Who is Bryan Adams
Evidence: Bryan Guy Adams OC OBC FRPS (born November 5, 1959) is a Canadian musician, singer, songwriter, composer, and photographer. He has been cited as one of the best-selling music artists of all time, and is estimated to have sold between 75 million and more than 100 million records and singles worldwide. Adams was the most played artist on Canadian radio in the 2010s and has had 25 top-15 singles in Canada and a dozen or more in each of the US, UK, and Australia.
Answer: Bryan Guy Adams was born on November 5, 1959 and is a Canadian musician, singer, songwriter, composer, and photographer. He has been cited as one of the best-selling music artists of all time, and is estimated to have sold between 75 million and more than 100 million records and singles worldwide.

Adams was the most played artist on Canadian radio in the 2010s and has had 25 top-15 singles in Canada and a dozen or more in each of the US, UK, and Australia.
Question: What are some of the must-visit places in Tohoku, Japan?
Answer: Some of the must-visit places in Tohoku, Japan's Northeast region, include the region’s two most popular destinations: the beautiful and historic city of Sendai and the serene and beautiful Matsushima Bay. Additionally, the Tohoku region is home to some of the most popular destinations in Japan, including the Oirase gorge in southern Aomori prefecture, which is famed for its many waterfalls and hiking trails, and Akita city, which is best known for preserving its unique arts, architecture and cultural heritage from an earlier age. In terms of summer celebrations, the Kanto festival in Akita together with Aomori's Nebuta Festival and Sendai's Tanabata festival make up the three great festivals of the Tohoku Region. Tohoku is also home to some of the best onsen (natural hot spring) destinations in all of Japan such as Ginzan onsen in Yamagata prefecture, Akiu onsen in Miyagi, and Nyuto onsen in Akita.
Question: How did they do in NICE (Dinh et al., 2014) on flow-based generative models?
Evidence: Our approach is related to flow-based generative models, which are first described in NICE BIBREF16 and have recently received more attention BIBREF17 , BIBREF19 , BIBREF18 . This relevant work mostly adopts simple (e.g. Gaussian) and fixed priors and does not attempt to learn interpretable latent structures. Another related generative model class is variational auto-encoders (VAEs) BIBREF45 that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures BIBREF46 , BIBREF47 . Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges BIBREF48 . Our approach can also be viewed in connection with generative adversarial networks (GANs) BIBREF49 that is a likelihood-free framework to learn implicit generative models. However, it is non-trivial for a gradient-based method like GANs to propagate gradients through discrete structures.
Our approach is related to flow-based generative models, which are first described in NICE (Dinh et al., 2014) and have recently received more attention (Dinh et al., 2016; Jacobsen et al., 2018; Kingma and Dhariwal, 2018). This relevant work mostly adopts simple (e.g. Gaussian) and fixed priors and does not attempt to learn interpretable latent structures.
Answer: This relevant work mostly adopts simple (e.g. Gaussian) and fixed priors and does not attempt to learn interpretable latent structures.