DataHammer/scimrc huggingface dataset:
Question: Tell me if these are brands or companies: Kleenex, Procter & Gamble, Cadillac, Doritos, Chase?
Answer: It can be difficult to know if something is a brand or a company.  You asked about a few specific brands and companies.  Here is what is a brand and what is a company from the list that you gave me:

Kleenex is a brand.  The Kleenex brand is owned by the company, Kimberly-Clark

Procter & Gamble is a company.  Procter & Gamble owns many brands.  One famous brand of Procter & Gamble is Crest.

Cadillac is a brand.  The Cadillac brand is owned by General Motors.

Doritos is a brand.  Doritos is one of the brands of Frito-Lay, a wholly owned subsidiary of PepsiCo.  

Chase is a company.  To be very specific, JPMorgan Chase Bank, N.A., is a company  doing business as Chase. 

I hope this list helps you!
Question: What is the architecture of their model?
Evidence: Hence, the contributions of this work are two-fold: (1) we develop a linguistically-infused neural network model to classify reactions in social media posts, and (2) we apply our model to label 10.8M Twitter posts and 6.2M Reddit comments in order to evaluate the speed and type of user reactions to various news sources.
We develop a neural network architecture that relies on content and other linguistic signals extracted from reactions and parent posts, and takes advantage of a “late fusion” approach previously used effectively in vision tasks BIBREF13 , BIBREF14 . More specifically, we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.
Hence, the contributions of this work are two-fold: (1) we develop a linguistically-infused neural network model to classify reactions in social media posts, and (2) we apply our model to label 10.8M Twitter posts and 6.2M Reddit comments in order to evaluate the speed and type of user reactions to various news sources.
We develop a neural network architecture that relies on content and other linguistic signals extracted from reactions and parent posts, and takes advantage of a “late fusion” approach previously used effectively in vision tasks BIBREF13 , BIBREF14 . More specifically, we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.
Answer: The answers are shown as follows:
* we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.
Question: How is the entity recall expert trained?
Evidence: Following the data filtering and RL training steps described in §3.2 and §3.3, we train both referenceand model-based experts for intrinsic and extrinsic hallucination using DAE accuracy and entity overlap precision metrics as rewards, respectively. Also, because experts for both intrinsic and extrinsic hallucinations are trained to improve precision with respect to the source document, they may negatively impact the content recall. So, we train entity-recall experts to maximize recall of salient entities between the generated summary and the reference summary. We train reference-based experts for DAE metric on data filtered for intrinsic hallucination, and for entity overlap precision and entity recall metric on data filtered for extrinsic hallucination. The model-based experts for both metrics are trained on the entire training dataset.
Next, for each of the three kinds of experts, we select one of the reference-and model-based experts having least factual error (or maximum factual recall) on validation dataset and combine them through weights or logits ensembling. We use the element-wise weighted average of all the parameters of pre-trained summarization model (φ) and expert models (θ i expert ) for weights ensembling (eq. 3). For logits ensembling, we use the weighted average of logits (z t ) from all the experts (z t,θ i expert ) and the pre-trained model (z t,φ ) during decoding, as described by the eq. 4.
So, we train entity-recall expert to maximize recall of salient entities between the generated summary and the reference summary.
Answer: It is trained to maximize entity recall (Eq 1) where recall is defined with respect to the human written reference summary.
Question: How does Walter White die in Breaking bad
Answer: In the final episode of Breaking Bad, Walter attempts to clean-up his mess by killing off all those who betrayed him, and rescue Jesse from his captors.  Walter builds a moving arm mount for an M60 machine gun that he can activate from the car's remote key fob, and places it in the trunk of a car that he got from the same gun dealer.  Walter then drives to the compound where the Aryan Brotherhood are living, under a trick that he can help them make “meth” with a new recipe.

Once inside, Walter Argues with Jack about why Jessie isn’t dead yet, causing the angry Jack to bring Jessie into the room to prove they are not friends.  AT this point, Walter charges Jessie to the floor and presses the key fob, causing the M60 to begin shooting into the building.

Everyone in the building is shot dead except for Jessie, Walter, Todd and Jack.  Jesse strangles Todd to death with his handcuffs, and Walter Shoots Jack with his own gun.  Jessie then leaves the compound in one of the cars that is sitting outside.

After this, you become aware that Walter was hit by a ricochet while the M60 was firing, and has effectively been shot in the stomach.  He walks over to the building where Jessie was making Meth, and falls to the floor seconds before the Police enter the building.
Question: Which test set is the most hardest phenomenon to capture using round-trip translation?
Evidence: Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Answer: VP ellipsis.
Question: Tell me about the 1999 UEFA champions league final?
Evidence: The 1999 UEFA Champions League final was an association football match between Manchester United of England and Bayern Munich of Germany, played at Camp Nou in Barcelona, Spain, on 26 May 1999, to determine the winner of the 1998–99 UEFA Champions League. Injury time goals from Manchester United's Teddy Sheringham and Ole Gunnar Solskjær cancelled out Mario Basler's early goal for Bayern to give Manchester United a 2–1 win. Referee Pierluigi Collina has cited this match as one of the most memorable of his career, and described the noise from the crowd at the end of the game as being like a "lion's roar".

The two sides had played each other earlier in the competition, having both been drawn in Group D in the group stage; Bayern won the group, while Manchester United qualified for the knockout phase as one of the two best runners-up across all six groups. After beating Inter Milan in the quarter-finals, Manchester United beat another Italian side, Juventus, in the semis to reach the final; meanwhile, Bayern beat fellow Germans Kaiserslautern in the quarter-finals, before overcoming Ukrainian side Dynamo Kyiv in the semis. The victory completed a treble-winning season for Manchester United, after they had won the Premier League and FA Cup. Bayern were also playing for a treble, having won the Bundesliga and reached the DFB-Pokal final, although they went on to lose that match as well.
Answer: The 1999 Champions league final was won by Manchester United in dramatic fashion when they scored two goals in injury time. Teddy Sheringham and Ole Solskjaer scored late on for United after they'd conceded a goal early in the first half. The victory gave manchester United the "treble" and is considered one of the greatest achievements in the history of club football.
Question: How many teams are in the MLB National League
Evidence: Major League Baseball (MLB) is a professional baseball organization and the oldest major professional sports league in the world.  MLB is composed of 30 teams, divided equally between the National League (NL) and the American League (AL), with 29 in the United States and 1 in Canada.
Answer: The National League (NL) consists of 15 teams aligned in three divisions. In the NL East are the Atlanta Braves, Miami Marlins, New York Mets, Philadelphia Phillies, and Washington (D.C.) Nationals. In the NL Central are the Chicago Cubs, Cincinnati Reds, Milwaukee Brewers, Pittsburgh Pirates, and St. Louis Cardinals.
Question: Please give me the keypoints about Mehmed II's reign
Evidence: Mehmed II (Ottoman Turkish: محمد ثانى, romanized: Meḥmed-i s̱ānī; Turkish: II. Mehmed, pronounced [icinˈdʒi ˈmehmed]; 30 March 1432 – 3 May 1481), commonly known as Mehmed the Conqueror (Ottoman Turkish: ابو الفتح, romanized: Ebū'l-fetḥ, lit. 'the Father of Conquest'; Turkish: Fâtih Sultan Mehmed), was an Ottoman sultan who ruled from August 1444 to September 1446, and then later from February 1451 to May 1481.

In Mehmed II's first reign, he defeated the crusade led by John Hunyadi after the Hungarian incursions into his country broke the conditions of the truce Peace of Szeged. When Mehmed II ascended the throne again in 1451, he strengthened the Ottoman navy and made preparations to attack Constantinople. At the age of 21, he conquered Constantinople and brought an end to the Byzantine Empire. After the conquest, Mehmed claimed the title Caesar of the Roman Empire (Ottoman Turkish: قیصر‎ روم, romanized: Qayser-i Rûm), based on the fact that Constantinople had been the seat and capital of the surviving Eastern Roman Empire since its consecration in 330 AD by Emperor Constantine I. The claim was only recognized by the Patriarchate of Constantinople. Nonetheless, Mehmed II viewed the Ottoman state as a continuation of the Roman Empire for the remainder of his life, seeing himself as "continuing" the Empire rather than "replacing" it.

Mehmed continued his conquests in Anatolia with its reunification and in Southeast Europe as far west as Bosnia. At home he made many political and social reforms, encouraged the arts and sciences, and by the end of his reign, his rebuilding program had changed Constantinople into a thriving imperial capital. He is considered a hero in modern-day Turkey and parts of the wider Muslim world. Among other things, Istanbul's Fatih district, Fatih Sultan Mehmet Bridge and Fatih Mosque are named after him.
Answer: Mehmed II also known as Mehmed the Conqueror was an Ottoman Sultan who reigned from 1432 to 1481. He is known for conquering Constantinople, ending the Byzantine Empire and greatly expanded the Ottoman empire.
Question: What does the Latin phrase mea culpa mean in English?
Answer: Mea culpa means “my fault” in English.
Question: Identify which instrument is string or percussion: Lambeg, Hurdy-gurdy
Answer: Hurdy-gurdy is string, Lambeg is percussion.
Question: Why everyone doesn't see solar eclipse when it occurs?
Answer: You have to be in the moon's darkest shadow (umbra). This place is small, so other areas around the world won't see it.
Question: From the passage provided, extract when Gandhi moved to South Africa
Evidence: Mohandas Karamchand Gandhi (/ˈɡɑːndi, ˈɡændi/ GA(H)N-dee, Gujarati: [ˈmoɦəndɑs ˈkəɾəmtʃənd ˈɡɑ̃dʱi]; 2 October 1869 – 30 January 1948) was an Indian lawyer, anti-colonial nationalist and political ethicist who employed nonviolent resistance to lead the successful campaign for India's independence from British rule. He inspired movements for civil rights and freedom across the world. The honorific Mahātmā (Sanskrit: "great-souled", "venerable"), first applied to him in 1914 in South Africa, is now used throughout the world.

Born and raised in a Hindu family in coastal Gujarat, Gandhi trained in the law at the Inner Temple, London, and was called to the bar at age 22 in June 1891. After two uncertain years in India, where he was unable to start a successful law practice, he moved to South Africa in 1893 to represent an Indian merchant in a lawsuit. He went on to live in South Africa for 21 years. It was here that Gandhi raised a family and first employed nonviolent resistance in a campaign for civil rights. In 1915, aged 45, he returned to India and soon set about organising peasants, farmers, and urban labourers to protest against excessive land-tax and discrimination.
Answer: Gandhi moved to South Africa in 1893
Question: From the text provided, extract the name of the most profitable public banking company in Sri Lanka. Include the profit and revenue in US dollars.
Evidence: This list is based on the Sri Lanka Parliament, which ranks the Sri Lanka's largest public companies.

Rank	Name	Financial statistics in millions US$	Industry
Revenue	Profit	Assets
1	Ceylon Petroleum Corporation	3,016	9.9	1,477	Oil and gas
2	Bank of Ceylon	1,316	413	13,290	Banking
3	Ceylon Electricity Board	1,268	-138.12	4,127	Electricity generation
4	SriLankan Airlines	1,017	-230.28	1,273	Airline
5	People's Bank (Sri Lanka)	1,013	70	10,320	Banking
6	National Savings Bank (Sri Lanka)	672.42	36.89	6,370	Banking
7	Sri Lanka Telecom	474.85	34.92	1,154	Telecommunications
8	Sri Lanka Ports Authority	248.25	5.77	1,988	Commercial ports
9	Sri Lanka Insurance	242.5	28.55	941.5	Insurance
Answer: The most profitable public bank in Sri Lanka is the Bank of Ceylon. The Bank of Ceylon has a recorded profit of $413 million dollars US with a revenue of $1,316 million dollars US. The Bank of Ceylon is the 2nd largest public company in Sri Lanka.
Question: Think up a bunch of different meals I can make that include potatoes.
Answer: Potatoes are a very versatile food that can be used in many meals. Some of my favorites are:
- Shepherd's Pie
- Loaded Baked Potatoes
- Poutine
- Potatoes au Gratin
Question: Understanding these limitations, what model does the author propose?
Evidence: In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models. The results show significant improvement with an average of 26.8 point increase in F-measure on our test data and 38.6 increase on the totally new dataset.
In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models
Answer: They present a new network based on a bidirectional GRU model.
Question: What was Goff's best game mentioned in the text?
Evidence: In the season-opener against the Philadelphia Eagles, Goff threw for 215 yards and two touchdowns, but threw an interception returned for a touchdown as the Lions narrowly lost by a score of 38–35. In the next game against the Washington Commanders, he threw for 256 yards and four touchdowns in a 36–27 victory. In the Week 2 game against Washington, Goff produced his sixth straight home game with two or more passing touchdowns, establishing a Lions franchise record. On October 2, Goff again threw four touchdowns passes, and this time added a tenure-high 378 passing yards during a 48–45 loss to the Seattle Seahawks. Goff finished his second season with the Lions with 4,438 yards, 29 touchdowns, and seven interceptions, starting in all 17 games. Goff was named to his third Pro Bowl as an alternative, replacing Super Bowl bound Jalen Hurts.
Answer: Goff's best game mentioned in the text was on October 2 when he threw four touchdowns passes, and added a tenure-high 378 passing yards during a 48–45 loss to the Seattle Seahawks.

Goff played another good game against the Washington Commanders, when he threw for 256 yards and four touchdowns in a 36–27 victory, but this performance wasn't as good as the game against the Seahawks because he threw for fewer yards even though he threw for the same amount of touchdowns.

Note that the text is also missing information that would be useful in judging Goff's performance such as QBR, completion percentage, etc.
Question: What two continents is Russia a part of?
Answer: Russia is a part of Europe and Asia
Question: What is the best Hip-Hop artist of all time ?
Answer: There are a variety of Hip-Hop artists that have been spanning through multiple years, hip-hop in the 80s is a lot different than the modern era hip-hop. The genre is constantly evolving and multiple sub-genres has emerged, like Lo-Fi, Trip-hop, Gangsta rap, ignorant rap etc...
It's also hard to measure what makes the success of an artist, is it the records sold ? the number of songs ? the activity of the fanbase ? the quality of the rhymes ? the impact his song had ? What the artist do outside of it's musical activity ? it's longevity ?
For example we can say that Run-DMC has been the first rap group to really push the genre forward as they were the first group to be on Mtv, having a sponsorship with Adidas, and mixing Rock with Rap in their Song with Aerosmith, but their longevity has been shortened due to the murdering of Jam Master Jay.
Tupac or Notorious Big are acknowledged by a lot has the best rappers of all time because of their rhyme scheme and the fact that their song remains classics today.
Question: Which company produces Hibiki Whisky?
Evidence: Hibiki (Japanese: 響) (meaning "resonance" or "echo") is a premium blended whisky produced in Japan by Beam Suntory (a subsidiary of Suntory Beverage & Food Ltd, which itself is a subsidiary of Suntory Holdings of Osaka, Japan). It has won several awards.
Answer: Hibiki Whisky is a premium blended whisky produced in Japan by Beam Suntory, a subsidiary of Suntory Beverage & Food Ltd / Suntory Holdings of Osaka, Japan.
Question: Is Star Wars based on a book?
Answer: No, the Star Wars universe originated from the film series created by George Lucas beginning with 'Star Wars: A New Hope' in 1977. A number of books have been written based on the films or in the storyline created from them, but the original material was a film.
Question: Brainstorm reasons for and against taking a daily low-dose aspirin.
Answer: According to Berkeley Wellness, there’s evidence that a daily aspirin “protects people already diagnosed with cardiovascular disease (CVD).” However, there is insufficient evidence that a daily low-dose aspirin is as effective for primary prevention; it is not as necessarily effective for people who have “not been diagnosed with CVD.” Additionally, high BMI may also alter the way aspirin is metabolized, further complicating the data. Finally, there’s evidence that suddenly stopping aspirin can actually increase the risk of a heart attack or stroke. Thus, not everyone should be taking a daily low-dose aspirin - consult your doctor!
Question: From the passage provided, what does Adam M. Grant do?
Evidence: Adam M. Grant (born August 13, 1981) is an American popular science author, and professor at the Wharton School of the University of Pennsylvania specializing in organizational psychology.
Answer: Adam M. Grant is an American author and professor at the Wharton School of the University of Pennsylvania who specializes in organizational psychology.