DataHammer/scimrc huggingface dataset:
Question: Given a reference text about Stevan L. Davies, tell me where he went to college.
Evidence: Stevan L. Davies (born 1948) is an author and Emeritus Professor of Religious Studies at Misericordia University in Dallas, Pennsylvania. His most notable work has been in the analysis of Christian apocrypha, especially the Gospel of Thomas. He is married to Sally Watkins Davies and has two children, Michael L. Davies and Meredith G. Russell.[citation needed]

Davies holds a bachelor's degree from Duke University and a doctorate in philosophy from Temple University.

He was Professor of Religious Studies at Misericordia University from 1979 to 2015.
Answer: Stevan L. Davies went to college at Duke University for his bachelor's degree and Temple University for his doctorate in philosophy.
Question: Are the academically younger authors cited less than older?
Evidence: FLOAT SELECTED: Figure 30 Aggregate citation statistics by academic age.
FLOAT SELECTED: Figure 30 Aggregate citation statistics by academic age.
Answer: Yes.
Question: What is genomics?
Answer: Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism. Genes may direct the production of proteins with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput DNA sequencing and bioinformatics to assemble and analyze the function and structure of entire genomes. Advances in genomics have triggered a revolution in discovery-based research and systems biology to facilitate understanding of even the most complex biological systems such as the brain.

The field also includes studies of intragenomic (within the genome) phenomena such as epistasis (effect of one gene on another), pleiotropy (one gene affecting more than one trait), heterosis (hybrid vigour), and other interactions between loci and alleles within the genome.
Question: Given this paragraph, what is Peru's most popular tourist attraction and how many people visit it every year?
Evidence: Machu Picchu is both a cultural and natural UNESCO World Heritage Site. Since its rediscovery in 1911, growing numbers of tourists have visited the site each year, with numbers exceeding 1.4 million in 2017. As Peru's most visited tourist attraction, and a major revenue generator, it is continually exposed to economic and commercial forces. In the late 1990s, the Peruvian government granted concessions to allow the construction of a cable car and a luxury hotel, including a tourist complex with boutiques and restaurants and a bridge to the site. Many people protested the plans, including Peruvians and foreign scientists, saying that more visitors would pose a physical burden on the ruins. In 2018, plans were restarted to again construct a cable car to encourage Peruvians to visit Machu Picchu and boost domestic tourism. A no-fly zone exists above the area. UNESCO is considering putting Machu Picchu on its List of World Heritage in Danger.
Answer: Machu Picchu is Peru's most visited tourist attraction, and a considerable number of people visit it every year. My data shows 1.4 million people visited Machu Picchu in the year 2017.
Question: What is the greatest movie of all time?
Answer: Determining the greatest movie of all time would depend on defining what is meant by "greatest". Opinions on what makes a movie "great" would be completely subjective and vary from person to person. Those opinions may be based on how funny, interesting or exciting a given movie is to a person, which in itself is also subject to individual preferences and personality. Some people might even base the criteria on achievements such as number of Oscars awards won, biggest box office record or popularity achieved. There's no way to answer this question in a definitive fashion.
Question: Do the jointly learning multiple relative tasks demonstrate significant performance gains for each task？
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Answer: Yes, they do.
Question: Give me a list of privacy centric browsers for Android users
Answer: 1. Brave: Main goal behind building Brave was to blocking ads and trackers by default. Brave rewards users with Basic Attention Token (BAT) which is based on Ethereum blockchain for surfing over the browser.
2. Bromite: This browser is based on Chromium open-source project. It is light-weight and fast. One needs to install it from APK file as it is not available over Google Play Store.
3. Firefox Focus: For all the things that you want to keep separate from the main browser. As it blocks all the ads and other things by default, it results in overall faster browsing and quicker page load times.
Question: Where is Independence mall?
Evidence: Independence Mall (formerly Westfield Shoppingtown Independence or Westfield Independence) is the only enclosed shopping mall in Wilmington, North Carolina. It opened in 1979 with anchor stores Belk-Beery (now Belk), JCPenney, and Sears. A later expansion added a new wing and Dillard's store to the center after Westfield Group purchased the mall. The mall is located on Oleander Drive (U.S. 76) and Independence Blvd east of downtown Wilmington. The mall has a food court and no longer features a carousel.
Answer: Independence mall is located in Wilmington, North Carolina.
Question: What are the implicit quality indicators in this paper?
Evidence: We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content. We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document. Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv. These results underline the feasibility of assessing document quality via visual features, and the complementarity of visual and textual document representations for quality assessment.
We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content.
Answer: Images and visual layout.
Question: What are the ten essentials? Give your answer in JSON, with the name and examples of each essential.
Evidence: The ninth edition of Mountaineering: The Freedom of the Hills, published in 2017, defines the Ten Essentials as:

Navigation: map, altimeter, compass, GPS device, personal locator beacon
Headlamp: with LED bulb and spare batteries
Sun protection: sunglasses, sun protective clothing, sunscreen
First aid: a first aid kit, wrapped in waterproof packaging
Knife: hikers on a short trip may also carry a multi-tool, strong adhesive tape and cordage; on a longer trip, further small tools may be useful
Fire: the means to both start and sustain a fire; either a butane lighter or matches, or other fire making device. Firestarters for igniting even wet wood, and in areas where no firewood will be available, a stove is highly advisable.
Shelter: plastic tube tent, jumbo plastic trash bag or bivy sack
Extra food: at least one day's food for a short hike, that should require no cooking.
Extra water: drinking water and the skills and tools to purify water
Extra clothes: additional items may be needed if spending the night in the emergency shelter

https://en.wikipedia.org/wiki/Ten_Essentials
Answer: [
  {"name": "Navigation", "examples":  ["map", "altimeter", "compass", "GPS device", "personal locator beacon"]},
  {"name": "Headlamp", "examples":  ["LED headlamp", "spare batteries"]},
  {"name": "Sun protection", "examples":  ["sunglasses", "sun protective clothing", "sunscreen"]},
  {"name": "First aid", "examples":  ["first aid kit"]},
  {"name": "Knife", "examples":  ["knife", "multi-tool", "tape", "cordage"]},
  {"name": "Fire", "examples":  ["butane lighter", "matches", "firestarters", "stove"]},
  {"name": "Shelter", "examples":  ["plastic tube tent", "jumbo plastic trash bag", "bivy sack"]},
  {"name": "Extra food", "examples":  ["at least one day's food"]},
  {"name": "Extra water", "examples":  ["drinking water", "skills and tools to purify water"]},
  {"name": "Extra clothes", "examples":  ["items to spend the night"]}
]
Question: Can you describe Gene expression?
Answer: Genes generally express their functional effect through the production of proteins, molecules responsible for most functions in the cell. Proteins are made up of one or more polypeptide chains, each composed of a sequence of amino acids. The DNA sequence of a gene is used to produce a specific amino acid sequence. This process begins with the production of an RNA molecule with a sequence matching the gene's DNA sequence, a process called transcription.

This messenger RNA molecule then serves to produce a corresponding amino acid sequence through a process called translation. Each group of three nucleotides in the sequence, called a codon, corresponds either to one of the twenty possible amino acids in a protein or an instruction to end the amino acid sequence; this correspondence is called the genetic code. The flow of information is unidirectional: information is transferred from nucleotide sequences into the amino acid sequence of proteins, but it never transfers from protein back into the sequence of DNA—a phenomenon Francis Crick called the central dogma of molecular biology.

The specific sequence of amino acids results in a unique three-dimensional structure for that protein, and the three-dimensional structures of proteins are related to their functions. Some are simple structural molecules, like the fibers formed by the protein collagen. Proteins can bind to other proteins and simple molecules, sometimes acting as enzymes by facilitating chemical reactions within the bound molecules (without changing the structure of the protein itself). Protein structure is dynamic; the protein hemoglobin bends into slightly different forms as it facilitates the capture, transport, and release of oxygen molecules within mammalian blood.
Question: Given the following text, what is Mýrdalsjökull, which country is it located in,  how much rain or snow does it receive each year?
Evidence: Mýrdalsjökull (pronounced [ˈmirˌtalsˌjœːkʏtl̥] (listen), Icelandic for "(the) mire dale glacier" or "(the) mire valley glacier") is an ice cap in the south of Iceland. It is to the north of Vík í Mýrdal and to the east of the smaller ice cap Eyjafjallajökull. Between these two glaciers is Fimmvörðuháls pass. Its peak reaches 1,493 m (4,898 ft) in height and in the year 1980 it covered an area of approximately 595 km2 (230 sq mi).
The icecap of the glacier covers an active volcano called Katla. The caldera of the volcano has a diameter of 10 km (6 mi) and the volcano erupts usually every 40–80 years. The last eruption took place in 1918. Scientists are actively monitoring the volcano, particularly after the eruption of nearby Eyjafjallajökull began in April 2010. Since the year 930, 16 eruptions have been documented.
The Eldgjá, a volcanic eruption fissure about 30 km (19 mi) long, which erupted in the year 936, is part of the same volcanic system.
Before the Hringvegur (the main ring road round the island) was built, people feared traversing the plains in front of the volcano because of the frequent jökulhlaups (glacial floods) and the deep rivers to be crossed, although the road is still vulnerable to major events. Especially dangerous was the glacial flood after the eruption of 1918 when the coastline was extended by 5 km (3.1 mi) by laharic flood deposits.
Mýrdalsjökull is an exceedingly wet location, with models suggesting it receives more than 10 metres of precipitation annually.
Answer: What: an ice cap
Country: Iceland 
rain or snow per year: 10 meters annually
Question: what semantically conditioned models did they compare with?
Evidence: For goal-oriented generation, many of the models evaluated using the Inform/Request metrics have made use of structured data to semantically condition the generation model in order to generate better responses BIBREF35, BIBREF1, BIBREF0. BIBREF35 proposed to encode each individual dialog act as a unique vector and use it as an extra input feature, in order to influence the generated response. This method has been shown to work well when tested on single domains where the label space is limited to a few dialog acts. However, as the label space grows, using a one-hot encoding representation of the dialog act is not scalable. To deal with this problem, BIBREF1 introduced a semantically conditioned generation model using Hierarchical Disentangled Self-Attention (HDSA) . This model deals with the large label space by representing dialog acts using a multi-layer hierarchical graph that merges cross-branch nodes. For example, the distinct trees for hotel-recommend-area and attraction-recommend-area can be merged at the second and third levels sharing semantic information about actions and slots but maintaining specific information about the domains separate. This information can then be used as part of an attention mechanism when generating a response. This model achieves the state-of-the-art result for generation in both BLEU and Inform/Request metrics.
To deal with this problem, BIBREF1 introduced a semantically conditioned generation model using Hierarchical Disentangled Self-Attention (HDSA) .
Answer: The answers are shown as follows:
* Hierarchical Disentangled Self-Attention
Question: Name some Ivy League schools
Answer: Some Ivy League schools are Cornell, Harvard, Yale, Dartmouth and Columbia
Question: What is the size of this dataset?
Evidence: The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). We first merge all the meta-data (all_sources_metadata_2020-03-13.csv) with their corresponding full-text papers. Then we create a tokenized corpus (CORD-19-corpus.json) for further NER annotations.
The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). We first merge all the meta-data (all_sources_metadata_2020-03-13.csv) with their corresponding full-text papers. Then we create a tokenized corpus (CORD-19-corpus.json) for further NER annotations.


Answer: The answers are shown as follows:
* 29,500 documents in the CORD-19 corpus (2020-03-13)
Question: What do you know about Apache Spark?
Evidence: Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since.
Answer: Apache Spark is an open-source analytics engine for large-scale data processing.
Question: What do I need to be aware of when talking to an alien?
Answer: You need to carefully consider your actions and presuppose a friendly exchange. Here are suggestions that may help:

1. Try to avoid using offensive behavior and remarks. Respect each other's culture, values and dignity.
2. Try not to mention any human weapons, technology, and information that can cause damage or confusions.
3. During the communication, make sure to confirm the other party's attitude towards humans. Whenever possible, choose aliens with a friendly attitude to communicate with. If you see hostility toward you, or even toward humanity, be vigilant and inform others if the situation is out of your control.

Always keep in mind that your actions may concern humans or represent others. Your communication with aliens requires compliance with the ethics and laws of both parties.
Question: Does MLE have any defects?
Evidence: Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.
Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent.
Answer: MLE is not good at creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged.
Question: Who is the greatest quarterback of all time?
Answer: Tom Brady is widely considered to be the greatest NFL quarterback of all time. He holds the all-time records for Passing Yards, Passing Touchdowns, Completions. 

Brady also has a record 7 Super Bowl championships, 3 more than any other quarterback and a record unlikely to be broken. He won 6 of these with the New England Patriots, with whom he spent the first 20 years of career. 

After early-career comparisons to his contemporary rival Peyton Manning, and mid-career comparisons to another all-time great Joe Montana, Brady ended his career as the indisputable Greatest of All Time (GOAT) by playing at an extremely high level for multiple decades.
Question: In what year did the Geneva Conference ratify the Vietnamese Communist Victory?
Answer: 1954 the Geneva Conference divided independent Vietnam into Communist North and pro_western South.
Question: What is the difference between meiosis and mitosis
Answer: Both, meiosis and mitosis, are kinds of cell division. Mitosis produces two genetically identical child cells from a single parent cell whereas meiosis produces cells that are genetically unique from the parent and contain only half as much DNA.
Question: Tell me whether these are metals or non-metals: Sodium, Helium, Aluminum, Chlorine, Gold
Answer: Metals: Sodium, Aluminium, Gold
Non-Metals: Chlorine, Helium