DataHammer/scimrc huggingface dataset:
Question: What's the game engine used by Bethesda before Creation Engine? And what's the last game make with it?
Evidence: After using the Gamebryo engine to create The Elder Scrolls III: Morrowind, The Elder Scrolls IV: Oblivion, and Fallout 3, Bethesda decided that Gamebryo's capabilities were becoming too outdated and began work on the Creation Engine for their next game, The Elder Scrolls V: Skyrim, by forking the codebase used for Fallout 3.

Following the completion of Skyrim, Bethesda set out to enhance the graphical core of the Creation Engine by first adding a physically based deferred renderer to allow for more dynamic lighting and to paint materials object surfaces with realistic materials. Bethesda worked with technology company Nvidia to implement volumetric lighting through a technique that makes use of hardware tesselation. Additionally the updated version of the Creation Engine powering Bethesda's Fallout 4 offers more advanced character generation.
Answer: Gamebryo was the engine used by Bethesda before Creation Engine. It is developed by Gamebase Cot., Ltd and Gamebase USA. Gamebryo is used by numerous companies within the gaming industry.

Bethesda decided that Gamebryo's capabilities were becoming too outdated and began work on the Creation Engine for their next game, The Elder Scrolls V: Skyrim, by forking the codebase used for Fallout 3.

The last game from Bethesda made with Gamebryo engine is Fallout 3 (2008).
Question: Name some famous books on philosophy?
Answer: Meditations by Marcus Aurelius.
Man's Search for Meaning by Viktor Frankl.
The Story of Philosophy by Will Durant.
The Republic by Plato.
Discourses by Epictetus.
Question: Are cats or dogs better?
Answer: The debate on if cats or dogs are better has been talked about for ages, there is no clear winner. Humans love being binary and needing to choose between one or the other. Each person is different, and can benefit from cats AND dogs in their life. You can like both cats and dogs, and like them equally as well.
Question: What are some types of physical exercise?
Answer: Exercise can encompass: cardiovascular (aka, cardio), to improve blood flow/circulation and overall heart and lung health
lifting, to build or tone muscles (arms, legs, shoulders benefit from lifting)
core, to improve balance, coordination, and stability
stretching, to improve flexibility and tone (yoga, martial arts, barre, pilates)
endurance (swimming, cycling, running distances)
Question: Write a poem about a little neurodiverse boy with amazing gifts.
Answer: I know a little boy called Shane
He has an amazing, unique brain
All the country flags of the world he knows, at a glance
Big numbers he adds and multiples, whenever he gets a chance
His curiosity is what fascinates me
And alas, his fantastic memory!
He sometimes struggles to make friends
And seldom cares about the latest fashion trends
He is a shining star, shining bright
He brings joy to everyone around him, and pure delight!
Question: What does the author use to solve the problems?
Evidence: Informal speech is different from formal speech, especially in Vietnamese due to many conjunctive words in this language. Building an ASR model to handle such kind of speech is particularly difficult due to the lack of training data and also cost for data collection. There are two components of an ASR system that contribute the most to the accuracy of it, an acoustic model and a language model. While collecting data for acoustic model is time-consuming and costly, language model data is much easier to collect.
The language model training for the Automatic Speech Recognition (ASR) system usually based on corpus crawled on formal text, so that some conjunctive words which often used in conversation will be missed out, leading to the system is getting biased to writing-style speech.
In this paper, we present our attempt to mitigate the problems using a large scale data set and a language model combination technique that only require a small amount of conversation data but can still handle very well conversation speech.
In this paper, we present our attempt to mitigate the problems using a large scale data set and a language model combination technique that only require a small amount of conversation data but can still handle very well conversation speech.
Answer: A large scale data set and a language model combination technique.
Question: What wine varietal is the most popular produced in Oregon?
Answer: Oregon is well-known for its Pinot Noir. Many of the most awarded Pinot Noirs come from Willamette Valley.
Question: What are the benefits of exercising and why one should workout everyday especially if you have a sedentary lifestyle that makes you sit all day long?
Answer: Based on various researches, it’s evident that health risks are at rise due to sedentary lifestyle. This has led to multiple health issues such as cardiovascular diseases, diabetes and obesity. By exercising, one can stay in shape and active especially if the workout is rigorous that makes your heart pump faster. Most doctors recommend to exercise at least half an hour everyday to stay fit.
Question: What was the relationship of the person who became MD at Grandmaster Capital Management to Paypal?
Evidence: Individuals whom the media refers to as members of the PayPal Mafia include:

Peter Thiel, PayPal founder and former chief executive officer who is sometimes referred to as the "don" of the PayPal Mafia
Max Levchin, founder and chief technology officer at PayPal
Elon Musk, co-founder of Zip2, founder of X.com which merged with Confinity to form PayPal. Musk later founded SpaceX, co-founded Tesla, Inc., co-founded OpenAI, Neuralink, founded The Boring Company, and became owner of Twitter, Inc.
David O. Sacks, former PayPal COO who later founded Geni.com and Yammer
Scott Banister, early advisor and board member at PayPal.
Roelof Botha, former PayPal CFO who later became a partner and Senior Steward of venture capital firm Sequoia Capital
Steve Chen, former PayPal engineer who co-founded YouTube.
Reid Hoffman, former executive vice president who later founded LinkedIn and was an early investor in Facebook, Aviary
Ken Howery, former PayPal CFO who became a partner at Founders Fund
Chad Hurley, former PayPal web designer who co-founded YouTube
Eric M. Jackson, who wrote the book The PayPal Wars and became chief executive officer of WND Books and co-founded CapLinked
Jawed Karim, former PayPal engineer who co-founded YouTube
Jared Kopf, former PayPal (executive assistant to Peter Thiel) who co-founded Slide, HomeRun and NextRoll
Dave McClure, former PayPal marketing director, a super angel investor for start up companies
Andrew McCormack, co-founder of Valar Ventures
Luke Nosek, PayPal co-founder and former vice president of marketing and strategy, became a partner at Founders Fund with Peter Thiel and Ken Howery
Keith Rabois, former executive at PayPal who later worked at LinkedIn, Slide, Square, Khosla Ventures, and currently with Peter Thiel at Founders Fund, and personally invested in Tokbox, Xoom, Slide, LinkedIn, Geni, Room 9 Entertainment, YouTube, and Yelp
Jack Selby, former vice president of corporate and international development at PayPal who co-founded Clarium Capital with Peter Thiel, later becoming managing director of Grandmaster Capital Management
Premal Shah, former product manager at PayPal, became the founding president of Kiva.org
Russel Simmons, former PayPal engineer who co-founded Yelp Inc.
Jeremy Stoppelman, former vice president of technology at PayPal who later co-founded Yelp
Yishan Wong, former engineering manager at PayPal, later worked at Facebook and became the CEO of Reddit
Answer: Jack Selby was former vice president of corporate and international development at PayPal.
Question: To what other competitive baselines is this approach compared?
Evidence: FLOAT SELECTED: Table 1: Automatic Evaluation Activity/Entity F1 results for baselines and our 3 models (attn means “with attention”). LSTM, HRED and VHRED are reported in Serban et al. (2017a), VHRED (attn) and Reranking-RL in Niu and Bansal (2018a), and the rest are produced by our work. All our four models have statistically significantly higher F1 values (p < 0.001) against VHRED (attn) and MMI.
FLOAT SELECTED: Table 1: Automatic Evaluation Activity/Entity F1 results for baselines and our 3 models (attn means “with attention”). LSTM, HRED and VHRED are reported in Serban et al. (2017a), VHRED (attn) and Reranking-RL in Niu and Bansal (2018a), and the rest are produced by our work. All our four models have statistically significantly higher F1 values (p < 0.001) against VHRED (attn) and MMI.
Answer: LSTMs with and without attention, HRED, VHRED with and without attention, MMI and Reranking-RL
Question: Do aliens exist?
Answer: Although there is no definitive evidence for the existence of extraterrestrial life, pictures and videos of UFOs, although of low quality, have proven to many people that not only does extraterrestrial life exist, but it is visiting Earth regularly.
Question: Is there a similarity between BioIE and Information Extraction?
Evidence: The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases.
The main problems in BioIE are similar to those in Information Extraction:
This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain.
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .
The main problems in BioIE are similar to those in Information Extraction: 1. Named Entity Recognition 2. Relation Extraction 3. Event Extraction
Answer: Yes, there is.
Question: What did NVIDIA Corporation donate for the research?
Evidence: We acknowledge the support of NVIDIA Corporation for their donation of one GeForce GTX TITAN X GPU used for this research.
We acknowledge the support of NVIDIA Corporation for their donation of one GeForce GTX TITAN X GPU used for this research.
Answer: One GeForce GTX TITAN X GPU.
Question: Based on the reference text, which album helped Taylor Swift shift from a country star to more of a pop star?
Evidence: Taylor Alison Swift (born December 13, 1989) is an American singer-songwriter. Her genre-spanning discography, songwriting and artistic reinventions have received critical praise and wide media coverage. Born in West Reading, Pennsylvania, Swift moved to Nashville at age 14 to become a country artist. She signed a songwriting deal with Sony/ATV Music Publishing in 2004 and a recording contract with Big Machine Records in 2005. Her 2006 self-titled debut album made her the first female country artist (singer) to write a U.S. platinum-certified album.

Swift's next albums, Fearless (2008) and Speak Now (2010), explored country pop. The former's "Love Story" and "You Belong with Me" were the first country songs to top the U.S. pop and all-genre airplay charts, respectively. She experimented with rock and electronic styles on Red (2012), which featured her first Billboard Hot 100 number-one song, "We Are Never Ever Getting Back Together", and eschewed her country image in her synth-pop album, 1989 (2014), supported by chart-topping songs "Shake It Off", "Blank Space", and "Bad Blood". Media scrutiny inspired the urban-flavored Reputation (2017) and its number-one single "Look What You Made Me Do".
Answer: Taylor Swift's 1989 album was her first pop album, helping change her image from a country star.
Question: Tell me whether these are solid or gas at room temperature: oxygen, nitrogen, carbon dioxide.
Answer: Oxygen, nitrogen and carbon dioxide are gases at room temperature.
Question: Where was Atlassian founded?
Evidence: Atlassian Corporation (/ətˈlæsiən/) is an Australian software company that develops products for software developers, project managers and other software development teams. The company is domiciled in Delaware, with global headquarters in Sydney, Australia, and US headquarters in San Francisco.
Answer: Atlassian Corporation was founded in Australia, but is domeciled in Delaware, with global headquarters in Sydney, Australia, and US headquarters in San Francisco.
Question: What datasets are used to evaluate the approach?
Evidence: We use two datasets, NELL-One and Wiki-One which are constructed by BIBREF11 . NELL-One and Wiki-One are derived from NELL BIBREF2 and Wikidata BIBREF0 respectively. Furthermore, because these two benchmarks are firstly tested on GMatching which consider both learned embeddings and one-hop graph structures, a background graph is constructed with relations out of training/validation/test sets for obtaining the pre-train entity embeddings and providing the local graph for GMatching.
We use two datasets, NELL-One and Wiki-One which are constructed by BIBREF11 .
Answer: NELL-One, Wiki-One
Question: Which is a species of fish? Soldierfish or Soldier Boy
Answer: Soldierfish
Question: Extract the most famous research of Mary Campbell Dawbarn, and put them in a list.
Evidence: Mary Campbell "Mollie" Dawbarn (5 January 1902 – 24 May 1982) was an Australian biochemist and nutritional physiologist. She is particularly well known for here research on B complex vitamins. Producing an assay for vitamin b12 and perfecting the methods for estimating B1 vitamin in bread are among her most notable works.
Answer: - Producing an assay for vitamin b12
- Perfecting the methods for estimating B1 vitamin in bread
Question: Are the authors the first to use linked entities to improve the performance of the summarizer?
Evidence: Due to its recent success, neural network models have been used with competitive results on abstractive summarization. A neural attention model was first applied to the task, easily achieving state-of-the-art performance on multiple datasets BIBREF5 . The model has been extended to instead use recurrent neural network as decoder BIBREF3 . The model was further extended to use a full RNN encoder-decoder framework and further enhancements through lexical and statistical features BIBREF15 . The current state-of-the-art performance is achieved by selectively encoding words as a process of distilling salient information BIBREF13 .
Neural abstractive summarization models have also been explored to summarize longer documents. Word extraction models have been previously explored, performing worse than sentence extraction models BIBREF19 . Hierarchical attention-based recurrent neural networks have also been applied to the task, owing to the idea that there are multiple sentences in a document BIBREF15 . Finally, distraction-based models were proposed to enable models to traverse the text content and grasp the overall meaning BIBREF4 . The current state-of-the-art performance is achieved by a graph-based attentional neural model, considering the key factors of document summarization such as saliency, fluency and novelty BIBREF14 .
Previous studies on the summarization tasks have only used entities in the preprocessing stage to anonymize the dataset BIBREF15 and to mitigate out-of-vocabulary problems BIBREF14 . Linked entities for summarization are still not properly explored and we are the first to use linked entities to improve the performance of the summarizer.
Linked entities for summarization are still not properly explored and we are the first to use linked entities to improve the performance of the summarizer.
Answer: Yes.
Question: Give me some ideas of what to cook for dinner, I am vegetarian and I have picky children
Answer: As a parent, I have some ideas for quick weeknight meals that should appeal to the whole family. 

Fried rice can be an easy one pan dinner incorporating vegetables, protein, healthy fats, and carbohydrates making it a balanced option for families. Try frying some onion and garlic in some coconut oil. While that cooks, chop up the children's most loved vegetables to throw in - I would recommend carrots, peppers, and sweet peas. Give that a few minutes to cook down then add in your cooked rice with some low sodium soy sauce and candied ginger syrup. Before serving, crack and egg into the pan and mix with the rice until cooked. 

Another great family friendly option that can be modified for vegetarians is shepherds pie. Try substituting the beef/lamb with either cooked lentils or Impossible Beef. The cheesy potatoes and savory filling makes for a family favorite! This can be made ahead of time and popped in the over 45 minutes before serving for an easy weeknight meal.
Question: What do they build to illustrate the main differences between Wikidata and static knowledge bases extracted from Wikipedia?
Evidence: Named Entity Linking is the task of detecting mentions of entities from a knowledge base in free text, as illustrated in Figure 1 .
Most of the entity linking literature focuses on target knowledge bases which are derived from Wikipedia, such as DBpedia BIBREF0 or YAGO BIBREF1 . These bases are curated automatically by harvesting information from the info-boxes and categories on each Wikipedia page and are therefore not editable directly.
Wikidata BIBREF2 is an editable, multilingual knowledge base which has recently gained popularity as a target database for entity linking BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . As these new approaches to entity linking also introduce novel learning methods, it is hard to tell apart the benefits that come from the new models and those which come from the choice of knowledge graph and the quality of its data.
We review the main differences between Wikidata and static knowledge bases extracted from Wikipedia, and analyze their implactions for entity linking. We illustrate these differences by building a simple entity linker, OpenTapioca, which only uses data from Wikidata, and show that it is competitive with other systems with access to larger data sources for some tasks. OpenTapioca can be trained easily from a Wikidata dump only, and can be efficiently kept up to date in real time as Wikidata evolves. We also propose tools to adapt existing entity linking datasets to Wikidata, and offer a new entity linking dataset, consisting of affiliation strings extracted from research articles.
We review the main differences between Wikidata and static knowledge bases extracted from Wikipedia, and analyze their implactions for entity linking. We illustrate these differences by building a simple entity linker, OpenTapioca, which only uses data from Wikidata, and show that it is competitive with other systems with access to larger data sources for some tasks.
Answer: A simple entity linker, OpenTapioca.
Question: how does the water cycle work?
Answer: The water cycle has no starting point, but the oceans seem to be a good starting point. The sun warms the water in the oceans; it evaporates into the air. Rising air currents carry the vapour into the atmosphere, where the lower temperatures cause the vapour to condense into clouds.
Question: What datasets did they use?
Evidence: In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task contains 8,257 tweets. There are 12,784 entities in total in this dataset. Table TABREF19 shows statistics related to each named entity in training, development and test sets.
In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task contains 8,257 tweets. There are 12,784 entities in total in this dataset. Table TABREF19 shows statistics related to each named entity in training, development and test sets.


Answer: The answers are shown as follows:
* BIBREF8 a refined collection of tweets gathered from twitter
Question: What is the best boba order to place from a local boba store for a company that has around 300 employees in the office?
Answer: In order to serve up to 300 people, it's best to order a couple of different tea flavors to fit everyone's taste. People may also have different sweetness and ice preferences, but it can be hard to account for so many preferences, so a good default would be to set every drink to half sweetness and half ice.

A possible order could be: 20 cups of black milk tea, 20 cups of green milk tea, 20 cups of oolong milk tea, 20 cups of mango green tea, 20 cups of passionfruit green tea, 20 cups of matcha latte, 20 cups of taro milk tea, and 20 cups of Thai milk tea. It's best to ask for the boba on the side so that it stays fresh during delivery. Since this is a local store, it would be also wise to place the order ahead of time so that they aren't overwhelmed with the order size.

This order accounts for many different tastes and reduces the likelihood of over-ordering, as it's possible that not all employees will want boba.
Question: What skis should I buy?
Answer: There are many aspects to consider when buying a new pair of skis. The first and probably most important would be to consider you ability level. Ski companies typical group skiers into one of three categories: Level 1 (Beginner), Level 2 (Intermediate) or Level 3 (Advanced). Considering you ability level will help you decide on other factors to consider when purchasing a ski, such as the length, underfoot width, dimensions, camber and rocker.

Another important consideration to make when purchasing skis is to think about the type of terrain that you like to ski. Do you prefer skiing all-mountain conditions, powder conditions or backcountry conditions?

Based on your answers to the previous two questions, you should be able to narrow the universe of skis that you'd consider for purchase. Next, you should use your height to determine the length of the ski that would fit you well.

After you've made a decision on what type of skis you'd like to buy, you should consider what type of bindings and boots you would like to use with your skis.
Question: What is a Moluccan hanging parrot?
Evidence: The Moluccan hanging parrot (Loriculus amabilis) is a species of parrot in the family Psittaculidae. It is endemic to forest and nearby habitats on Halmahera, Bacan and Morotai in Indonesia. It has sometimes included the Sula hanging parrot as a subspecies, but the two are increasingly treated as separate species based on their distinct differences in plumage and size (11 cm for the Moluccan hanging parrot versus 14 cm for the Sula hanging parrot).
Answer: The Moluccan hanging parrot is a species of parrot in the family Psittaculidae. It is endemic to forest and nearby habitats on Halmahera, Bacan and Morotai in Indonesia.
Question: Give me a list of 5 first date ideas
Answer: - Grab coffee 
- Dine out for a meal
- Do something active, like a hike
- Go to the museum
- Have a picninc