DataHammer/scimrc huggingface dataset:
Question: Do they perform a quantitative analysis of their model displaying knowledge distortions?
Evidence: Additionally, we provide evidence that the model learns knowledge of a separation between syntax and the lexicon that is similar to that of a human. Figure FIGREF6 displays the learned $\sigma (w)$ embeddings for some input words, across the domains. To avoid cherry-picking the results, we chose the input words arbitrarily, subject to the following constraint. We considered each word to typically have a different syntactic category than the other choices from that domain. This constraint was used to present a diverse selection of words. Table TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. To avoid cherry-picking the results, we arbitrarily chose an input for each domain, subject to the following constraint. The input is not in the train set and the undamaged LLA-LSTM model produces a translation that we judge to be correct. For all inputs that we chose, damage to the analog of Broca's area (the LSTMs) results in an output that describes content only if it is described by the input. However, the output does not show understanding of the input's syntax. In the naturalistic domains, damage to the analog of Wernicke's area (the Lexicon Unit) results in an output with incorrect content that would be acceptable if the input had different words but the same syntax. These knowledge distortions are precisely those that are expected in the respective human aphasics BIBREF0. We also provide corpus-level results from the damaged models by presenting mean precision on the test sets. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.
TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia.
Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.
Answer: Yes.
Question: What's the best way to brew an espresso?
Answer: There is no general consensus on how to evaluate a cup of espresso, so there is no general "best way". Here are a few pointers that most of the coffee-lover community agrees will enhance the consistency and flavor of an espresso:

* Use freshly roasted beans! No amount of expensive espresso machines can save a stale batch of beans.
* Make sure your puck is evenly distributed. If needed, use a WDT tool to stir the puck gently from bottom to top, until you reach consistency.
* Observe the brewing process closely and make sure the fluid flows smoothly. If the stream is unbalanced or side-splitting, check your puck and your machine's pressurization.
Question: what is typical enterprise sales person comp mix between base and commission?
Answer: 50/50
Question: Give me a summary of why an abstract is useful in academic literature?
Evidence: Academic literature uses the abstract to succinctly communicate complex research. An abstract may act as a stand-alone entity instead of a full paper. As such, an abstract is used by many organizations as the basis for selecting research that is proposed for presentation in the form of a poster, platform/oral presentation or workshop presentation at an academic conference. Most bibliographic databases only index abstracts rather than providing the entire text of the paper. Full texts of scientific papers must often be purchased because of copyright and/or publisher fees and therefore the abstract is a significant selling point for the reprint or electronic form of the full text. The abstract can convey the main results and conclusions of a scientific article but the full text article must be consulted for details of the methodology, the full experimental results, and a critical discussion of the interpretations and conclusions. An abstract allows one to sift through copious numbers of papers for ones in which the researcher can have more confidence that they will be relevant to their research. Once papers are chosen based on the abstract, they must be read carefully to be evaluated for relevance. It is generally agreed that one must not base reference citations on the abstract alone, but the content of an entire paper.
Answer: An abstract is useful in academic research because it conveys the complex research in a concise way by acting as a stand-alone summary. Therefore, it is usually used by many organizations as a base for selecting research. Moreover, abstracts are usually provided free of charge by many bibliographic databases which makes the research works more accessible. In addition, researchers can understand if the work is relevant to their research just by reading the abstract.
Question: What did the author collect in this article?
Evidence: Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0 .
For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community. In particular, during May-July 2015, we gathered the profile information for all the users that have self-reported their location in the U.S., along with a number of posts for all their associated blogs. We utilize this blog collection to generate maps of the U.S. that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes. We believe that these maps can provide valuable insights and partial verification of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5 .
For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community.
Answer: For this paper, the author compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community.
Question: Please classify each of the following as European or Asian countries: England, Thailand, Greece, Japan.
Answer: England and Greece are European countries, while Thailand and Japan are Asian countries.
Question: what is a computer?
Answer: A computer is a machine that can be programmed to carry out sequences of arithmetic or logical operations (computation) automatically.
Question: What multilingual parallel data is used for training proposed model?
Evidence: We adopt the mixture of two multilingual translation corpus as our training data: MultiUN BIBREF20 and OpenSubtitles BIBREF21. MultiUN consists of 463,406 official documents in six languages, containing around 300M words for each language. OpenSubtitles is a corpus consisting of movie and TV subtitles, which contains 2.6B sentences over 60 languages. We select four shared languages of the two corpora: English, Spanish, Russian and Chinese. Statistics of the training corpus are shown in Table TABREF14. Sentences are tokenized by Wordpiece as in BERT. A multilingual vocabulary of 50K tokens is used. For validation and testing, we randomly sample 10000 sentences respectively from each language pair. The rest data are used for training. For monolingual pre-training, we use English Wikipedia corpus, which contains 2,500M words.
We adopt the mixture of two multilingual translation corpus as our training data: MultiUN BIBREF20 and OpenSubtitles BIBREF21. MultiUN consists of 463,406 official documents in six languages, containing around 300M words for each language. OpenSubtitles is a corpus consisting of movie and TV subtitles, which contains 2.6B sentences over 60 languages. We select four shared languages of the two corpora: English, Spanish, Russian and Chinese. Statistics of the training corpus are shown in Table TABREF14.
Answer: The answers are shown as follows:
* MultiUN BIBREF20
* OpenSubtitles BIBREF21
Question: Who are some of the most successful golfers in history?
Answer: Sam Snead, Tiger Woods, and Jack Nicklaus are the most successful golfers of all time with the top three most major championship wins.
Question: Was Wilbur Snyder a good wrestler
Evidence: Wilbur Snyder (September 15, 1929 – December 25, 1991) was an American football player and professional wrestler.

Wrestling career
Snyder's wrestling debut occurred during football's 1953 off-season. He was trained by Sandor Szabo and Warren Bockwinkel in Southern California. In 1954, Snyder retired from football completely to pursue a full-time career in wrestling. Part of his in-ring persona included the use of many football tactics, and it garnered Snyder a lot of national attention. He had already been a regional champion in Montreal, Quebec, Canada when he made a name for himself by defeating Verne Gagne and winning the United States Championship at Marigold Arena in Chicago on April 7, 1956.
Answer: Wilbur Snyder (September 15, 1929 – December 25, 1991) was an American football player and professional wrestler.

Snyder's wrestling debut occurred during football's 1953 off-season. He was trained by Sandor Szabo and Warren Bockwinkel in Southern California. In 1954, Snyder retired from football completely to pursue a full-time career in wrestling. Part of his in-ring persona included the use of many football tactics, and it garnered Snyder a lot of national attention. 

He had already been a regional champion in Montreal, Quebec, Canada when he made a name for himself by defeating Verne Gagne and winning the United States Championship at Marigold Arena in Chicago on April 7, 1956.
Question: Is there a fast way to put a duvet cover over a duvet?
Answer: Yes! First, lay the duvet out on top of a bed. Next, turn the duvet cover inside out, and place it on top of the duvet, with the bottom facing the top of the bed and duvet. Tie the corners of the duvet cover to the duvet. With a friend, reach inside the duvet cover to grab the furthest corners of the duvet cover with the hand facing the top of the bed, and grab the other corners with the opposite hand. Note: your arms should be crossed. Pull your arms so they are no longer crossed, and stretch them out as far as you can reach. You now have the duvet inside the duvet cover.
Question: Is the model tested against any baseline?
Evidence: Suppose a user wants to write a sentence “I will be 10 minutes late.” Ideally, she would type just a few keywords such as “10 minutes late” and an autocomplete system would be able to infer the intended sentence (Figure FIGREF1). Existing left-to-right autocomplete systems BIBREF0, BIBREF1 can often be inefficient, as the prefix of a sentence (e.g. “I will be”) fails to capture the core meaning of the sentence. Besides the practical goal of building a better autocomplete system, we are interested in exploring the tradeoffs inherent to such communication schemes between the efficiency of typing keywords, accuracy of reconstruction, and interpretability of keywords.
One approach to learn such schemes is to collect a supervised dataset of keywords-sentence pairs as a training set, but (i) it would be expensive to collect such data from users, and (ii) a static dataset would not capture a real user's natural predilection to adapt to the system BIBREF2. Another approach is to avoid supervision and jointly learn a user-system communication scheme to directly optimize the combination of efficiency and accuracy. However, learning in this way can lead to communication schemes that are uninterpretable to humans BIBREF3, BIBREF4 (see Appendix for additional related work).
In this work, we propose a simple, unsupervised approach to an autocomplete system that is efficient, accurate, and interpretable. For interpretability, we restrict keywords to be subsequences of their source sentences based on the intuition that humans can infer most of the original meaning from a few keywords. We then apply multi-objective optimization approaches to directly control and achieve desirable tradeoffs between efficiency and accuracy.
We observe that naively optimizing a linear combination of efficiency and accuracy terms is unstable and leads to suboptimal schemes. Thus, we propose a new objective which optimizes for communication efficiency under an accuracy constraint. We show this new objective is more stable and efficient than the linear objective at all accuracy levels.
As a proof-of-concept, we build an autocomplete system within this framework which allows a user to write sentences by specifying keywords. We empirically show that our framework produces communication schemes that are 52.16% more accurate than rule-based baselines when specifying 77.37% of sentences, and 11.73% more accurate than a naive, weighted optimization approach when specifying 53.38% of sentences. Finally, we demonstrate that humans can easily adapt to the keyword-based autocomplete system and save nearly 50% of time compared to typing a full sentence in our user study.
We empirically show that our framework produces communication schemes that are 52.16% more accurate than rule-based baselines when specifying 77.37% of sentences, and 11.73% more accurate than a naive, weighted optimization approach when specifying 53.38% of sentences.
Answer: Yes.
Question: Which one of the following objects is least like the others, and why?
Lemon, Car, Grass, Lime
Answer: A car is least like the others, because it is a man-made non-living object, while the rest are plants.
Question: Given this paragraph about the demographics of Osaka, what was the highest number of residents ever recorded to be living in Osaka at any one point in time?
Evidence: Population numbers have been recorded in Osaka since as early as 1873, in the early Meiji era. According to the census in 2005, there were 2,628,811 residents in Osaka, an increase of 30,037 or 1.2% from 2000. There were 1,280,325 households with approximately 2.1 persons per household. The population density was 11,836 persons per km2. The Great Kantō earthquake caused a mass migration to Osaka between 1920 and 1930, and the city became Japan's largest city in 1930 with 2,453,573 people, outnumbering even Tokyo, which had a population of 2,070,913. The population peaked at 3,252,340 in 1940, and had a post-war peak of 3,156,222 in 1965, but continued to declined since, as the residents moved out to the suburbs.

There were 144,123 registered foreigners, the two largest groups being Korean (60,110) and Chinese (39,551) 2021 years. Ikuno, with its Tsuruhashi district, is the home to one of the largest population of Korean residents in Japan, with 20,397 registered Zainichi Koreans.
Answer: The highest population ever recorded in Osaka was 3,252,340 in 1940.
Question: Write a polite rejection response to a LinkedIn recruiter
Answer: Hello,

Thank you for your message. I’m currently happy where I am but hope to reconnect in the future. Best of luck to you on the search. 

With gratitude,
Question: what was the American Revolutionary War
Evidence: The American Revolutionary War (April 19, 1775 – September 3, 1783), also known as the Revolutionary War or American War of Independence, was the military conflict of the American Revolution in which American Patriot forces under George Washington's command defeated the British, establishing and securing the independence of the United States.
Answer: The American Revolutionary War (April 19, 1775 – September 3, 1783), also known as the Revolutionary War or American War of Independence, was the military conflict of the American Revolution in which American Patriot forces under George Washington's command defeated the British, establishing and securing the independence of the United States. Fighting began on April 19, 1775 at the Battles of Lexington and Concord. The war was formalized and intensified following passage of the Lee Resolution, which asserted that the Thirteen Colonies were "free and independent states", and the Declaration of Independence, drafted by the Committee of Five and written primarily by Thomas Jefferson, by the Second Continental Congress in Philadelphia, on July 2, 1776 and July 4, 1776, respectively.
Question: Do Ceanothus have shallow roots?
Answer: Ceanothus plants are super variable, but in general, they tend to have a deep taproot. Sometimes this is not the case if water sources are more reliable near the soil surface.
Question: Write a short blog post about why you have chosen to delete your instagram account and why you think that instagram has an negative impact on society.
Answer: Hi friends, long time no talk! Today, I want to tell you all about why I decided to delete my instagram account. This is a decision I had been thinking about for a long time, and ultimately I decided it just made sense to get rid of the account. However, I didn't want to do this without sharing with you all why I made this decision, because I think it is important to explain. I've had an instagram account for years, but often felt as though I would get sucked into a scroll spiral (we've all been there, let's be real) where I would just sit on my phone for hours. I had already put in place screen time limits, but I felt like I wasn't really getting a lot out of the app. However, what ultimately made me decide to go full send and delete my account was reading about how social media causes low self esteem, and how it could be causing many of the mental health issues we are seeing with young people right now. After reading about this, I decided enough was enough, and I finally deleted my account. I would encourage you all to do the same - don't worry, I'm not giving up this blog, so you'll still be able to check in on what I'm up to. Let me know in the comments what you guys think, and talk next week!
Question: Extract names of all weapons mentioned in the paragraph below:
Evidence: In the late 18th century, French General Jean-Baptiste Vaquette de Gribeauval promoted standardized weapons in what became known as the Système Gribeauval after it was issued as a royal order in 1765. (Its focus at the time was artillery more than muskets or handguns.) One of the accomplishments of the system was that solid cast cannons were bored to precise tolerances, which allowed the walls to be thinner than cannons poured with hollow cores. However, because cores were often off center, the wall thickness determined the size of the bore. Standardized boring allowed cannons to be shorter without sacrificing accuracy and range because of the tighter fit of the shells. It also allowed standardization of the shells.
Answer: The following are names of weapons in the paragraph above: artillery, muskets, handguns, cannons, shells
Question: What data does the HAHA dataset include?
Evidence: The Humor Analysis based on Human Annotation (HAHA) 2019 BIBREF1 competition asked for analysis of two tasks in the Spanish language based on a corpus of publicly collected data described in Castro et al. BIBREF2 :
The HAHA dataset includes labeled data for 24,000 tweets and a test set of 6,000 tweets (80%/20% train/test split.) Each record includes the raw tweet text (including accents and emoticons), a binary humor label, the number of votes for each of five star ratings and a “Funniness Score” that is the average of the 1 to 5 star votes cast. Examples and data can be found on the CodaLab competition webpage.
The HAHA dataset includes labeled data for 24,000 tweets and a test set of 6,000 tweets (80%/20% train/test split.) 
Answer: The HAHA dataset includes labeled data for 24,000 tweets and a test set of 6,000 tweets (80%/20% train/test split.)
Question: What are the baselines?
Evidence: We show the final results for opinion recommendation, comparing our proposed model with the following state-of-the-art baseline systems:
RS-Average is the widely-adopted baseline (e.g., by Yelp.com), using the averaged review scores as the final score.
RS-Linear estimates the rating score that a user would give by INLINEFORM0 BIBREF49 , where INLINEFORM1 and INLINEFORM2 are the the training deviations of the user INLINEFORM3 and the product INLINEFORM4 , respectively.
RS-Item applies INLINEFORM0 NN to estimate the rating score BIBREF50 . We choose the cosine similarity between INLINEFORM1 to measure the distance between product.
RS-MF is a state-of-the-art recommendation model, which uses matrix factorisation to predict rating score BIBREF8 , BIBREF41 , BIBREF25 .
Sum-Opinosis uses a graph-based framework to generate abstractive summarisation given redundant opinions BIBREF51 .
Sum-LSTM-Att is a state-of-the-art neural abstractive summariser, which uses an attentional neural model to consolidate information from multiple text sources, generating summaries using LSTM decoding BIBREF44 , BIBREF3 .
All the baseline models are single-task models, without considering rating and summarisation prediction jointly. The results are shown in Table TABREF46 . Our model (“ Joint”) significantly outperforms both “RS-Average” and “RS-Linear” ( INLINEFORM0 using INLINEFORM1 -test), which demonstrates the strength of opinion recommendation, which leverages user characteristics for calculating a rating score for the user.
We show the final results for opinion recommendation, comparing our proposed model with the following state-of-the-art baseline systems:

RS-Average is the widely-adopted baseline (e.g., by Yelp.com), using the averaged review scores as the final score.

RS-Linear estimates the rating score that a user would give by INLINEFORM0 BIBREF49 , where INLINEFORM1 and INLINEFORM2 are the the training deviations of the user INLINEFORM3 and the product INLINEFORM4 , respectively.

RS-Item applies INLINEFORM0 NN to estimate the rating score BIBREF50 . We choose the cosine similarity between INLINEFORM1 to measure the distance between product.

RS-MF is a state-of-the-art recommendation model, which uses matrix factorisation to predict rating score BIBREF8 , BIBREF41 , BIBREF25 .

Sum-Opinosis uses a graph-based framework to generate abstractive summarisation given redundant opinions BIBREF51 .

Sum-LSTM-Att is a state-of-the-art neural abstractive summariser, which uses an attentional neural model to consolidate information from multiple text sources, generating summaries using LSTM decoding BIBREF44 , BIBREF3 .

All the baseline models are single-task models, without considering rating and summarisation prediction jointly. The results are shown in Table TABREF46 . 
Answer: The answers are shown as follows:
* RS-Average 
* RS-Linear
* RS-Item
* RS-MF
* Sum-Opinosis
* Sum-LSTM-Att
Question: Classify each of the following as either a drink or a food: apple juice, cake, hamburger, carrot, orange juice, soda.
Answer: apple juice is a drink
cake is a food
hamburger is a food
carrot is a food
orange juice is a drink
soda is a drink