DataHammer/scimrc huggingface dataset:
Question: How many observations show the usefulness of linked entities for abstractive summarization?
Evidence: As discussed in Section "Introduction" , we find three observations that show the usefulness of linked entities for abstractive summarization.
First, summaries are mainly composed of linked entities extracted from the original text. In the example, it can be seen that the summary contains four words that refer to different entities. In fact, all noun phrases in the summary mention at least one linked entity. In our experimental data, we extract linked entities from the original text and compare them to the noun phrases found in the summary. We report that $77.1\%$ and $75.1\%$ of the noun phrases on the Gigaword and CNN datasets, respectively, contain at least one linked entity, which confirms our observation.
Second, linked entities can be used to represent the topic of the summary, defined as a multinomial distribution over entities, as graphically shown in the example, where the probabilities refer to the relevance of the entities. Entities have been previously used to represent topics BIBREF7 , as they can be utilized as a controlled vocabulary of the main topics in a document BIBREF8 . In the example, we see that the entity “Jae Seo” is the most relevant because it is the subject of the summary, while the entity “South Korean” is less relevant because it is less important when constructing the summary.
Third, we can make use of the entity commonsense that can be learned as a continuous vector representation from a separate larger corpus BIBREF9 , BIBREF10 . In the example, if we know that the entities “Los Angeles Dodgers” and “New York Mets” are American baseball teams and “Jae Seo” is a baseball player associated with the teams, then we can use this information to generate more coherent summaries. We find that $76.0\%$ of the extracted linked entities are covered by the pre-trained vectors in our experimental data, proving our third observation.
As discussed in Section 1, we find three observations that show the usefulness of linked entities for abstractive summarization.
Answer: Three observations.
Question: Tell me whether these Game of Thrones characters are dead or alive at the end of Season 8: Jon Snow, Cersei Lannister, Daenerys Targaryen, Bran Stark, Khal Drogo, Hodor, Ygritte, Oberyn Martell, Arya Stark, Olenna Tyrell, Tyrion Lannister
Answer: At the end of season 8 of TV show Game of Thrones, the only characters that are alive from the list you've provided are: Jon Snow, Bran Stark, Arya Stark, Tyrion Lannister. The remaining characters did not make it to the end. That is, Cersei Lannister, Daenerys Targaryen, Khal Drogo, Hodor, Ygritte, Oberyn Martell, and Olenna Tyrell are all dead by the time season 8 finale ends.
Question: What is an index fund?
Answer: An index fund is a basket of individual stocks that is designed to match the performance of specific group of stocks. Index funds allow investors to avoid investing in an individual stock and distribute the investment risk across a broader set of companies.
Question: What is the difference between a guitar and bass?
Answer: The main difference between a guitar and a bass is the number of strings that they have. A guitar typically has six strings, while a bass typically has four. Additionally, guitars are typically larger and more expensive than basses.
Question: What city has the northern-most post office in Wisconsin?
Answer: Cornucopia, a small town located on the south shore of Lake Superior, is home to the northern-most post office in Wisconsin.
Question: At what age are children considered adults?
Answer: In general, most children are considered Adults at 18 in most parts of the world.
Question: Who was Bran the Builder?
Answer: Brandon Stark, known as Bran(don) the Builder, is purported to have lived during the Age of Heroes and was the founder of House Stark. Legend claims he built Winterfell and the magical Wall.
Question: You are getting ready for your soccer game and look into your soccer bag. Which of the following items are part of your soccer equipment: cleats, boots, tights, jersey, swimsuit, milk, carrots, water, shin guards, socks, folder, racket, glove
Answer: soccer equipment: cleats, boots, jersey, water, shin guards, socks, ball
Question: Tell me which ones are edible and which ones are not: Lemon, Table, Carrot, Carat, John
Answer: Edible: Lemon, Carrot
Not Edible: Table, Carat, John
Question: What is the name of the curvy street in San Francisco?
Answer: Lombard Street in San Francisco is often referred to as the "crookedest" or most curvy street in the world.
Question: Can you potty train a bunny?
Answer: Just like cats, bunnies can be litter trained, which is why so many bunny owners are able to let their buns run free in the house. Most rabbits prefer to do their business in one spot, and so litter training comes naturally.
Question: what genre was the most difficult to classify?
Evidence: The bossa-nova and jovem-guarda genres, which have few instances in the dataset, are among the most difficult ones to classify using the model. The pop genre, by contrast, has a small distribution between the number of songs and the number of artists, and could not be well classified by our model. This may indicate that our model was unable to identify a pattern due to the low number of songs per artist, or that the song lyrics of this genre cover several subjects that are confused with other genres.
The bossa-nova and jovem-guarda genres, which have few instances in the dataset, are among the most difficult ones to classify using the model.
Answer: The answers are shown as follows:
*  bossa-nova and jovem-guarda genres
Question: What is a Series I-Bond?
Answer: An I-Bond is a savings bond you can purchase from treasury direct that helps protect you from inflation. An I-Bond earns interest at both a fixed rate and the inflation rate, which is determined by the treasury department and is adjusted twice a year.  You can purchase up to $10,000 of electronic I-Bonds per calendar year.
Question: Give me a list of herbs I can plant in my garden
Answer: Mint, Thyme, Basil, Cilantro, Parsley, Tarragon, Oregano
Question: What does the idiom burning your bridges mean?
Answer: The idiom burning your bridges means do not do something that you may regret later. It is commonly used in the scenario when someone is quitting a job. If someone is quitting a job, someone else may say to that person 'Quit your job professionally and respectfully! Don't burn your bridges!'. Someone who burns their bridges risks that the people they worked with may never give them a positive professional reference nor want to hire them in a future opportunity.
Question: What new metrics are suggested to track progress?
Evidence: It is now clear that a key aspect for future work will be developing additional performance metrics based on topological properties. We are in line with recent work BIBREF16 , proposing to shift evaluation from absolute values to more exploratory evaluations focusing on weaknesses and strengths of the embeddings and not so much in generic scores. For example, one metric could consist in checking whether for any given word, all words that are known to belong to the same class are closer than any words belonging to different classes, independently of the actual cosine. Future work will necessarily include developing this type of metrics.
We are in line with recent work BIBREF16 , proposing to shift evaluation from absolute values to more exploratory evaluations focusing on weaknesses and strengths of the embeddings and not so much in generic scores. For example, one metric could consist in checking whether for any given word, all words that are known to belong to the same class are closer than any words belonging to different classes, independently of the actual cosine.
Answer: The answers are shown as follows:
*  For example, one metric could consist in checking whether for any given word, all words that are known to belong to the same class are closer than any words belonging to different classes, independently of the actual cosine
Question: What automatic evaluation metrics are used?
Evidence: We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6. We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6.
We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6.
Answer: The answers are shown as follows:
* BLEU
* embedding-based metrics (Average, Extrema, Greedy and Coherence)
* , entropy-based metrics (Ent-{1,2})
* distinct metrics (Dist-{1,2,3} and Intra-{1,2,3})
Question: What are the benefits of a high protein diet
Answer: High protein diets can have a lot of benefits. For one, if you are interested in muscle growth, protein helps with that, and a high protein diet can lead to increased muscle growth. Additionally, protein rich meals can help people stay full longer which can help decrease snacking leading to weight loss
Question: What's the performance of support vector machines compared to other supervised approaches?
Evidence: Specifically, we conducted a feature ablation study to assess the informativeness of each feature group and a feature elimination study to determine the optimal feature sets for classifying Twitter tweets. We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms BIBREF12 , BIBREF13 . The dataset contains 9,473 annotations for 9,300 tweets. Each tweet is annotated as no evidence of depression (e.g., “Citizens fear an economic depression") or evidence of depression (e.g., “depressed over disappointment"). If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e.g., “feeling down in the dumps"), disturbed sleep (e.g., “another restless night"), or fatigue or loss of energy (e.g., “the fatigue is unbearable") BIBREF10 . For each class, every annotation (9,473 tweets) is binarized as the positive class e.g., depressed mood=1 or negative class e.g., not depressed mood=0.
Based on our prior initial experiments using these feature groups, we learned that support vector machines perform with the highest F1-score compared to other supervised approaches.
Answer: Based on their prior initial experiments using these feature groups, they learned that support vector machines perform with the highest F1-score compared to other supervised approaches.
Question: Provide for me, a number of famous Japanese animation characters that I could research for an assignment on anime.
Answer: Here are a number of famous Japanese animation characters that you could use for further research: Kaito Kuroba (Detective Conan), Giyu Tomioka (Demon Slayer), Muichiro Tokito (Demon Slayer), Toge Inumaki (Jujutsu Kaisen), Rimuru Tempest (That Time I Got Reincarnated As A Slime), Gintoki Sakata (Gintama), Doraemon (Doraemon),  Serena (Pokemon), Satoru Gojo (Jujutsu Kaisen), and Levi Ackerman (Attack On Titan).
Question: Classify these objects as edible or inedible: pizza, slipper, cake, banana, biscuit, shoe, pencil, chalk, soda, juice, television, computer, mirror, cereal, rice
Answer: These items are edible: pizza, cake, banana, biscuit, soda, cereal, rice
These items are inedible: slipper, shoe, pencil, chalk, television, computer, mirror
Question: In the game Dungeons and Dragons, players can be spell casters or non-spell casters. Sort this list into those two categories: Wizard, Bard, Ranger, Barbarian, Cleric, Druid, Rogue, Sorcerer, Warlock, Fighter and Monk.
Answer: In D&D the casters are the Bard, Cleric, Druid, Sorcerer, Warlock, and Wizard. The Rogue, Fighter, Barbarian, Monk and Ranger are non-spell casters.
Question: On Twitter, do the demographic attributes and answers show more correlations than on Yahoo! Answers?
Evidence: As the table shows, terms extracted from Yahoo! Answers tend to be more related, in terms of the number of correlated terms, to attributes related to religion or ethnicity compared to terms from Twitter. However, for two particular attributes (i.e., Price and Buddhist), the number of correlated terms from Twitter is higher than the ones from Yahoo! Answers . These results collectively suggest that there is a wealth of terms, both in Yahoo! Answers and Twitter, which can be used to predict the population demographics.
terms extracted from Yahoo! Answers tend to be more related, in terms of the number of correlated terms, to attributes related to religion or ethnicity compared to terms from Twitter. However, for two particular attributes (i.e., Price and Buddhist), the number of correlated terms from Twitter is higher than the ones from Yahoo! Answers . 
Answer: No.
Question: Which full-frame mirrorless camera was first available in the market?
Evidence: Sony was the first to introduce a full-frame mirrorless camera, the α7, in 2013.
Answer: Sony α7 in 2013
Question: Give me a list of three historical piano composers.
Answer: Johann Sebastian Bach, Ludwig van Beethoven, and Frédéric Chopin.
Question: Name four pork products I could expect to find in a delicatessen
Answer: Pork is a versatile meat. You can expect to find Bacon, Proscuitto, Salami and Pork Belly in a delicatessen near you.
Question: What constitutes the AWS Kinesis family?
Answer: The Kinesis Family of Services is a set of Amazon Web Services (AWS) products designed for processing and analyzing streaming data in real-time. The Kinesis Family of Services includes three main services: 
- Amazon Kinesis Data Streams is a scalable and durable real-time data streaming service that enables you to collect and process large amounts of data in real-time. You can use Kinesis Data Streams to build custom applications for real-time processing of streaming data, such as log processing, real-time analytics, and data ingestion from various sources.
- Amazon Kinesis Data Firehose is a fully managed service that allows you to capture, transform, and load streaming data into Amazon S3, Amazon Redshift, and other data stores in near real-time. It simplifies the process of loading streaming data to these destinations without the need for any intermediate servers or data processing.
- Amazon Kinesis Data Analytics is a fully managed service that enables you to analyze streaming data in real-time using Apache Flink stream processing framework.
Question: How do you give a good presentation?
Answer: There are several things you should think about if you'd like to give a good presentation.

1. Know your audience. If you use jargon or reference concepts that your audience doesn't understand, you may lose your audience's attention.
2. Pay attention to how your audience is reacting. Do they appear bored? Do they look confused? You can and should make adjustments to what you're presenting based on this real-time feedback.
3. Communicate clearly. You should make a deliberate attempt to speak clearly, and at a reasonable pace. You can think of an orator you admire, and channel their communication style.
4. Practice, practice, practice. Practicing the presentation beforehand can help your confidence. Even if you're extremely nervous, if you know the content of the presentation you're going to have more confidence than otherwise. Additionally, not having to spend as much conscious effort thinking about what content you need to say allows for more of your attention to be spent on some of the above suggestions, like ensuring you are communicating clearly and paying attention to your audience.

Through knowing your audience, paying attention to their reactions, communicating clearly and practicing, you can significantly increase your chances of giving a good presentation.
Question: What is the best way to make Indian ginger tea?
Answer: To make two cups of Indian masala tea, first take 1.5 cups of water in a pot and add about 1 teaspoon of grated ginger, add 2 tablespoons of loose tea leaves (best is Tetley tea). Bring this to boil and add  3/4 cup of milk (whole milk tastes the best) and 3 tablespoons of sugar. Simmer and let the mixture boil for 5 minutes. Use a fine strainer to pour the tea into cups. Enjoy your cup of the best Indian Ginger tea ever.
Question: What data/studies do the authors provide to support the assertion that the majority of aggressive conversations contain code-mixed languages?
Evidence: The informal setting/environment of social media often encourage multilingual speakers to switch back and forth between languages when speaking or writing. These all resulted in code-mixing and code-switching. Code-mixing refers to the use of linguistic units from different languages in a single utterance or sentence, whereas code-switching refers to the co-occurrence of speech extracts belonging to two different grammatical systemsBIBREF3. This language interchange makes the grammar more complex and thus it becomes tough to handle it by traditional algorithms. Thus the presence of high percentage of code-mixed content in social media text has increased the complexity of the aggression detection task. For example, the dataset provided by the organizers of TRAC-2018 BIBREF0, BIBREF2 is actually a code-mixed dataset.
The informal setting/environment of social media often encourage multilingual speakers to switch back and forth between languages when speaking or writing. These all resulted in code-mixing and code-switching. Code-mixing refers to the use of linguistic units from different languages in a single utterance or sentence, whereas code-switching refers to the co-occurrence of speech extracts belonging to two different grammatical systemsBIBREF3. This language interchange makes the grammar more complex and thus it becomes tough to handle it by traditional algorithms. Thus the presence of high percentage of code-mixed content in social media text has increased the complexity of the aggression detection task. For example, the dataset provided by the organizers of TRAC-2018 BIBREF0, BIBREF2 is actually a code-mixed dataset.
Answer: None
Question: Is LSTM the only trained to predict sentences?
Evidence: NIC - Show and Tell uses CNN model which is currently yielding the state-of-the-art results. The model achieved 0.628 when evaluating on BLEU-1 on COCO-2014 dataset. For CNN part, we utilize VGG-16 BIBREF20 architecture pre-trained on COCO-2014 image sets with all categories. In decoding part, LSTM is not only trained to predict sentence but also to compute probability for each word to be generated. As a result, output sentence will be chosen using search algorithms to find the one that have words yielding the maximum probabilities.
In decoding part, LSTM is not only trained to predict sentence but also to compute probability for each word to be generated.
Answer: No.
Question: Who did John Evelyn support during the English Civil War?
Evidence: Sir John Evelyn (1591–1664) was an English politician who sat in the House of Commons at various times between 1628 and 1660. He reluctantly supported the Parliamentary side in the English Civil War.

Evelyn was the son of Sir John Evelyn of Kingston, Godstone, Surrey and Marden, MP and his wife Elizabeth Stever, daughter of William Stever of Kingston upon Thames. He was baptised at Kingston upon Thames on 20 October 1591. He was admitted at Emmanuel College, Cambridge on 13 March 1606. He was a member of the Virginia Company in 1612 and of the East India Company in 1624. He was a JP for Surrey from 1624.
Answer: the Parliamentary side
Question: Based on the reference text about the Tampa Bay Rays, what is the name of their stadium and who are their main rivals?
Evidence: The Tampa Bay Rays are an American professional baseball team based in St. Petersburg, Florida. The Rays compete in Major League Baseball (MLB) as a member club of the American League (AL) East division. Since its inception, the team's home venue has been Tropicana Field.

Following nearly three decades of unsuccessfully trying to gain an expansion franchise or enticing existing teams to relocate to the Tampa Bay area, an ownership group led by Vince Naimoli was approved on March 9, 1995. The team began play as the Tampa Bay Devil Rays in the 1998 Major League Baseball season.

The team's first decade of play was marked by futility; they finished in last place in the AL East in all but the 2004 season, when they finished second to last. Following the 2007 season, Stuart Sternberg, who had purchased controlling interest in the team from Vince Naimoli two years earlier, changed the team's name from "Devil Rays" to "Rays", now meaning both a manta ray and a ray of sunshine; a manta ray logo appears on the uniform sleeves while a sunburst appears on the uniform front. The 2008 season saw the Rays post their first winning season, their first AL East championship, and their first American League pennant (defeating the rival Boston Red Sox in the ALCS), though they lost to the Philadelphia Phillies in that year's World Series. Since then, the Rays have played in the postseason seven more times, winning the American League pennant again in 2020 and losing to the Los Angeles Dodgers in that year's World Series.

The Tampa Bay Rays' chief rivals are the Boston Red Sox and the New York Yankees, which also play in the AL East. Regarding the former, there have been several notable on-field incidents. The Rays also have an in-state interleague rivalry with the National League (NL)'s Miami Marlins (originally the Florida Marlins), whom they play in the Citrus Series.

Through 2022, the Rays' all-time record is 1,912–2,034 (.485)
Answer: The name of the Tampa Bay Rays stadium is Tropicana Field and their main rivals are the Boston Red Sox and New York Yankees.
Question: What does below SAS code do
PROC SUMMARY NWAY MISSING DATA=named_data;
		CLASS col1 col2 col3 col4;
		VAR tot_amt tot_gst_amt total_settlement_amt;
		OUTPUT OUT=PYMT_SUM(DROP=_:) SUM=;
	QUIT;
Answer: The SAS code block you provided uses the PROC SUMMARY procedure to calculate summary statistics for a dataset called named_data. Here's what each line of the code does:

NWAY - specifies that the summary statistics should be calculated for all possible combinations of the CLASS variables.
MISSING - specifies that missing values in the named_data dataset should be included in the summary statistics.
CLASS - specifies the variables that define the groups for which the summary statistics should be calculated.
VAR - specifies the variables for which summary statistics should be calculated.
OUTPUT - specifies the output dataset and the statistics to include in the output.
OUT - specifies the name of the output dataset.
DROP=_: - drops the automatic variables created by SAS.
SUM= - calculates the sum of the specified variables.
The resulting output dataset PYMT_SUM will contain the summary statistics for each combination of the CLASS variables specified