DataHammer/scimrc huggingface dataset:
Question: Did they use the state-of-the-art model to analyze the attention?
Evidence: We make two main contributions. First, we introduce new strategies for interpreting the behavior of deep models in their intermediate layers, specifically, by examining the saliency of the attention and the gating signals. Second, we provide an extensive analysis of the state-of-the-art model for the NLI task and show that our methods reveal interesting insights not available from traditional methods of inspecting attention and word saliency.
Second, we provide an extensive analysis of the state-of-the-art model for the NLI task and show that our methods reveal interesting insights not available from traditional methods of inspecting attention and word saliency.
Answer: The answers are shown as follows:
* we provide an extensive analysis of the state-of-the-art model
Question: What does it mean if something "slaps"?
Answer: Slaps is a slang word originating from the San Francisco Bay Area, popularized by local rapper E-40. Music "slaps" when the music hits hard, the bass pumps just right, and you feel it in your chest. Music that makes you want to get active and dance is music that "slaps".

Food does not ever slap. Food smacks if it is good. It does not ever slap.
Question: What datasets were used?
Evidence: Dataset: We perform experiments on two widely-used tasks for the English-to-Japanese language pair: KFTT BIBREF12 and BTEC BIBREF13 . KFTT is a collection of Wikipedia article about city of Kyoto and BTEC is a travel conversation corpus. BTEC is an easier translation task than KFTT, because KFTT covers a broader domain, has a larger vocabulary of rare words, and has relatively long sentences. The details of each corpus are depicted in Table TABREF19 .
Dataset: We perform experiments on two widely-used tasks for the English-to-Japanese language pair: KFTT BIBREF12 and BTEC BIBREF13 . KFTT is a collection of Wikipedia article about city of Kyoto and BTEC is a travel conversation corpus. BTEC is an easier translation task than KFTT, because KFTT covers a broader domain, has a larger vocabulary of rare words, and has relatively long sentences. 
Answer: The answers are shown as follows:
* KFTT BIBREF12 and BTEC BIBREF13
Question: What is a NFT?
Answer: A non-fungible token (NFT) is a unique digital identifier that cannot be copied, substituted, or subdivided, that is recorded in a blockchain, and that is used to certify ownership and authenticity. The ownership of an NFT is recorded in the blockchain and can be transferred by the owner, allowing NFTs to be sold and traded. NFTs can be created by anybody, and require few or no coding skills to create. NFTs typically contain references to digital files such as photos, videos, and audio. Because NFTs are uniquely identifiable assets, they differ from cryptocurrencies, which are fungible.
Question: Can AI fully replace software engineer job?
Answer: AI can generate software code for a well described model. The general code may need software engineers to validate and evaluate it. It can help speed up software development, but for some complex product, it still needs software engineers to divide the development to many small modules and well define each module before AI can generate the software code for it. AI is a good tool to help software engineers, but it can't fully replace software engineer jobs
Question: What does it take to be a really good sports coach?
Answer: A good sports coach requires a balanced mixture of personality, skill, experience and charisma.  In terms of personality, a great sports coach will have good emotional intelligence to interact with many different types of people.  In terms of skill, the coach will need to have either played the sport they coach and/or be very well trained in the art of motor skill learning.  They will need to be able to use the right words, actions and demonstrations to teach skills to players at different level.  They need the experience to understand the many different scenarios that come up during practices and games and know how to apply different strategies.  They also need the charisma to be a good leader.  Their coaching style needs to inspire their teams to perform at the best of their ability.  The leadership trust is built over a series of positive interactions with helping players to achieve their goals.
Question: Given a reference text about Elk Grove, what state is it located in and about how many people live there?
Evidence: Elk Grove is a city in Sacramento County, California, United States. Located just south of the state capital of Sacramento, it is part of the Sacramento–Arden-Arcade–Roseville Metropolitan Statistical Area. As of the 2020 Census, the population of the city was 176,124. A 2021 Census estimate puts the population of the city at 178,997. Elk Grove has many wineries, wine cellars, and vineyards. Elk Grove was the fastest-growing city in the U.S. between July 1, 2004, and July 1, 2005, and is also presently the second-largest city in Sacramento County by population.

The City of Elk Grove became the first city in California to be incorporated in the 21st century. It is a general law city with a council/manager form of government. One of Elk Grove's largest employers is the Elk Grove Unified School District, which is the city's second largest employer.
Answer: Elk Grove is a city in California and has a population of about 180,000.
Question: Classify the below into cars or SUVs.

Mazda CX50, Nissan Altima, Mazda CX5, Toyota Rav4, KIA Sportage, Honda Civic, Hyundai Elantra.
Answer: SUVs- Mazda CX50, Mazda CX5, Toyota Rav4, KIA Sportage. Cars- Nissan Altima, Honda Civic, Hyundai Elantra.
Question: Could you tell me more about the metrics used for performance evaluation?
Evidence: FLOAT SELECTED: Table 1: BLUE tasks
The aim of the inference task is to predict whether the premise sentence entails or contradicts the hypothesis sentence. We use the standard overall accuracy to evaluate the performance.
The aim of the relation extraction task is to predict relations and their types between the two entities mentioned in the sentences. The relations with types were compared to annotated data. We use the standard micro-average precision, recall, and F1-score metrics.
The aim of the named entity recognition task is to predict mention spans given in the text BIBREF20 . The results are evaluated through a comparison of the set of mention spans annotated within the document with the set of mention spans predicted by the model. We evaluate the results by using the strict version of precision, recall, and F1-score. For disjoint mentions, all spans also must be strictly correct. To construct the dataset, we used spaCy to split the text into a sequence of tokens when the original datasets do not provide such information.
The sentence similarity task is to predict similarity scores based on sentence pairs. Following common practice, we evaluate similarity by using Pearson correlation coefficients.
HoC (the Hallmarks of Cancers corpus) consists of 1,580 PubMed abstracts annotated with ten currently known hallmarks of cancer BIBREF27 . Annotation was performed at sentence level by an expert with 15+ years of experience in cancer research. We use 315 ( $\sim $ 20%) abstracts for testing and the remaining abstracts for training. For the HoC task, we followed the common practice and reported the example-based F1-score on the abstract level BIBREF28 , BIBREF29 .
FLOAT SELECTED: Table 1: BLUE tasks
We use the standard overall accuracy to evaluate the performance
We use the standard micro-average precision, recall, and F1-score metrics
We evaluate the results by using the strict version of precision, recall, and F1-score.
Following common practice, we evaluate similarity by using Pearson correlation coefficients.
we followed the common practice and reported the example-based F1-score on the abstract level
we followed the common practice and reported the example-based F1-score on the abstract level
Answer: BLUE utilizes different metrics for each of the tasks: Pearson correlation coefficient, F-1 scores, micro-averaging, and accuracy
Question: What is photosynthesis?
Evidence: Photosynthesis is a process used by plants and other organisms to convert light energy into chemical energy that, through cellular respiration, can later be released to fuel the organism's activities. Some of this chemical energy is stored in carbohydrate molecules, such as sugars and starches, which are synthesized from carbon dioxide and water – hence the name photosynthesis, from the Greek phōs (φῶς), "light", and synthesis (σύνθεσις), "putting together". Most plants, algae, and cyanobacteria perform photosynthesis; such organisms are called photoautotrophs. Photosynthesis is largely responsible for producing and maintaining the oxygen content of the Earth's atmosphere, and supplies most of the energy necessary for life on Earth.

Although photosynthesis is performed differently by different species, the process always begins when energy from light is absorbed by proteins called reaction centers that contain green chlorophyll (and other colored) pigments/chromophores. In plants, these proteins are held inside organelles called chloroplasts, which are most abundant in leaf cells, while in bacteria they are embedded in the plasma membrane. In these light-dependent reactions, some energy is used to strip electrons from suitable substances, such as water, producing oxygen gas. The hydrogen freed by the splitting of water is used in the creation of two further compounds that serve as short-term stores of energy, enabling its transfer to drive other reactions: these compounds are reduced nicotinamide adenine dinucleotide phosphate (NADPH) and adenosine triphosphate (ATP), the "energy currency" of cells.
Answer: Photosynthesis is
- a process used by plants and other organisms to convert light energy into chemical energy 
- a word derived from the Greek phōs (φῶς), "light", and synthesis (σύνθεσις), "putting together"
- a process that always begins when energy from light is absorbed by proteins called reaction centers
- performed differently by different species
- largely responsible for producing and maintaining the oxygen content of the Earth's atmosphere
Question: Is this an English-language dataset?
Evidence: Within this scenario, it is crucial to analyse people attitudes towards rumours in social media and to resolve their veracity as soon as possible. Several approaches have been proposed to check the rumour veracity in social media BIBREF1 . This paper focus on a stance-based analysis of event-related rumours, following the approach proposed at SemEval-2017 in the new RumourEval shared task (Task 8, sub-task A) BIBREF2 . In this task English tweets from conversation threads, each associated to a newsworthy event and the rumours around it, are provided as data. The goal is to determine whether a tweet in the thread is supporting, denying, querying, or commenting the original rumour which started the conversation. It can be considered a stance classification task, where we have to predict the user's stance towards the rumour from a tweet, in the context of a given thread. This task has been defined as open stance classification task and is conceived as a key step in rumour resolution, by providing an analysis of people reactions towards an emerging rumour BIBREF0 , BIBREF3 . The task is also different from detecting stance towards a specific target entity BIBREF4 .
 In this task English tweets from conversation threads, each associated to a newsworthy event and the rumours around it, are provided as data. The goal is to determine whether a tweet in the thread is supporting, denying, querying, or commenting the original rumour which started the conversation. 
Answer: Yes.
Question: What was Charles Dickens last (unfinished) novel
Answer: Mystery of Edwin
Drood
Question: What are some ways to inculcate practice based learning in kids?
Answer: Learning by doing is one of the best ways a kid can learn a concept. One way to do this is by using STEM based home kits that complement what a kid learns at school. For every concept the kid learns at school they should be exposed to application of the concept in the real world. Additionally, modern technology like virtual reality and augmented reality can fill the gap to an extent. Using Virtual Reality kids can see and visit places such as factories, landmarks which are hard to visit. Using Augmented Reality kids can see things right in from of them and can interact deeply with the 3D object closely. the learning curriculum must be comprehensive and emphasize experiential learning.
Question: Give me 5 kinds of nuts that are delicious.
Answer: Five kinds of nuts that are delicious are 1) pistachios, 2) brazil nuts, 3) hazelnuts, 4) peanuts, and 5) macadamia nuts.
Question: What is the highest-grossing film worldwide?
Evidence: With a worldwide box-office gross of over $2.9 billion, Avatar is proclaimed to be the "highest-grossing" film, but such claims usually refer to theatrical revenues only and do not take into account home video and television income, which can form a significant portion of a film's earnings. Once revenue from home entertainment is factored in it is not immediately clear which film is the most successful. Titanic earned $1.2 billion from video and DVD sales and rentals, in addition to the $2.2 billion it grossed in theatres. While complete sales data are not available for Avatar, it earned $345 million from the sale of sixteen million DVD and Blu-ray units in North America, and ultimately sold a total of thirty million DVD and Blu-ray units worldwide. After home video income is accounted for, both films have earned over $3 billion each. Television broadcast rights will also substantially add to a film's earnings, with a film often earning as much as 20–25% of its theatrical box office for a couple of television runs on top of pay-per-view revenues; Titanic earned a further $55 million from the NBC and HBO broadcast rights, equating to about 9% of its North American gross.
Answer: Avatar is the highest-grossing film worldwide, grossing over $2.9B. It was written and directed by James Cameron, and was released in 2009.
Question: What is the philosophy of the movie Up?
Answer: Ellie is clearly a lot more adventurous than Carl. In fact that is clear in the first few minutes. To Carl, "Spirit of Adventure" is a balloon and for her its an abandoned house. The size, I think, matters because that is the size of the spirit of adventure.

Another thing alluding to her spirit of adventure is the loss of the baby and her subsequent rebounding from the sadness. She takes this grave loss as an adventure in her book. Carl is a good husband to point out that she needs to fulfill her "Things I am going to do" promise to herself, but does not realize how Ellie views the loss of the baby.

Carl is stuck on the aspect of having the house on Paradise island. He thinks that Ellie never lived her adventures and never added to her book since her first wish never got fulfilled. 

When an adventure presents itself in the form of Kevin, Carl, still attached to the past, prefers to fulfill the perceived wish by Ellie. When he does get the home on the water fall, it is clear that he is not satisfied. He can say that the dog nor Kevin are his problems. But when he sits down in his beloved chair, and looks through Ellie book of adventure again, he realizes that Ellie lived her adventures. She had no regrets. Her life was an adventure. And just at the right time, Carl sees the message from her to live his own adventure, i.e. Kevin.

Things become clearer for Carl. He is absolved of his guilt. He realizes that Ellie never felt bad about not being able to have her home on Paradise island. This moment of clarity is what all humans crave for. With this clarity, it was easy for him to be rid of all his attachments. He left behind his and his wife's chairs that he is clearly very attached to.

The message here, at least for the adults to discern and then communicate to the kids is to live your life. Yes you have dreams. But in day to day life, those dreams may not be achieved. Look for adventure. Look to live fully and without attachment to your dreams like Ellie did. Because, then, in living this full life, it just might be that your dream comes true. It just might be that the house is on Paradise island even without you trying like in the end of the movie!
Question: How do you make money?
Answer: There are many ways to make money.  You can apply for job and perform a service that pays money.  If you have a talent you might be able monetize that talent through performance like acting in movies or tutoring students.  If you are creative and build and make things you could sell those things for money like furniture or custom artwork.  If you still don't know how to make money then going back to school might be an option to learn a new trade skill such as welding, construction, or working on cars and boats.
Question: What is the city of Seattle known for?
Answer: Seattle is located in the northwest corner of the United States and is known for its overcast and rainy weather. Seattle has a strong coffee culture, in part because Starbucks was started there. The surrounding Cascade mountains and Puget Sound provide plenty of outdoor adventures, such as boating, rock climbing, and hiking. Many famous musicians started in Seattle, including Nirvana and Macklemore.
Question: What's one way biotechnology was used in world war one?
Evidence: In the early twentieth century scientists gained a greater understanding of microbiology and explored ways of manufacturing specific products. In 1917, Chaim Weizmann first used a pure microbiological culture in an industrial process, that of manufacturing corn starch using Clostridium acetobutylicum, to produce acetone, which the United Kingdom desperately needed to manufacture explosives during World War I.
Answer: In 1917, Chaim Weizmann first used a pure microbiological culture in an industrial process, that of manufacturing corn starch using Clostridium acetobutylicum, to produce acetone, which the United Kingdom desperately needed to manufacture explosives during World War I.
Question: What does the dataset consist of?
Evidence: We study human-human negotiations in the buyer-seller bargaining scenario, which has been a key research area in the literature BIBREF0. In this section, we first describe our problem setup and key terminologies by discussing the dataset used. Later, we formalize our problem definition.
Dataset: For our explorations, we use the Craigslist Bargaining dataset (CB) introduced by BIBREF4. Instead of focusing on the previously studied game environments BIBREF5, BIBREF6, the dataset considers a more realistic setup: negotiating the price of products listed on Craigslist. The dataset consists of 6682 dialogues between a buyer and a seller who converse in natural language to negotiate the price of a given product (sample in Table TABREF1). In total, 1402 product ad postings were scraped from Craigslist, belonging to six categories: phones, bikes, housing, furniture, car and electronics. Each ad posting contains details such as Product Title, Category Type and a Listing Price. Moreover, a secret target price is also pre-decided for the buyer. The final price after the agreement is called the Agreed Price, which we aim to predict.
Defining the problem: Say we are provided with a product scenario $S$, a tuple: (Category, Title, Listing Price, Target Price). Define the interactions between a buyer and seller using a sequence of $n$ events $E_n:<e_{1}, e_{2}, ..., e_{n}>$, where $e_{i}$ occurs before $e_{j}$ iff $i<j$. Event $e_{i}$ is also a tuple: (Initiator, Type, Data). Initiator is either the Buyer or Seller, Type can be one of (message, offer, accept, reject or quit) and Data consists of either the corresponding natural language dialogue, offer price or can be empty. Nearly $80\%$ of events in CB dataset are of type `message', each consisting a textual message as Data. An offer is usually made and accepted at the end of each negotiation. Since the offers directly contain the agreed price (which we want to predict), we only consider `message' events in our models. Given the scenario $S$ and first $n$ events $E_n$, our problem is then to learn the function $f_{n}$: $A = f_{n}(S, E_n)$ where $A$ refers to the final agreed price between the two negotiating parties.

The dataset consists of 6682 dialogues (9.2 turns on average) between buyers and sellers who converse to negotiate the price of a given product.
Answer: The dataset consists of 6682 dialogues (9.2 turns on average) between buyers and sellers who converse to negotiate the price of a given product.
Question: What is the performance achieved on NarrativeQA?
Evidence: FLOAT SELECTED: Table 5: Performance of our and competing models on the NarrativeQA test set. aSeo et al. (2017); bTay et al. (2018); cBauer et al. (2018); dIndurthi et al. (2018); eHu et al. (2018). fResults on the NarrativeQA validation set.
FLOAT SELECTED: Table 5: Performance of our and competing models on the NarrativeQA test set. aSeo et al. (2017); bTay et al. (2018); cBauer et al. (2018); dIndurthi et al. (2018); eHu et al. (2018). fResults on the NarrativeQA validation set.
Answer: Bleu-1: 54.11, Bleu-4: 30.43, METEOR: 26.13, ROUGE-L: 59.87
Question: Is the ARAML with a smaller standard deviation more stable than other GAN baselines?
Evidence: Dialogue evaluation is an open problem and existing works have found that automatic metrics have low correlation to human evaluation BIBREF35 , BIBREF36 , BIBREF37 . Thus, we resorted to manual evaluation to assess the generation quality on WeiboDial. We randomly sampled 200 posts from the test set and collected the generated results from all the models. For each pair of responses (one from ARAML and the other from a baseline, given the same input post), five annotators were hired to label which response is better (i.e. win, lose or tie) in terms of grammaticality (whether a response itself is grammatical and logical) and relevance (whether a response is appropriate and relevant to the post). The two metrics were evaluated independently.
The evaluation results are shown in Table TABREF35 . To measure the inter-annotator agreement, we calculated Fleiss' kappa BIBREF38 for each pair-wise comparison where results show moderate agreement ( INLINEFORM0 ). We also conducted sign test to check the significance of the differences.
As shown in Table TABREF35 , ARAML performs significantly better than other baselines in all the cases. This result indicates that the samples surrounding true responses provide stable rewards for the generator, and stable RAML training paradigm significantly enhances the performance in both metrics.
We can see that our model with smaller standard deviation is more stable than other GAN baselines in both metrics.
Answer: Yes, it is.
Question: What type of terrain is a gravel bike used for?
Evidence: A gravel bicycle is a type of bicycle intended for gravel cycling, including gravel racing. They are also sometimes known as "adventure bicycles", particularly ones intended for harsher off-road terrain.

While bicycles have been used for riding on such roads since bicycles were invented, the "modern" gravel bicycle, as a category, evolved in the 2000s, adopting technology from road bicycles, cyclocross bicycles and mountain bikes. They also share many characteristics of touring bicycles, such as relaxed geometry, wide tires and wide-range gearing.
Answer: Gravel bikes are a do-it-all kind of bike. They are effective on a wide array of terrain, ranging from smooth roads to singletrack trails, but thrive on harsher off-road terrain.
Question: What is HTML?
Answer: The HyperText Markup Language (HTML) is the standard markup language for creating web pages.