DataHammer/scimrc huggingface dataset:
Question: Is it a neural model? How is it trained?
Evidence: Here we describe the components of our probabilistic model of question generation. Section "Compositionality and computability" describes two key elements of our approach, compositionality and computability, as reflected in the choice to model questions as programs. Section "A grammar for producing questions" describes a grammar that defines the space of allowable questions/programs. Section "Probabilistic generative model" specifies a probabilistic generative model for sampling context-sensitive, relevant programs from this space. The remaining sections cover optimization, the program features, and alternative models (Sections "Optimization" - "Alternative models" ).
The details of optimization are as follows. First, a large set of 150,000 questions is sampled in order to approximate the gradient at each step via importance sampling. Second, to run the procedure for a given model and training set, we ran 100,000 iterations of gradient ascent at a learning rate of 0.1. Last, for the purpose of evaluating the model (computing log-likelihood), the importance sampler is also used to approximate the normalizing constant in Eq. 14 via the estimator $Z \approx \mathbb {E}_{x\sim q}[\frac{p(x;\mathbf {\theta })}{q(x)}]$ .
Here we describe the components of our probabilistic model of question generation. 
The details of optimization are as follows. First, a large set of 150,000 questions is sampled in order to approximate the gradient at each step via importance sampling. Second, to run the procedure for a given model and training set, we ran 100,000 iterations of gradient ascent at a learning rate of 0.1.
Answer: No, it is a probabilistic model trained by finding feature weights through gradient ascent
Question: What is the use of the dataset created by the authors?
Evidence: In this paper we have presented a new dataset containing discharge summaries and nursing progress notes, focusing on frequently readmitted patients and high-context social determinants of health, and originating from a large tertiary care hospital. Each patient note was annotated by at least one clinical researcher and one resident physician for 13 high-context patient phenotypes.
Phenotype definitions, dataset distribution, patient note statistics, inter-operator error, and the results of baseline models were reported to demonstrate that the dataset is well-suited for the development of both rule-based and statistical models for patient phenotyping. We hope that the release of this dataset will accelerate the development of algorithms for patient phenotyping, which in turn would significantly help medical research progress faster.
In this paper we have presented a new dataset containing discharge summaries and nursing progress notes, focusing on frequently readmitted patients and high-context social determinants of health, and originating from a large tertiary care hospital. Each patient note was annotated by at least one clinical researcher and one resident physician for 13 high-context patient phenotypes. Phenotype definitions, dataset distribution, patient note statistics, inter-operator error, and the results of baseline models were reported to demonstrate that the dataset is well-suited for the development of both rule-based and statistical models for patient phenotyping. We hope that the release of this dataset will accelerate the development of algorithms for patient phenotyping, which in turn would significantly help medical research progress faster.
Answer: The dataset is well-suited for the development of both rule-based and statistical models for patient phenotyping.
Question: How many rules had to be defined?
Evidence: We describe our rules for WikiSQL here. We first detect WHERE values, which exactly match to table cells. After that, if a cell appears at more than one column, we choose the column name with more overlapped words with the question, with a constraint that the number of co-occurred words is larger than 1. By default, a WHERE operator is INLINEFORM0 , except for the case that surrounding words of a value contain keywords for INLINEFORM1 and INLINEFORM2 . Then, we deal with the SELECT column, which has the largest number of co-occurred words and cannot be same with any WHERE column. By default, the SELECT AGG is NONE, except for matching to any keywords in Table TABREF8 . The coverage of our rule on training set is 78.4%, with execution accuracy of 77.9%.
Our rule for KBQA is simple without using a curated mapping dictionary. First, we detect an entity from the question using strict string matching, with the constraint that only one entity from the KB has the same surface string and that the question contains only one entity. After that, we get the connected relations of the detected entity, and assign the relation as the one with maximum number of co-occurred words. The coverage of our rule on training set is 16.0%, with an accuracy of 97.3% for relation prediction.
The pipeline of rules in SequentialQA is similar to that of WikiSQL. Compared to the grammar of WikiSQL, the grammar of SequentialQA has additional actions including copying the previous-turn logical form, no greater than, no more than, and negation. Table TABREF23 shows the additional word-level mapping table used in SequentialQA. The coverage of our rule on training set is 75.5%, with an accuracy of 38.5%.
We describe our rules for WikiSQL here. We first detect WHERE values, which exactly match to table cells. After that, if a cell appears at more than one column, we choose the column name with more overlapped words with the question, with a constraint that the number of co-occurred words is larger than 1. By default, a WHERE operator is INLINEFORM0 , except for the case that surrounding words of a value contain keywords for INLINEFORM1 and INLINEFORM2 . Then, we deal with the SELECT column, which has the largest number of co-occurred words and cannot be same with any WHERE column. By default, the SELECT AGG is NONE, except for matching to any keywords in Table TABREF8 . The coverage of our rule on training set is 78.4%, with execution accuracy of 77.9%.
Our rule for KBQA is simple without using a curated mapping dictionary.
The pipeline of rules in SequentialQA is similar to that of WikiSQL. Compared to the grammar of WikiSQL, the grammar of SequentialQA has additional actions including copying the previous-turn logical form, no greater than, no more than, and negation.
Answer: WikiSQL - 2 rules (SELECT, WHERE)
SimpleQuestions - 1 rule
SequentialQA - 3 rules (SELECT, WHERE, COPY)
Question: Can I copy my Delta Lake table to another location?
Answer: Yes you can copy your Delta Lake table to another location. Remember to copy files without changing the timestamps to ensure that the time travel with timestamps will be consistent.
Question: What is the meaning of life?
Answer: Although it is a deep question that has challenged philosophers for all time, Douglas Adams in his book "The Hitchhicker's Guide to the Universe" humorously states that the answer is "42".
Question: Why we have to work?
Answer: Work is the means by which most people exchange their labor for income, and it is also how society efficiently allocates labor resources to where they are needed. Additionally, work helps individuals maintain their daily schedules.
Question: Give me a bulleted list of the last 5 Super Bowl winning teams
Answer: Kansas City Chiefs
Los Angeles Rams
Tampa Bay Buccaneers
Kansas City Chiefs 
New England Patriots
Question: Why am I so sad all the time?
Answer: I'm not sure. I don't know the full extent of your circumstances. What I do know is that you can seek guidance and support from friends and family, and if you don't feel like you have someone close to rely on or turn to, there are many resources available for those experiencing depression, feeling isolation, or generally struggling with mental health. I encourage you to see what resources are available to you and seek professional help — sometimes talking to someone (beyond a generative AI model like me) can help.
Question: Classify each of the following as either even or odd number: 1, 3, 15, 24, 56, 47, 4, 88, 13, 10, 74, 35, 99, 82, 6, 59, 73, 12, 68, 9.
Answer: Odd numbers are numbers that cannot be divided exactly by 2. They include: 1, 3, 9, 13, 15, 35, 47, 59, 73, 99.
Even numbers are numbers that can be divided by 2. They include: 4, 6, 10, 12, 24, 56, 68, 74, 82, 88.
Question: I need to move a large piece of furniture out of my second-story apartment. I assembled it in my apartment and would rather not disassemble it. What can I do?
Answer: If it's not too heavy, you might be able to lower it over the balcony of your apartment to some helpers at the street level below.
Question: Create a dialogue between two characters discussing the impact of social media on their lives. Your dialogue should explore both the positive and negative effects of social media on society.
Answer: Person 1 (P1): I feel so anxious nowadays. Especially, when I open these social media apps.
Person 2 (P2): Oh no, I am sorry to hear. I actually really enjoy these apps. They help me destress.
P1: That is nice. I am glad it is working for you. I wonder if I am unable to cancel all this noise and the endless pressure does not help. I find myself comparing with other people on the platform; how they are vacationing while I am grinding at work or how they are able to run marathons and do pull ups while I am struggling to even climb 10 stairs
P2: That is so true. I remember having this conversation with a friend of mine a few months ago. Then I figured, if I can follow pages that makes me feel good and if I interact with those pages more.. my feed will improve. It has worked well for me. You can actually use these platforms to keep yourself motivated.
P1: That sounds motivating. I fear I might waste a lot of time on these.
P2: That is a valid concern. I set up app timers to ensure I am not on it more than 20 minutes. May be try that?
P2: I will try that!
Question: Tell me about Cris Gunther
Evidence: Clarence Richard Adams (born July 6, 1974) is an American former professional boxer. He has won a world title in the Super Bantamweight weight division.

Professional career
Adams turned pro in 1990, at the age of 16. In 2000 defeated Néstor Garza for the WBA super bantamweight title by unanimous decision. In his first defense he beat Andres Fernandez by 6th-round TKO. The fight ended when the referee stopped the fight and the ringside physician, Armando Sanchez, determined that Fernandez could not continue due to cuts.

Adams defended the title one more time before vacating to fight Paulie Ayala. He lost the bout by split decision in 2001, and also lost a rematch in 2002. Adams retired in 2003 after drawing with journeyman Manuel Sepeda, but returned to boxing in 2006. On June 12, 2009, Adams defeated veteran Alex "Ali" Baba in an 8th-round TKO.

His last match was in 2010, a fourth-round technical knockout loss to Edel Ruiz, which brought Adams’ record to 44-7-4 with 20 knockouts and 1 no contest.

In 2015, Adams was ordered to spend half a year behind bars after pleading guilty to being part of a group of people involved with drug trafficking, fraud and prostitution. Adams, who was a driver for the limousine company accused of the criminal activity, “admitted to playing a small role in the prostitution and drug dealing portions of the scheme.”

Adams now owns Bones Adams Gym in Las Vegas, a boxing training center. He played a key role in helping Blair Cobbs becoming a professional boxer. As of September 2021, Adams is now training Amir Khan's first-ever signing and protege, Tal Singh, a former England amateur champion, who he is hoping to guide towards a historic world title triumph. Adams also trains Shane Mosley Jr. in his Las Vegas gym.
Answer: Cris Gunther is an American singer-songwriter who won the Best Message Song / Social Impact award from the Hollywood Music in Media Awards. He was born in Beckley, West Virginia and began his career in nightclubs and talent competitions. He attended Marymount Manhattan College and wrote and recorded music demos before performing in a Songwriters Hall Of Fame Showcase in New York City. Cris Gunther released his debut album, Fall into the Open, in 2007. He performed seventeen shows for his debut tour and reached No. 1 on Reverbnation's Pop Chart and the Brazilian national Top 5. His song "They Need Love" was rated No. 1 for three weeks in June 2015 on ISINA. In 2017, Gunther was selected to compete in the ISINA competition, and his song "Compass Love" was released for streaming by ISINA Music/Warner Bros. Records.
Question: How to play chess
Evidence: The rules of chess (also known as the laws of chess) govern the play of the game of chess. Chess is a two-player abstract strategy board game. Each player controls sixteen pieces of six types on a chessboard. Each type of piece moves in a distinct way. The object of the game is to checkmate (threaten with inescapable capture) the opponent's king. A game can end in various ways besides checkmate: a player can resign, and there are several ways a game can end in a draw.

While the exact origins of chess are unclear, modern rules first took form during the Middle Ages. The rules continued to be slightly modified until the early 19th century, when they reached essentially their current form. The rules also varied somewhat from region to region. Today, the standard rules are set by FIDE (Fédération Internationale des Échecs), the international governing body for chess. Slight modifications are made by some national organizations for their own purposes. There are variations of the rules for fast chess, correspondence chess, online chess, and Chess960.

Besides the basic moves of the pieces, rules also govern the equipment used, time control, conduct and ethics of players, accommodations for physically challenged players, and recording of moves using chess notation. Procedures for resolving irregularities that can occur during a game are provided as well.
Answer: To win the game of chess, a player must capture the opponent's king. This is known as "Checkmating" the king.
Question: Why this is the foremost fundamental step for developing Vietnamese Image Captioning?
Evidence: Image Captioning, the task of automatic generation of image captions, has attracted attentions from researchers in many fields of computer science, being computer vision, natural language processing and machine learning in recent years. This paper contributes to research on Image Captioning task in terms of extending dataset to a different language - Vietnamese. So far, there is no existed Image Captioning dataset for Vietnamese language, so this is the foremost fundamental step for developing Vietnamese Image Captioning. In this scope, we first build a dataset which contains manually written captions for images from Microsoft COCO dataset relating to sports played with balls, we called this dataset UIT-ViIC. UIT-ViIC consists of 19,250 Vietnamese captions for 3,850 images. Following that, we evaluate our dataset on deep neural network models and do comparisons with English dataset and two Vietnamese datasets built by different methods. UIT-ViIC is published on our lab website for research purposes.
So far, there is no existed Image Captioning dataset for Vietnamese language, so this is the foremost fundamental step for developing Vietnamese Image Captioning.
Answer: Because there is no existed Image Captioning dataset for Vietnamese language.
Question: What is the metric used with WIKIHOP?
Evidence: FLOAT SELECTED: Table 2: Accuracy of different models on WIKIHOP closed test set and public validation set. Our Entity-GCN outperforms recent prior work without learning any language model to process the input but relying on a pretrained one (ELMo – without fine-tunning it) and applying R-GCN to reason among entities in the text. * with coreference for unmasked dataset and without coreference for the masked one.
FLOAT SELECTED: Table 2: Accuracy of different models on WIKIHOP closed test set and public validation set. Our Entity-GCN outperforms recent prior work without learning any language model to process the input but relying on a pretrained one (ELMo – without fine-tunning it) and applying R-GCN to reason among entities in the text. * with coreference for unmasked dataset and without coreference for the masked one.
Answer: The answers are shown as follows:
* Accuracy
Question: Give me a list of 5 neglected tropical diseases
Answer: Ascariasis,
Lymphatic filariasis,
Trypanosomiasis,
Leishmaniasis,
Onchocerciasis
Question: You are planning a trip and want to travel only in the Southern Hemisphere in the first half, and only the Northern Hemisphere in the second half. Sort the countries in your itinerary into 'Southern Hemisphere' and 'Northern Hemisphere'. Your itinerary includes: Fiji, Brazil, Australia, China, Argentina, India, Canada, and South Africa.
Answer: Most countries are in the Northern Hemisphere. These include: Brazil, China, India and Canada. Countries in the Southern Hemisphere are: Australia, South Africa, Argentina and Fiji.
Question: What language pair do the authors investigate?
Evidence: Neural machine translation has recently achieved impressive results BIBREF0 , BIBREF1 , while learning from raw, sentence-aligned parallel text and using little in the way of external linguistic information. However, we hypothesize that various levels of linguistic annotation can be valuable for neural machine translation. Lemmatisation can reduce data sparseness, and allow inflectional variants of the same word to explicitly share a representation in the model. Other types of annotation, such as parts-of-speech (POS) or syntactic dependency labels, can help in disambiguation. In this paper we investigate whether linguistic information is beneficial to neural translation models, or whether their strong learning capability makes explicit linguistic features redundant.
Let us motivate the use of linguistic features using examples of actual translation errors by neural MT systems. In translation out of English, one problem is that the same surface word form may be shared between several word types, due to homonymy or word formation processes such as conversion. For instance, close can be a verb, adjective, or noun, and these different meanings often have distinct translations into other languages. Consider the following English INLINEFORM0 German example:
For the English source sentence in Example SECREF4 (our translation in Example SECREF5 ), a neural MT system (our baseline system from Section SECREF4 ) mistranslates close as a verb, and produces the German verb schließen (Example SECREF6 ), even though close is an adjective in this sentence, which has the German translation nah. Intuitively, part-of-speech annotation of the English input could disambiguate between verb, noun, and adjective meanings of close.
As a second example, consider the following German INLINEFORM0 English example:
German main clauses have a verb-second (V2) word order, whereas English word order is generally SVO. The German sentence (Example UID7 ; English reference in Example UID8 ) topicalizes the predicate gefährlich 'dangerous', putting the subject die Route 'the route' after the verb. Our baseline system (Example UID9 ) retains the original word order, which is highly unusual in English, especially for prose in the news domain. A syntactic annotation of the source sentence could support the attentional encoder-decoder in learning which words in the German source to attend (and translate) first.
We will investigate the usefulness of linguistic features for the language pair German INLINEFORM0 English, considering the following linguistic features:
The inclusion of lemmas is motivated by the hope for a better generalization over inflectional variants of the same word form. The other linguistic features are motivated by disambiguation, as discussed in our introductory examples.
We will investigate the usefulness of linguistic features for the language pair German↔English, considering the following linguistic features: The inclusion of lemmas is motivated by the hope for a better generalization over inflectional variants of the same word form.
Answer: German↔English.
Question: What were Ansel Adams key achievements?
Evidence: Ansel Easton Adams (February 20, 1902 – April 22, 1984) was an American landscape photographer and environmentalist known for his black-and-white images of the American West. He helped found Group f/64, an association of photographers advocating "pure" photography which favored sharp focus and the use of the full tonal range of a photograph. He and Fred Archer developed a system of image-making called the Zone System, a method of achieving a desired final print through a technical understanding of how the tonal range of an image is the result of choices made in exposure, negative development, and printing.
Adams was a life-long advocate for environmental conservation, and his photographic practice was deeply entwined with this advocacy. At age 12, he was given his first camera during his first visit to Yosemite National Park. He developed his early photographic work as a member of the Sierra Club. He was later contracted with the United States Department of the Interior to make photographs of national parks. For his work and his persistent advocacy, which helped expand the National Park system, he was awarded the Presidential Medal of Freedom in 1980.
Answer: Ansel Adams was a famous landscape photographer and environmentalist. His photography focused on black-and-white photos of the American West. He garnered critical acclaim for his technical understanding of imaging along with being a vocal advocate for environmental conservation. He won the Presidential Medal of Freedom for helping to expand the National Park System through his photography.