DataHammer/scimrc huggingface dataset:
Question: As a firefighter, you commonly have the following items on you: ax, helmet, pants, jacket, oxygen tank, mask, gloves, boots, hose, shovel, light. Please tell me which of these you wear and which you carry.
Answer: wear: helmet, pants, jacket, mask, gloves, boots
carry:  ax, oxygen tank, hose, shovel, light
Question: What are some typical characters of Siamese cats?
Answer: Siamese cats tend to have slender, long bodies with apple-shaped heads and large ears. They can come in a variety of colors, such as chocolate point, seal point, and orange flame. Siamese owners claim that Siamese cats tend to be more loyal to their favorite human and are dog-like in their playful nature, such as enjoying playing fetch. Siamese also are a vocal breed and meow loudly and often.
Question: Why Cancun is famous
Answer: Cancun has some best resorts in the World with all inclusive food and beverage with Private beach
Question: The comparison experiments only compare the proposed approach with one baseline. Are there other methods in the setting?
Evidence: We use two challenging physical models to experimentally evaluate our proposed approach. To analyze our method and to compare to previous work, we first consider synthetically created data. Afterwards, we show that our method achives promising results also on real-world data. For details about the ODEs describing the dynamics, additional implementation details, an ablation study, as well as additional results we refer the reader to the Appendix.
Although several learning-based approaches that infer physical models from image data have been proposed (de Avila, existing approaches are particularly tailored towards settings with large training corpora. However, these methods typically suffer from a decreasing estimation accuracy in scarce training data regimes, or if out of distribution generalization is required (cf. App. F).
In contrast, our proposed approach is able to predict physical parameters from a single short video clip. Due to the lack of existing baselines tailored towards estimation from a single video, we adapt the recent work of Jaques et al. () and to act as baseline methods.
Due to the lack of existing baselines tailored towards estimation from a single video, we adapt the recent work of Jaques et al. (2020) and Zhong & Leonard (2020) to act as baseline methods.
Answer: By comparing against the method proposed by Jaques et al. [1] we include another baseline as reference.
Question: Name some TV shows that American actress Gillian Anderson has been in.
Answer: The X-Files, The Crown, Sex Education, Hannibal, The Fall, The First Lady, The Great
Question: Given a reference text about Giovanni Dominici, when was he beatified?
Evidence: Giovanni Dominici (c. 1355 – 10 June 1419) was an Italian Catholic prelate and Dominican who became a cardinal. His ideas had a profound influence on the art of Fra Angelico who entered the order through him. But he once encountered difficulties becoming a friar due to a speech impairment that his superiors believed would rule him ineligible for both profession and the priesthood. Dominici became a noted theologian and preacher and was tireless in establishing monasteries and convents in cities such as Fiesole and Lucca.

He attempted to resign his cardinalate in 1415 during the Council of Constance after he succeeded in convincing the pope to abdicate in order to end the Western Schism. But the Council refused to accept his resignation though he had resigned from the archbishopric that he held. He spent the remainder of his life as a papal legate for Pope Martin V until he died in Buda.

He had been first named as a Blessed since 1622 though he had not been recognized as such until he was beatified on 9 April 1832.[
Answer: Giovanni Dominici was beatified on April 9, 1832.
Question: What measure of semantic similarity is used?
Evidence: In the vector space model approach, we follow the sparse context-based disambiguated method BIBREF12 , BIBREF13 . For estimating the sense of the word INLINEFORM0 in a sentence, we search for such a synset INLINEFORM1 that maximizes the cosine similarity to the sentence vector: DISPLAYFORM0
For estimating the sense of the word INLINEFORM0 in a sentence, we search for such a synset INLINEFORM1 that maximizes the cosine similarity to the sentence vector: DISPLAYFORM0
Answer: The answers are shown as follows:
* cosine similarity
Question: What career did  GeLing Yan have?
Evidence: Geling Yan (simplified Chinese: 严歌苓; traditional Chinese: 嚴歌苓; pinyin: Yán Gēlíng; born 16 November 1958]) is a Chinese-American author and screenwriter. Yan began performing as a dancer at age 12. She served in the People's Liberation Army in Chengdu, during the Cultural Revolution in Tibet and later as a journalist in the Sino-Vietnamese War, achieving a rank equivalent to lieutenant colonel.
Answer: She is an author, screenwriter, and once was a dancer and a journalist.
Question: Given a reference text about spaghetti, what shape is it, where did it originate, and are there any variations of it?
Evidence: Spaghetti is a long, thin, solid, cylindrical pasta. It is a staple food of traditional Italian cuisine. Like other pasta, spaghetti is made of milled wheat, water, and sometimes enriched with vitamins and minerals. Italian spaghetti is typically made from durum-wheat semolina. Usually the pasta is white because refined flour is used, but whole wheat flour may be added. Spaghettoni is a thicker form of spaghetti, while spaghettini is a thinner form. Capellini is a very thin spaghetti, sometimes known colloquially as "angel hair pasta".
Answer: The shape of spaghetti is long, thin, and cylindrical. It traditionally comes from Italy and has a two variations - those being Spaghettoni, which is thicker, and capellini, which is thiner and sometime called 'angel hair pasta'.
Question: Who are some students at Hogwarts in Hufflepuff house that were in the same year as Harry Potter?
Answer: Hogwarts is the fictitious school in the Harry Potter book series. The books mentions several students in Harry Potter's year from the Hufflepuff house, such as Megan Jones, Susan Bones, Ernie Macmilian, Zacharias Smith, and Hannah Abbott.
Question: What are the results of the author's experiment compared with those of Wang et al.?
Evidence: Minimal pre-processing was done by converting text to lower case, removing the hashtags at the end of tweets and separating each punctuation from the connected token (e.g., awesome!! INLINEFORM0 awesome !!) and replacing comma and new-line characters with white space. The text, then, was tokenized using TensorFlow-Keras tokenizer. Top N terms were selected and added to our dictionary where N=100k for higher count emotions joy, sadness, anger, love and N=50k for thankfulness and fear and N=25k for surprise. Seven binary classifiers were trained for the seven emotions with a batch size of 250 and for 20 epochs with binary cross-entropy as the objective function and Adam optimizer. The architecture of the model can be seen in Figure FIGREF6 . For training each classifier, a balanced dataset was created with selecting all tweets from the target set as class 1 and a random sample of the same size from other classes as class 0. For each classifier, 80% of the data was randomly selected as the training set, and 10% for the validation set, and 10% as the test set. As mentioned before we used the two embedding models, ConceptNet Numberbatch and fastText as the two more modern pre-trained word vector spaces to see how changing the embedding layer can affect the performance. The result of comparison among different embeddings can be seen in Table TABREF10 . It can be seen that the best performance was divided between the two embedding models with minor performance variations.
The comparison of our result with Wang et al. can be seen in Table TABREF9 . as shown, the results from our model shows significant improvement from 10% increase in F-measure for a high count emotion joy up to 61.7 point increase in F-measure for a low count emotion surprise. on average we showed 26.8 point increase in F-measure for all categories and more interestingly our result shows very little variance between different emotions compare to results reported by Wang et al.
The comparison of our result with Wang et al. can be seen in Table 4. as shown, the results from our model shows significant improvement from 10% increase in F-measure for a high count emotion joy up to 61.7 point increase in Fmeasure for a low count emotion surprise.
Answer: The results from their model shows significant improvement from 10% increase in F-measure for a high count emotion joy up to 61.7 point increase in Fmeasure for a low count emotion surprise.
Question: What should be the basis of predicting an implicit discourse relation?
Evidence: Previous work BIBREF1 , BIBREF2 , BIBREF10 has revealed that recognizing explicit vs. implicit discourse relations requires different strategies. Note that in the PDTB dataset, explicit discourse relations were distinguished from implicit ones, depending on whether a discourse connective exists between two discourse units. Therefore, explicit discourse relation detection can be simplified as a discourse connective phrase disambiguation problem BIBREF1 , BIBREF2 . On the contrary, predicting an implicit discourse relation should rely on understanding the overall contents of its two discourse units BIBREF2 , BIBREF10 .
Considering the different natures of explicit vs. implicit discourse relation prediction, we decide to untie parameters at the final discourse relation prediction layer and train two softmax classifiers, as illustrated in Figure 2 . The two classifiers have different sets of parameters, with one classifier for only implicit discourse relations and the other for only explicit discourse relations.
$$y_{t-1} =
{\left\lbrace \begin{array}{ll}
softmax(W_{exp}[hDU_{t-1},hDU_t]+b_{exp}),&exp\\
softmax(W_{imp}[hDU_{t-1},hDU_t]+b_{imp}),&imp
\end{array}\right.}$$   (Eq. 12) 
The loss function used for the neural network training considers loss induced by both implicit relation prediction and explicit relation prediction:
$$Loss = Loss_{imp} + \alpha *Loss_{exp}$$   (Eq. 13) 
The $\alpha $ , in the full system, is set to be 1, which means that minimizing the loss in predicting either type of discourse relations is equally important. In the evaluation, we will also evaluate a system variant, where we will set $\alpha = 0$ , which means that the neural network will not attempt to predict explicit discourse relations and implicit discourse relation prediction will not be influenced by predicting neighboring explicit discourse relations.
On the contrary, predicting an implicit discourse relation should rely on understanding the overall contents of its two discourse units (Lin et al., 2014; Rutherford and Xue, 2016).
Answer: Predicting an implicit discourse relation should rely on understanding the overall contents of its two discourse units.
Question: What are the performance metrics used?
Evidence: As a convention, the metric of joint goal accuracy is used to compare our model to previous work. The joint goal accuracy only regards the model making a successful belief state prediction if all of the slots and values predicted are exactly matched with the labels provided. This metric gives a strict measurement that tells how often the DST module will not propagate errors to the downstream modules in a dialogue system. In this work, the model with the highest joint accuracy on the validation set is evaluated on the test set for the test joint accuracy measurement.
As a convention, the metric of joint goal accuracy is used to compare our model to previous work.
Answer: The answers are shown as follows:
* joint goal accuracy
Question: What is a Peloton in a bicycle race?
Answer: The peloton in a bicycle race is the group of riders all riding in a tightly grouped formation.  Riding in a group allows riders to save energy by drafting behind riders who are in the front.  When an individual or small group attempts to ride away in front of the peloton it is referred to as a break away.
Question: Why should you get out of your comfort zone?
Answer: Being uncomfortable becomes easier when you realize that’s literally the point. It’s like working out. If you work out & it becomes easy for you, you already got all you could out of that workout. Your body adjusted & is strong enough to handle it now. You’re not improving anymore. That’s why you up the ante with harder workouts, where you’re uncomfortable & not quite strong enough yet. Because eventually you will be strong enough. But if it’s not a challenge, there’s no gain and that’s exactly how it is with situations out of your comfort zone. The more you put yourself in them, the easier they get—but that in-between phase where you’re struggling is still completely valid, ok and natural. It’s what’s supposed to happen and it’s what’s going to change you as a person. And you should keep doing it
Question: Given the reference text about the American Rescue Plan Act, tell me how much money was authorized to be spent.
Evidence: The American Rescue Plan Act of 2021, also called the COVID-19 Stimulus Package or American Rescue Plan, is a US$1.9 trillion economic stimulus bill passed by the 117th United States Congress and signed into law by President Joe Biden on March 11, 2021, to speed up the country's recovery from the economic and health effects of the COVID-19 pandemic and the ongoing recession. First proposed on January 14, 2021, the package builds upon many of the measures in the CARES Act from March 2020 and in the Consolidated Appropriations Act, 2021, from December.

Beginning on February 2, 2021, Democrats in the United States Senate started to open debates on a budget resolution that would allow them to pass the stimulus package through the process of reconciliation which would not require support from Republicans. The House of Representatives voted 218–212 to approve its version of the budget resolution. A so-called vote-a-rama session started two days later after the resolution was approved, and the Senate introduced amendments in the relief package. The day after, Vice President Kamala Harris cast her first tie-breaking vote as vice president in order to give the Senate's approval to start the reconciliation process, with the House following suit by voting 219–209 to agree to the Senate version of the resolution.

On February 8, 2021, the Financial Services and Education and Labor committees released a draft of $1.9 trillion stimulus legislation. A portion of the relief package was approved by the House Ways and Means on February 11, setting it up for a vote in the House. The legislation was also approved by the Transportation and Infrastructure, Small Business, and House Veterans Affairs committees. On February 22, the House Budget Committee voted 19–16 to advance the bill to the House for a floor vote. The bill passed the House by a vote of 219–212 on February 27. All but two Democrats voted for the bill and all Republicans voted against the bill. A modified version passed the Senate on March 6 by a vote of 50–49. The final amended bill was passed by the House on March 10 by a vote of 220–211 with one Democrat voting against it with all Republicans. The bill was signed into law by President Biden on March 11, 2021, which was the first anniversary of COVID-19 being declared a pandemic by the World Health Organization.
Answer: The American Rescue Plan authorized $1.9T of economic stimulus to be spent.
Question: How is the effectiveness of the model evaluated?
Evidence: Following the previous works, we report the precision ($P.$), recall ($R.$) and $F_1$ scores for target recognition and targeted sentiment. Note that a correct target prediction requires the boundary of the target to be correct, and a correct targeted sentiment prediction requires both target boundary and sentiment polarity to be correct.
Following the previous works, we report the precision ($P.$), recall ($R.$) and $F_1$ scores for target recognition and targeted sentiment. 
Answer: The answers are shown as follows:
* precision ($P.$), recall ($R.$) and $F_1$ scores for target recognition and targeted sentiment
Question: What did Wei advocate for?
Evidence: From an early age, Wei espoused the New Text school of Confucianism and became a vocal member of the statecraft school, which advocated practical learning in opposition to the allegedly barren evidentiary scholarship as represented by scholars like Dai Zhen. Among other things, Wei advocated sea transport of grain to the capital instead of using the Grand Canal and he also advocated a strengthening of the Qing Empire's frontier defense. In order to alleviate the demographic crisis in China, Wei also spoke in favor of large scale emigration of Han Chinese into Xinjiang.
Answer: Wei advocated for the following:
- practical learning in opposition to allegedly barren evidentiary scholarship as represented by scholars like Dai Zhen
- sea transport of grain to the capital instead of using the Grand Canal
- a strengthening of the Qing Empire's frontier defense
- large scale emigration of Han Chinese into Xinjiang
Question: What method was used to generate the OpenIE extractions?
Evidence: We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. For each test set, we use the corresponding training questions $Q_\mathit {tr}$ to retrieve domain-relevant sentences from S. Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).
For each test set, we use the corresponding training questions $Q_\mathit {tr}$ to retrieve domain-relevant sentences from S. Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).
Answer: The answers are shown as follows:
* for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S
* take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$
Question: Samuel Tilden, Grover Cleveland, Al Gore, and Hillary Clinton share what distinction among U.S. presidential candidates?
Answer: They won the popular vote but lost the electoral college vote
Question: Identify which instrument is string or percussion: Gbedu, Guitalele
Answer: Guitalele is string, Gbedu is percussion.
Question: Classify each of the objects are "round" or "rectangular": television, baseball, brick, tire, book, manhole, donut, bench
Answer: Television: Rectangular
Baseball: Round
Brick: Rectangular
Tire: Round
Book: Rectangular
Manhole: Round
Donut: Round 
Bench: Rectangular
Question: Who won the India’s highest literary award 56th Jnanpith award
Answer: Nilmani Phookan(Assamese Poet)
Question: What is the relationship between the co-voting and retweeting patterns?
Evidence: Overall, we observe a positive correlation between retweeting and co-voting, which is significantly different from zero. The strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets. Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union. The only exception, with a significantly negative coefficient, is the area Economic and monetary system. This implies that in the area Economic and monetary system we observe a significant deviation from the usual co-voting patterns. Results from section “sec:coalitionpolicy”, confirm that this is indeed the case. Especially noteworthy are the coalitions between GUE-NGL and Greens-EFA on the left wing, and EFDD and ENL on the right wing. In the section “sec:coalitionpolicy” we interpret these results as a combination of Euroscepticism on both sides, motivated on the left by a skeptical attitude towards the market orientation of the EU, and on the right by a reluctance to give up national sovereignty.
Overall, we observe a positive correlation between retweeting and co-voting, which is significantly different from zero. The strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets. Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union. The only exception, with a significantly negative coefficient, is the area Economic and monetary system. This implies that in the area Economic and monetary system we observe a significant deviation from the usual co-voting patterns.
Answer: The answers are shown as follows:
* we observe a positive correlation between retweeting and co-voting
* strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets
* Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union
* significantly negative coefficient, is the area Economic and monetary system
Question: In Growing Mixture of Experts, "Compared to Ens, MoE has exponentially many more components". Where does the "exponentially" come from?
Evidence: If we consider each path in the MoE model to be a different model, then with L layer of k MoE components, there are kL many possible paths through the MoE model, hence the number of paths grows exponentially with the number of layers. You can think of this as an ensemble with exponentially many components, but this is still tractable because components share parameters.
Answer: If we consider each “path” in the MoE model to be a different model, then with `L` layer of `k` MoE components, there are `k^L` many possible paths through the MoE model, hence the number of paths grows exponentially with the number of layers. You can think of this as an ensemble with exponentially many components, but this is still tractable because components share parameters. We have clarified this in Appendix A.
Question: what is the difference between ARM and x86 chipsets?
Answer: ARM uses Reduced Instruction Set Computing Architecture (RISC) whereas x86 uses Complex Instruction Set Computing Architecture (CISC). ARM executes single instruction per cycle whereas x86 executes complex instructions in multiple cycles. This allows softwares to be optimized for performance against ARM chipset as opposed to an 86 hardware approach to optimize. It would also be interesting to mention that ARM uses higher memory and less registers compared to x86.
Question: What’s a typical dish in Brazil?
Answer: Brazil has a wide variety of culinary options heavily based on meats, local fruits and fresh vegetables. One traditional dish is the “feijoada”, a black bean and sausage stew that is generally served with rice, yucca flower and oranges. Another popular dish is the so called “Picanha”, essentially the cap of the top sirloin.
Question: What is repetition?
Answer: Repetition is doing the same thing over and over again.  Repetition helps you get good at certain hobbies and activities that require a lot of practice.  Some of these activities can be riding a bike, bowling, running,  sports, gymnastics, or speaking another language.  Repetition is also used in school to help with test preparation in English and Mathematics.  Repetition literally means the repetition of words or the repeating meaning of a word.