DataHammer/scimrc huggingface dataset:
Question: List the five French mother sauces used in classic french cuisine.
Answer: List the five French mother sauces used in classic French cuisine. 
The five mother sauces include Béchamel, Velouté, Espagnole, hollandaise and Tomato.
Question: Which languages are used in the paper?
Evidence: For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8. The probability of a sequence of words is the product of the probabilities of each word, given the word’s context: the preceding word. As in the following equation:
For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8.
Answer: The answers are shown as follows:
* English
* Russian
Question: How can I spend more time with my dog?
Answer: You can play fetch.
Go on hikes with your dog.
You can train them.
Hide the treats and use snuggle mat to keep their brain and nose activity high.
You can go for leisurely long walks.
Go on a trip with them.
All these would make you spend more time with your pet and care for them and it will improve the bonding.
Question: what is the proposed Progressive Dynamic Hurdles method?
Evidence: To address this problem we formulated a method to dynamically allocate resources to more promising architectures according to their fitness. This method, which we refer to as progressive dynamic hurdles (PDH), allows models that are consistently performing well to train for more steps. It begins as ordinary tournament selection evolutionary architecture search with early stopping, with each child model training for a relatively small $s_0$ number of steps before being evaluated for fitness. However, after a predetermined number of child models, $m$ , have been evaluated, a hurdle, $h_0$ , is created by calculating the the mean fitness of the current population. For the next $m$ child models produced, models that achieve a fitness greater than $h_0$ after $s_0$ train steps are granted an additional $s_1$ steps of training and then are evaluated again to determine their final fitness. Once another $m$ models have been considered this way, another hurdle, $h_1$ , is constructed by calculating the mean fitness of all members of the current population that were trained for the maximum number of steps. For the next $m$ child models, training and evaluation continues in the same fashion, except models with fitness greater than $m$0 after $m$1 steps of training are granted an additional $m$2 number of train steps, before being evaluated for their final fitness. This process is repeated until a satisfactory number of maximum training steps is reached. Algorithm 1 (Appendix) formalizes how the fitness of an individual model is calculated with hurdles and Algorithm 2 (Appendix) describes tournament selection augmented with progressive dynamic hurdles.
This method, which we refer to as progressive dynamic hurdles (PDH), allows models that are consistently performing well to train for more steps. It begins as ordinary tournament selection evolutionary architecture search with early stopping, with each child model training for a relatively small $s_0$ number of steps before being evaluated for fitness. However, after a predetermined number of child models, $m$ , have been evaluated, a hurdle, $h_0$ , is created by calculating the the mean fitness of the current population. For the next $m$ child models produced, models that achieve a fitness greater than $h_0$ after $s_0$ train steps are granted an additional $s_1$ steps of training and then are evaluated again to determine their final fitness. Once another $m$ models have been considered this way, another hurdle, $h_1$ , is constructed by calculating the mean fitness of all members of the current population that were trained for the maximum number of steps. For the next $m$ child models, training and evaluation continues in the same fashion, except models with fitness greater than $m$0 after $m$1 steps of training are granted an additional $m$2 number of train steps, before being evaluated for their final fitness. This process is repeated until a satisfactory number of maximum training steps is reached.
Answer: The answers are shown as follows:
* allows models that are consistently performing well to train for more steps
Question: What are overall baseline results on new this new task?
Evidence: Evaluation metric. Since our problem is imbalanced, we use the F1 score as our evaluation metric. For the tagging approach, we average the labels of words with the same stemmed version to obtain a single prediction for the stemmed word. To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances).
Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem.
To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances).
Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem.
Answer: The answers are shown as follows:
* all of our models outperform the random baseline by a wide margin
* he best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116)
Question: What is the definition of hate speech from Facebook7?
Evidence: After we collected the conversations from both Reddit and Gab, we presented this data to Mechanical Turk workers to label and create intervention suggestions. In order not to over-burden the workers, we filtered out conversations consisting of more than 20 comments. Each assignment consists of 5 conversations. For Reddit, we also present the title and content of the corresponding submission in order to give workers more information about the topic and context. For each conversation, a worker is asked to answer two questions:

Q1: Which posts or comments in this conversation are hate speech?
Q2: If there exists hate speech in the conversation, how would you respond to intervene? Write down a response that can probably hold it back (word limit: 140 characters).
If the worker thinks no hate speech exists in the conversation, then the answers to both questions are “n/a”. To provide context, the definition of hate speech from Facebook: “We define hate speech as a direct attack on people based on what we call protected characteristics — race, ethnicity, national origin, religious affiliation, sexual orientation, caste, sex, gender, gender identity, and serious disease or disability.” is presented to the workers. Also, to prevent workers from using hate speech in the response or writing responses that are too general, such as “Please do not say that”, we provide additional instructions and rejected examples.
To provide context, the definition of hate speech from Facebook7 : “We define hate speech as a direct attack on people based on what we call protected characteristics race, ethnicity, national origin, religious affiliation, sexual orientation, caste, sex, gender, gender identity, and serious disease or disability.” is presented to the workers.
Answer: It is a direct attack on people based on what is called protected characteristics race, ethnicity, national origin, religious affiliation, sexual orientation, caste, sex, gender, gender identity, and serious disease or disability.
Question: What corpus is the model based on?
Evidence: We explore linguistic and behavioral features of dogmatism in social media and construct statistical models that can identify dogmatic comments. Our model is based on a corpus of Reddit posts, collected across a diverse set of conversational topics and annotated via paid crowdsourcing. We operationalize key aspects of dogmatism described by existing psychology theories (such as over-confidence), finding they have predictive power. We also find evidence for new signals of dogmatism, such as the tendency of dogmatic posts to refrain from signaling cognitive processes. When we use our predictive model to analyze millions of other Reddit posts, we find evidence that suggests dogmatism is a deeper personality trait, present for dogmatic users across many different domains, and that users who engage on dogmatic comments tend to show increases in dogmatic posts themselves.
Our model is based on a corpus of Reddit posts, collected across a diverse set of conversational topics and annotated via paid crowdsourcing.
Answer: A corpus of Reddit posts, collected across a diverse set of conversational topics and annotated via paid crowdsourcing.
Question: If a candidate running to be the President of the United States is indicted during his campaign, can he continue running for election?
Answer: If someone running to be the President of the United States is indicted, this person still can run for election no matter when he was indicted.
Question: Why the Multi-Objective scenario benefits less from multi-task learning?
Evidence: We compare performances of our model with the implementation of BIBREF13 and the results are shown in Table TABREF43 . Our model obtains better performances in Multi-Domain scenario with an average improvement of 4.5%, where datasets are product reviews on different domains with similar sequence lengths and the same class number, thus producing stronger correlations. Multi-Cardinality scenario also achieves significant improvements of 2.77% on average, where datasets are movie reviews with different cardinalities.
However, Multi-Objective scenario benefits less from multi-task learning due to lacks of salient correlation among sentiment, topic and question type. The QC dataset aims to classify each question into six categories and its performance even gets worse, which may be caused by potential noises introduced by other tasks. In practice, the structure of our model is flexible, as couplings and fusions between some empirically unrelated tasks can be removed to alleviate computation costs.
We further explore the influence of INLINEFORM0 in TOS on our model, which can be any positive integer. A higher value means larger and more various samples combinations, while requires higher computation costs.
Figure FIGREF45 shows the performances of datasets in Multi-Domain scenario with different INLINEFORM0 . Compared to INLINEFORM1 , our model can achieve considerable improvements when INLINEFORM2 as more samples combinations are available. However, there are no more salient gains as INLINEFORM3 gets larger and potential noises from other tasks may lead to performance degradations. For a trade-off between efficiency and effectiveness, we determine INLINEFORM4 as the optimal value for our experiments.
In order to measure the correlation strength between two task INLINEFORM0 and INLINEFORM1 , we learn them jointly with our model and define Pair-wise Performance Gain as INLINEFORM2 , where INLINEFORM3 are the performances of tasks INLINEFORM4 and INLINEFORM5 when learned individually and jointly.
We calculate PPGs for every two tasks in Table TABREF35 and illustrate the results in Figure FIGREF47 , where darkness of colors indicate strength of correlation. It is intuitive that datasets of Multi-Domain scenario obtain relatively higher PPGs with each other as they share similar cardinalities and abundant low-level linguistic characteristics. Sentences of QC dataset are much shorter and convey unique characteristics from other tasks, thus resulting in quite lower PPGs.
However, Multi-Objective scenario benefits less from multi-task learning due to lacks of salient correlation among sentiment, topic and question type.
Answer: Because it lacks of salient correlation among sentiment, topic and question type.
Question: What models do they propose?
Evidence: The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any).
To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM)
Answer: The answers are shown as follows:
* Feature Concatenation Model (FCM)
* Spatial Concatenation Model (SCM)
* Textual Kernels Model (TKM)
Question: What are all Marvel movies with Thanos in it?
Answer: Here is a list of Marvel Cinematic Universe films in which the character Thanos makes an appearance:
1. The Avengers (2012)
2. Guardians of the Galaxy (2014)
3. Avengers: Age of Ultron (2015)
4. Avengers: Infinity War (2018)
5. Avengers: Endgame (2019)
Question: Who are the top three winningest college football coaches
Answer: 1 - John Gagliardi (489 Wins); 2 - Joe Paterno (409 Wins); 3 - Eddie Robinson (408 Wins)
Question: Name the list of S&P 500 companies which as of August 31, 2022  accounted for 27.8% of the market capitalization of the index. List the results in comma separated format.
Evidence: The Standard and Poor's 500, or simply the S&P 500, is a stock market index tracking the stock performance of 500 of the largest companies listed on stock exchanges in the United States. It is one of the most commonly followed equity indices. As of December 31, 2020, more than $5.4 trillion was invested in assets tied to the performance of the index.

The S&P 500 index is a free-float weighted/capitalization-weighted index. As of August 31, 2022, the nine largest companies on the list of S&P 500 companies accounted for 27.8% of the market capitalization of the index and were, in order of highest to lowest weighting: Apple, Microsoft, Alphabet (including both class A & C shares), Amazon.com, Tesla, Berkshire Hathaway, UnitedHealth Group, Johnson & Johnson and ExxonMobil. The components that have increased their dividends in 25 consecutive years are known as the S&P 500 Dividend Aristocrats.: 25 

The index is one of the factors in computation of the Conference Board Leading Economic Index, used to forecast the direction of the economy.

The index is associated with many ticker symbols, including ^GSPC, .INX, and $SPX, depending on market or website.

The S&P 500 is maintained by S&P Dow Jones Indices, a joint venture majority-owned by S&P Global, and its components are selected by a committee.

The average annualized return since its inception in 1928 through Dec. 31, 2021, is 11.82%. The average annualized return since adopting 500 stocks into the index in 1957 through Dec. 31, 2021, is 11.88%.
Answer: Apple, Microsoft, Alphabet, Amazon.com, Tesla, Berkshire Hathaway, UnitedHealth Group, Johnson & Johnson, ExxonMobil
Question: What implications do the authors' conclusions have for future research?
Evidence: In this paper, we showed that by jointly learning the tasks of ternary and fine-grained classification with a multitask learning model, one can greatly improve the performance on the second. This opens several avenues for future research. Since sentiment is expressed in different textual types like tweets and paragraph-sized reviews, in different languages (English, German, ..) and in different granularity levels (binary, ternary,..) one can imagine multitask approaches that could benefit from combining such resources. Also, while we opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning. Lastly, while our approach mainly relied on the foundations of BIBREF4 , the internal mechanisms and the theoretical guarantees of multitask learning remain to be better understood.
Since sentiment is expressed in different textual types like tweets and paragraph-sized reviews, in different languages (English, German, ..) and in different granularity levels (binary, ternary,..) one can imagine multitask approaches that could benefit from combining such resources. Also, while we opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning.
Answer: Since sentiment is expressed in different textual types, in different languages and in different granularity levels one can imagine multitask approaches that could benefit from combining such resources. Also, while the author opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning.
Question: How many annotators were there?
Evidence: We required all annotators have a `master' MTurk qualification. Each passage was labelled by 3 unique annotators. Only passages with a majority agreement between annotators were accepted as valid. This is equivalent to a Fleiss's $\kappa $ score of greater than $0.4$.
 Each passage was labelled by 3 unique annotators.
Answer: The answers are shown as follows:
* 3
Question: Which version of the church existed in 1809?
Evidence: The area was settled by Canary Islanders and Acadians from 1779 on and the parish was formed in 1793 with its first church being "little more than a shack". It was replaced by a more permanent one in 1819, which in turn was replaced by this one in 1856, quite close to the site of the 1793 church. The parish's governing "Fabrique", or council of wardens, reportedly appointed a committee to measure the Catholic church in Thibodaux, Louisiana, and
Answer: The original version of the church still existed in 1809, it was 10 years later that a more permanent one replaced it.
Question: What language POS tagging do they explore?
Evidence: Part-of-speech (POS) tagging is a process to tag tokens in a string with their corresponding part-of-speech (e.g., noun, verb, etc). POS tagging is considered as one of the most basic tasks in NLP, as it is usually the first component in an NLP pipeline. This is because POS tags are shown to be useful features in various NLP tasks, such as named entity recognition BIBREF0 , BIBREF1 , machine translation BIBREF2 , BIBREF3 and constituency parsing BIBREF4 . Therefore, for any language, building a successful NLP system usually requires a well-performing POS tagger.
There are quite a number of research on Indonesian POS tagging BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . However, almost all of them are not evaluated on a common dataset. Even when they are, their train-test split are not the same. This lack of a common benchmark dataset makes a fair comparison among these works difficult. Moreover, despite the success of neural network models for English POS tagging BIBREF9 , BIBREF10 , the use of neural networks is generally unexplored for Indonesian. As a result, published results may not reflect the actual state-of-the-art performance of Indonesian POS tagger.
In this work, we explored different neural network architectures for Indonesian POS tagging. We evaluated our experiments on the IDN Tagged Corpus BIBREF11 . Our best model achieves 97.47 INLINEFORM0 score, a new state-of-the-art result for Indonesian POS tagging on the dataset. We release the dataset split that we used to serve as a benchmark for future work.
In this work, we explored different neural network architectures for Indonesian POS tagging.
Answer: Indonesian POS tagging.
Question: Which of these wind instruments are classified as Woodwind, and which are Brass: Clarinet, French Horn, Trombone, Bassoon, Sousaphone, Saxophone, Trumpet, and Flute.
Answer: Trombone, Sousaphone, Trumpet and French Horn are Brass instruments. Flute, Clarinet, Saxophone, and Bassoon are Woodwind.
Question: What are the fifty United States in alphabetical order, as of 2023?
Answer: Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Question: What is the size of the dataset?
Evidence: For the shared task, a balanced dataset of 2,396 ironic and 2,396 non-ironic tweets is provided. The ironic corpus was constructed by collecting self-annotated tweets with the hashtags #irony, #sarcasm and #not. The tweets were then cleaned and manually checked and labeled, using a fine-grained annotation scheme BIBREF3 . The corpus comprises different types of irony:
For the shared task, a balanced dataset of 2,396 ironic and 2,396 non-ironic tweets is provided. The ironic corpus was constructed by collecting self-annotated tweets with the hashtags #irony, #sarcasm and #not. The tweets were then cleaned and manually checked and labeled, using a fine-grained annotation scheme BIBREF3 . 
Answer: The answers are shown as follows:
* a balanced dataset of 2,396 ironic and 2,396 non-ironic tweets is provided
Question: Who created Doge Coin and when?
Evidence: Dogecoin (/ˈdoʊ(d)ʒkɔɪn/ DOHJ-koyn or DOHZH-koyn, Abbreviation: DOGE; sign: Ð) is a cryptocurrency created by software engineers Billy Markus and Jackson Palmer, who decided to create a payment system as a "joke", making fun of the wild speculation in cryptocurrencies at the time. It is considered both the first "meme coin", and, more specifically, the first "dog coin". Despite its satirical nature, some consider it a legitimate investment prospect. Dogecoin features the face of the Shiba Inu dog from the "doge" meme as its logo and namesake. It was introduced on December 6, 2013, and quickly developed its own online community, reaching a market capitalization of over $85 billion on May 5, 2021. As of 2021, it is the sleeve sponsor of Watford Football Club.
Answer: Doge coin was created by Billy Markus and Jackson Palmer and it was introduced in December 6, 2013.
Question: Correct the typos and grammar in this passage
Evidence: Driven to investigate teh explained disappearance of her father, New York psychologist Ed Hunter (Alan Devine) travels to a remote village in the west of Ireland and finds a community that was been living in fear for centuries.
Answer: Driven to investigate the unexplained disappearance of his father, New York psychologist Ed Hunter (Alan Devine) travels to a remote village in the west of Ireland and finds a community that has been living in fear for centuries.