DataHammer/scimrc huggingface dataset:
Question: What are the benefits of this dataset?
Evidence: We present the first dataset for QA on social media data by leveraging news media and crowdsourcing. The proposed dataset informs us of the distinctiveness of social media from formal domains in the context of QA. Specifically, we find that QA on social media requires systems to comprehend social media specific linguistic patterns like informality, hashtags, usernames, and authorship. These distinguishing linguistic factors bring up important problems for the research of QA that currently focuses on formal text. We see our dataset as a first step towards enabling not only a deeper understanding of natural language in social media but also rich applications that can extract essential real-time knowledge from social media.
We see our dataset as a first step towards enabling not only a deeper understanding of natural language in social media but also rich applications that can extract essential real-time knowledge from social media.
Answer: Not only a deeper understanding of natural language in social media but also rich applications that can extract essential real-time knowledge from social media
Question: The process of identifying parameters related to restricted classes seems quite empirically, as a transformation component is needed from some prior knowledge. The authors have mentioned it for images. However, many data privacy related data are also tabular. In this case, how to apply a proper transformation? If this component is quite related to data format, any workaround for this issue?
Evidence: Next, we apply a grayscale data augmentation technique/transformation f to these images so that these images become different from the images that the original model was earlier trained on (assuming that the original model has not been trained on grayscale images). We can also use other data augmentation techniques that are not seen during the training process of the original model and that do not change the class of the image (refer to Sec. 11.7 in the appendix).
Answer: Our proposed approach only requires the use of data augmentation/transformation techniques that are not used during training. Therefore, for any domain, we can use data augmentation methods compatible with the domain as long as they have not been used during training and do not change the label of the datapoint.
Question: What field of science (biology, physics or chemistry) was Marie Curie a researcher in?
Answer: Marie Curie was a chemist
Question: Why do birds migrate north to south for the colder months in the north?
Evidence: Bird migration is the regular seasonal movement, often north and south, along a flyway, between breeding and wintering grounds. Many species of bird migrate. Migration carries high costs in predation and mortality, including from hunting by humans, and is driven primarily by the availability of food. It occurs mainly in the northern hemisphere, where birds are funneled onto specific routes by natural barriers such as the Mediterranean Sea or the Caribbean Sea.
Answer: Birds primarily migrate due to the availability of food in warmer climates. The north is a breeding ground, while the south is their wintering ground.
Question: Which vision-based approaches does this approach outperform?
Evidence: We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:
CNN-mean: taking the similarity score of the averaged feature of the two image sets.
CNN-avgmax: taking the average of the maximum similarity scores of two image sets.
We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.
Answer: The answers are shown as follows:
* CNN-mean
* CNN-avgmax
Question: What language tasks did they experiment on?
Evidence: We evaluated RobBERT in several different settings on multiple downstream tasks. First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks. Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning.
First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks. 
Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning.
Answer: The answers are shown as follows:
* sentiment analysis
* the disambiguation of demonstrative pronouns,
Question: What misbehavior is identified?
Evidence: It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example:
Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations.
Answer: The answers are shown as follows:
* if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations
Question: Who are the last 10 Masters Champions?
Answer: 2022 — Scottie Scheffler
2021 — Hideki Matsuyama
2020 — Dustin Johnson
2019 — Tiger Woods
2018 — Patrick Reed
2017 — Sergio Garcia 
2016 — Danny Willett
2015 — Jordan Spieth
2014 — Bubba Watson
2013 — Adam Scott
Question: Who is LaVelle Smith Jr.?
Evidence: LaVelle Smith Jr is an American choreographer and dancer. Known for his work in the music videos of singers such as En Vogue, Michael Jackson, Janet Jackson and Beyoncé Knowles, Smith has won the MTV Video Music Award for Best Choreography five times.
Answer: LaVelle Smith Jr is an American choreographer and dancer. Known for his work in the music videos of singers such as En Vogue, Michael Jackson, Janet Jackson and Beyoncé Knowles, Smith has won the MTV Video Music Award for Best Choreography five times.
Smith had originally wanted to pursue a career in drama, auditioning at the Youth Performing Arts School in Louisville, Kentucky. Upon failing the audition, Smith was given the option to study dance. Graduating from YPAS in 1983, Smith moved to Chicago and was employed by Gus Giordano in the dance troupe Giordano Jazz Dance Chicago. Unhappy with the level of pay, Smith auditioned for numerous dancing roles, while facing racial prejudice. "When that happens, you realize that you have to be even better or move to L.A. where it doesn't matter," he said later.

Noticed by singer Michael Jackson, Smith landed a job as a dancer before becoming his choreographer. Smith worked on three of Jackson's world tours; Bad, Dangerous and HIStory. He also worked as a choreographer in Michael Jackson's Ghosts, and for the musical Thriller - Live.

Smith appeared on the concert tours of The Rolling Stones, Diana Ross and Janet Jackson's Rhythm Nation 1814 Tour. Smith also worked for Victoria Beckham after the Spice Girl made a return as a solo artist.

Smith was awarded four MTV Video awards for co-choreographing En Vogue's music videos "My Lovin'" (1992), "Free Your Mind" (1993), "Whatta Man" (1994) and Michael Jackson's duet with sister Janet, "Scream" (1995). The music video for Beyoncé's "Crazy in Love" won Smith his fifth MTV Video Music Award for Best Choreography in 2003.

His other awards include a SSDC Bob Fosse award and an Emmy award. Smith was also the youngest inductee into the Millers Gallery of Greats.

Smith has choreographed Invincible: A Glorious Tribute To Michael Jackson featuring Jeffrey Perez and Pete Carter.

Smith's work with Michael Jackson is the subject of the 2019 documentary film The Man Behind The Dance.
Question: What is the plot of the movie The Quiet Family
Evidence: The Quiet Family (Korean: 조용한 가족; RR: Joyonghan Gajok) is a 1998 South Korean black comedy horror film directed by Kim Jee-woon. The story centers on a family who owns a hunting lodge in a remote area, whose customers always happen to end up dying. Among the film's main cast are pre-stardom Choi Min-sik and Song Kang-ho.

The film was loosely remade in Japanese as The Happiness of the Katakuris by Takashi Miike, in Indian Tamil as Yaamirukka Bayamey, in Kannada as Namo Bhootatma and in Telugu as Next Nuvve.
Answer: The Quiet Family is a 1998 South Korean black comedy horror film directed by Kim Jee-woon. The story centers on a family who owns a hunting lodge in a remote area, whose customers always happen to end up dying. Among the film's main cast are pre-stardom Choi Min-sik and Song Kang-ho.

The film was loosely remade in Japanese as The Happiness of the Katakuris by Takashi Miike, in Indian Tamil as Yaamirukka Bayamey, in Kannada as Namo Bhootatma and in Telugu as Next Nuvve.
Question: What dataset is used for training?
Evidence: Training and Validation Sets We collected a corpus of poems and a corpus of vernacular literature from online resources. The poem corpus contains 163K quatrain poems from Tang Poems and Song Poems, the vernacular literature corpus contains 337K short paragraphs from 281 famous books, the corpus covers various literary forms including prose, fiction and essay. Note that our poem corpus and a vernacular corpus are not aligned. We further split the two corpora into a training set and a validation set.
We collected a corpus of poems and a corpus of vernacular literature from online resources. The poem corpus contains 163K quatrain poems from Tang Poems and Song Poems, the vernacular literature corpus contains 337K short paragraphs from 281 famous books, the corpus covers various literary forms including prose, fiction and essay. Note that our poem corpus and a vernacular corpus are not aligned. We further split the two corpora into a training set and a validation set.
Answer: The answers are shown as follows:
* We collected a corpus of poems and a corpus of vernacular literature from online resources
Question: What does the basic form of NMT comprise?
Evidence: We train NMT with RAT to achieve better query translations. We improve a recently proposed NMT baseline, Transformer, that achieves state-of-the-art results for sentence pairs in some languages BIBREF8 . We discuss Transformer, RAT, and our multi-task learning architecture that achieves balanced translation.
A basic form of NMT comprises two components: (a) an encoder that computes the representations or meaning of si and (b) a decoder that generates one target word at a time.
Answer: A basic form of NMT comprises two components: (a) an encoder that computes the representations or meaning of si and (b) a decoder that generates one target word at a time.
Question: What specific machine learning-based classifiers did the author compare?
Evidence: Question classification (QC) deals with question analysis and question labeling based on the expected answer type. The goal of QC is to assign classes accurately to the questions based on expected answer. In modern system, there are two types of questions BIBREF0. One is Factoid question which is about providing concise facts and another one is Complex question that has a presupposition which is complex. Question Answering (QA) System is an integral part of our daily life because of the high amount of usage of Internet for information acquisition. In recent years, most of the research works related to QA are based on English language such as IBM Watson, Wolfram Alpha. Bengali speakers often fall in difficulty while communicating in English BIBREF1.
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers. Bengali questions have flexible inquiring ways, so there are many difficulties associated with Bengali QC BIBREF0. As there is no rich corpus of questions in Bengali Language available, collecting questions is an additional challenge. Different difficulties in building a QA System are mentioned in the literature BIBREF2 BIBREF3. The first work on a machine learning based approach towards Bengali question classification is presented in BIBREF0 that employ the Stochastic Gradient Descent (SGD).
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.
Answer: They compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Stochastic Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.
Question: My computer won't start. How do I fix it?
Answer: First check that the computer is plugged in. If there's a switch on the power supply make sure the switch is in the on position. Check to see if the fans turn on when you hit the power button. If so, then the computer is likely getting power. Do you hear a beeping sound? That means the computer is successfully able to load the BIOS and you issue might be related to the installation of the OS. If you can't get to the BIOS, you may have a hardware issue with computer. Open the case and check for loose connections. Try reseating the ram. If that doesn't work you may have a short on the motherboard, which might need replacement.
Question: Which of the following are considered hobbies: painting, hiking, rock climbing, peapod, bears, horse riding, chess, emails, clogs, board games, button, sewing, water bottle, keyboard, reading, soccer
Answer: painting, hiking, rock climbing, horse riding, chess, board games, reading, soccer
Question: Is it possible to take a peek at the weights to see if a layer clearly chooses p+ over p- or vice versa?
Evidence: We analyzed the behavior of the negative GNP on three graph-level tasks that we performed in the paper. In all experiments, we found that either GNP+ or GNP− tends to dominate the other side. To validate the observation, we masked the output of GNP+ and GNP− for readout to 0 on the graph-level tasks.
Answer: We also analyzed the behavior of the negative GNP on three graph-level tasks and found that either GNP+ or GNP− tends to dominate the other side. For details, please refer to Appendix C.6 (in the revised paper).
Question: Extract the steps to resolve a given supervised machine learning problem from the following text
Evidence: To solve a given problem of supervised learning, one has to perform the following steps:

Determine the type of training examples. Before doing anything else, the user should decide what kind of data is to be used as a training set. In the case of handwriting analysis, for example, this might be a single handwritten character, an entire handwritten word, an entire sentence of handwriting or perhaps a full paragraph of handwriting.
Gather a training set. The training set needs to be representative of the real-world use of the function. Thus, a set of input objects is gathered and corresponding outputs are also gathered, either from human experts or from measurements.
Determine the input feature representation of the learned function. The accuracy of the learned function depends strongly on how the input object is represented. Typically, the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object. The number of features should not be too large, because of the curse of dimensionality; but should contain enough information to accurately predict the output.
Determine the structure of the learned function and corresponding learning algorithm. For example, the engineer may choose to use support-vector machines or decision trees.
Complete the design. Run the learning algorithm on the gathered training set. Some supervised learning algorithms require the user to determine certain control parameters. These parameters may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation.
Evaluate the accuracy of the learned function. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set.
Answer: Use the following steps to solve a given problem of supervised learning:
1. Determine the type of training examples,
2. Gather a training set.
3. Determine the input feature representation of the learned function.
4. Determine the structure of the learned function and corresponding learning algorithm.
5. Run the learning algorithm on the gathered training set. 
6. Evaluate the accuracy of the learned function.
Question: How many of the utterances are transcribed?
Evidence: Speakers were assigned either to training or evaluation sets, with proportions of $\frac{2}{3}$ and $\frac{1}{3}$, respectively; then training and evaluation lists were built, accordingly. Table reports statistics from the spoken data set. The id All identifies the whole data set, while Clean defines the subset in which sentences containing background voices, incomprehensible speech and word fragments were excluded.
FLOAT SELECTED: Table 7: Statistics from the spoken data sets (2017) used for ASR.
Speakers were assigned either to training or evaluation sets, with proportions of $\frac{2}{3}$ and $\frac{1}{3}$, respectively; then training and evaluation lists were built, accordingly. Table reports statistics from the spoken data set. The id All identifies the whole data set, while Clean defines the subset in which sentences containing background voices, incomprehensible speech and word fragments were excluded.
FLOAT SELECTED: Table 7: Statistics from the spoken data sets (2017) used for ASR.
Answer: Total number of transcribed utterances including Train and Test for both Eng and Ger language is 5562 (2188 cleaned)
Question: In English do adjectives go before or after nouns they describe?
Answer: In English adjectives precede the item they are describing
Question: Given a reference text about Listeriosis, tell me how how the bacterium is transfered into animals, and people.
Evidence: Listeriosis is an infectious but not contagious disease caused by the bacterium Listeria monocytogenes, far more common in domestics animals (domestic mammals and poultry), especially ruminants, than in human beings. It can also occur in feral animals—among others, game animals—as well as in poultry and other birds.

The causative bacterium lives in the soil and in poorly made silage, and is acquired by ingestion. It is not contagious; over the course of a 30-year observation period of sheep disease in Morocco, the disease only appeared in the late 2000s (decade) when feeding bag-ensiled corn became common.[better source needed] In Iceland, the disease is called "silage sickness".
Answer: Listeriosis is most often associated with animals but can also infect people and is primarily transferred by ingestion.
Question: Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}
Evidence: Kevin Stanley Rohleder (7 April 1920 – 14 August 1983) was an Australian rules footballer who had played with St Kilda in the Victorian Football League (VFL). His brother, Noel Rohleder, played one game for South Melbourne. The son of Veronica Harriet Stanley (1899–1970), Kevin Stanley was born at Carlton, Victoria on 7 April 1920. He later took the surname Rohleder after his mother married Walter John Rohleder (1897–1982) in 1923.
Answer: • Kevin Stanley Rohleder
• Noel Rohleder
• Veronica Harriet Stanley
• Walter John Rohleder
Question: Identify the political office or offices Julius Steele Barnes held.
Evidence: Julius Steele Barnes (23 February 1792 – 12 November 1870) was an American physician. Besides being a skillful practitioner, and devoted to his calling, he also labored heartily for the social good of the community. He served one term as Connecticut State Senator, and held for a time the office of Judge of Probate.
Answer: Julius Steele Barnes was a Connecticut State senator and a Judge of Probate.
Question: Which baselines are explored?
Evidence: To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \ldots x_n$ and citing sentence $Y = y_1 \ldots y_m$ with a special separator token $\mho $. The model learns to approximate next token probabilities for each index after $\mho $:
We measure the closeness of two pairs of documents by measuring cosine distances between vector representations of their content. The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing. The distance between $(S,C)$ and candidate $(N_S,N_C)$ is computed as:
To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \ldots x_n$ and citing sentence $Y = y_1 \ldots y_m$ with a special separator token $\mho $.
The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing.
Answer: The answers are shown as follows:
* GPT2
* SciBERT model of BIBREF11