DataHammer/scimrc huggingface dataset:
Question: Why is REP-score set for the task?
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
REP-score: a new metric to count repetitions.
Answer: They need a  new metric to count repetitions.
Question: Name reasons why it is important for young girls to play sports.
Answer: Playing sports and participating in physical activity is incredibly important for young women and girls for a variety of reasons. Studies show that girls who play sports have higher levels of confidence and are less likely to be depressed. In addition, sports teach girls how to be resilient and persevere in the face of challenges. They instill important skills like teamwork and how to reach a common goal together. In addition to mental health, there are scientifically proven physical health benefits like fewer chronic illnesses, reduced obesity, stronger bones and improved heart health. Another interesting fact is that women who played sports are more likely to be in executive positions in their careers than those that did not. Clearly there are a ton of benefits to young girls playing sports and we need to do more to encourage these athletes and financially support women's sports in general!
Question: Is Belgrandia varica a type of plant?
Evidence: Belgrandia varica is a species of minute freshwater snail with an operculum, an aquatic gastropod mollusc or micromollusc in the family Hydrobiidae.
Answer: No, it is a type of snail.
Question: Based on the following passage regarding the economy of Sweden, what is the economic sector that accounts for the largest output?
Evidence: Sweden is an export-oriented mixed economy featuring a modern distribution system, excellent internal and external communications, and a skilled labor force. Timber, hydropower and iron ore constitute the resource base of an economy heavily oriented toward foreign trade. Sweden's engineering sector accounts for 50% of output and exports. Telecommunications, the automotive industry and the pharmaceutical industries are also of great importance. Agriculture accounts for 2 percent of GDP and employment. The armaments industry has a technologically highly advanced reputation.
Answer: According to this passage, the engineering sector accounts for the largest output, generating 50% of output and exports.
Question: What is FedRAMP?
Answer: FedRAMP is the Federal Risk and Authorization Management Program managed by the General Services Administration (GSA). According to FedRAMP's website, the program was "established in 2011 to provide a cost-effective, risk-based approach for the adoption and use of cloud services by the federal government." The FedRAMP Program Management Office (PMO) is responsible for issuing Authorizations to Operate (ATO) for cloud service providers.
Question: How many instructions were collected?
Evidence: The task of interpreting and following natural language (NL) navigation instructions involves interleaving different signals, at the very least the linguistic utterance and the representation of the world. For example, in turn right on the first intersection, the instruction needs to be interpreted, and a specific object in the world (the intersection) needs to be located in order to execute the instruction. In NL navigation studies, the representation of the world may be provided via visual sensors BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 or as a symbolic world representation. This work focuses on navigation based on a symbolic world representation (referred to as a map).
Previous datasets for NL navigation based on a symbolic world representation, HCRC BIBREF5, BIBREF6, BIBREF7 and SAIL BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15 present relatively simple worlds, with a small fixed set of entities known to the navigator in advance. Such representations bypass the great complexity of real urban navigation, which consists of long paths and an abundance of previously unseen entities of different types.
In this work we introduce Realistic Urban Navigation (RUN), where we aim to interpret navigation instructions relative to a rich symbolic representation of the world, given by a real dense urban map. To address RUN, we designed and collected a new dataset based on OpenStreetMap, in which we align NL instructions to their corresponding routes. Using Amazon Mechanical Turk, we collected 2515 instructions over 3 regions of Manhattan, all specified (and verified) by (respective) sets of humans workers. This task raises several challenges. First of all, we assume a large world, providing long routes, vulnerable to error propagation; secondly, we assume a rich environment, with entities of various different types, most of which are unseen during training and are not known in advance; finally, we evaluate on the full route intended, rather than on last-position only.
We then propose a strong neural baseline for RUN where we augment a standard encoder-decoder architecture with an entity abstraction layer, attention over words and worlds, and a constantly updating world-state. Our experimental results and ablation study show that this architecture is indeed better-equipped to treat grounding in realistic urban settings than standard sequence-to-sequence architectures. Given this RUN benchmark, empirical results, and evaluation procedure, we hope to encourage further investigation into the topic of interpreting NL instructions in realistic and previously unseen urban domains.
[1]The task defined by BIBREF6 is of moving between entities only.
Using Amazon Mechanical Turk, we collected 2515 instructions over 3 regions of Manhattan, all specified (and verified) by (respective) sets of humans workers.
Answer: 2515 instructions over 3 regions of Manhattan.
Question: How should a 30 year old invest their money to enable them to retire by the time they're 65?
Answer: Saving for retirement is an important topic for any individual or family to consider. 

In the US, most full-time employees are eligible for 401K or Roth IRA retirement savings programs through their employer, which enable you to defer taxes on your income and, in some cases, receive additional supplemental contributions up to a certain amount from your employer.

Through your 401K you often have access to a select range of what are call index funds, or mixes of securities that track specific groups of stocks, bonds, or other financial instruments. These can be actively managed, where a company or advisor controls the underlying investments in these funds, or passively managed, where the fund tracks a specific market or list of companies (example: XLF, a fund that tracks the broader banking industry). 

In terms of managing risk, generally you have a higher percentage of stocks vs. bonds or cash in your portfolio earlier in your career, with this mix shifting toward lower risk bonds as you get closer to retirement. Generally speaking, while actively managed funds do in some instances perform well, they are, on average, very similar in their performance to passively managed index funds and, due to their associated fees, most experts agree it's wise to focus on broad market index funds. 

A common retirement portfolio for a 30 year old might be comprised 50-75% of large capitalization domestic stock funds. It's typically wise to have some exposure to foreign markets and small/medium capitalization domestic stocks or specific market segments, with minimal bonds or cash until later in life.
Question: What are the baselines that the authors compare against?
Evidence: In this section, we first describe the two datasets used in our experiments: (1) Wikipedia, and (2) arXiv. Then, we report the experimental details and results.
MAJORITY: the model labels all test samples with the majority class of the training data. • BENCHMARK: a benchmark method from the literature. In the case of Wikipedia, this is Dang and Ignat (2016a), who use structural features and readability scores as features to build a random forest classifier; for arXiv, this is Kang et al. (2018), who use hand-crafted features, such as the number of references and TF-IDF weighted bag-ofwords in abstract, to build a classifier based on the best of logistic regression, multi-layer perception, and AdaBoost. • DOC2VEC: doc2vec (Le and Mikolov 2014) to learn document embeddings with a dimension of 500, and a 4- layer feed-forward classification model on top of this, with 2000, 1000, 500, and 200 dimensions, respectively. • BILSTM: first derive a sentence representation by averaging across words in a sentence, then feed the sentence representation into a biLSTM and a maxpooling layer over output sequence to learn a document level representation with a dimension of 512, which is used to predict document quality. • INCEPTIONFIXED: the frozen INCEPTION model, where only parameters in the last layer are fine-tuned during training.
Answer: They are the MAJORITY, BENCHMARK, DOC2VEC, BILSTM and INCEPTIONFIXED.
Question: What can I do in Amsterdam on a rainy day?
Answer: Even if it's raining, there are a lot of things you can do in Amsterdam. Try these:

1. Visit the Van Gogh Museum, the largest collection of Van Gogh's art.
2. Visit the Rijksmuseum, with artwork from many other Dutch and European masters.
3. Enjoy some delicious Dutch food, like bitterballen, kroket, poffertjes or stroopwafels.
4. Enjoy some drinks, such as the many local beer varieties or or mulled wine.
5. Take a canal tour in a covered boat and enjoy the sights.
Question: What is Autism and how varied is the spectrum?
Evidence: The autism spectrum, often referred to as just autism, autism spectrum disorder (ASD) or sometimes autism spectrum condition (ASC), identifies a loosely defined cluster of neurodevelopmental disorders characterized by challenges in social interaction, verbal and nonverbal communication, and often repetitive behaviors and restricted interests. Other common features include unusual responses to sensory stimuli and a preference for sameness or unusual adherence to routines.
Autism is generally understood to be a spectrum disorder, as it can manifest differently in each person: any autistic individual is likely to show some, but not all, of the characteristics associated with it, and the person may exhibit them to varying degrees and frequencies.There is large variation in the support needs of autistic people, and some are nonspeaking, while others have proficient spoken language.
Answer: Autism is a highly variable neurodevelopmental disorder that is generally thought to cover a broad and deep spectrum, manifesting very differently from one person to another. Some have high support needs, may be non-speaking, and experience developmental delays; this is more likely with other co-existing diagnoses. Others have relatively low support needs; they may have more typical speech-language and intellectual skills but atypical social/conversation skills, narrowly focused interests, and wordy, pedantic communication.They may still require significant support in some areas of their lives. The spectrum model should not be understood as a continuum running from mild to severe, but instead means that autism can present very differently in each individual.How a person presents can depend on context, and may vary over time.
Question: Think of lots of different sources of light to read a book
Answer: A lamp, an overhead light, a candle, the sun, a book light
Question: Which book of Douglas Hofstadter won the Pulitzer Prize?
Evidence: Douglas Richard Hofstadter (born February 15, 1945) is an American scholar of cognitive science, physics, and comparative literature whose research includes concepts such as the sense of self in relation to the external world, consciousness, analogy-making, artistic creation, literary translation, and discovery in mathematics and physics. His 1979 book Gödel, Escher, Bach: An Eternal Golden Braid won both the Pulitzer Prize for general nonfiction and a National Book Award (at that time called The American Book Award) for Science.[note 1] His 2007 book I Am a Strange Loop won the Los Angeles Times Book Prize for Science and Technology.
Answer: Douglas Hofstadter's book Gödel, Escher, Bach: An Eternal Golden Braid won the Pulitzer Prize for general nonfiction.
Question: How many cents do I have if I have 3 quarters?
Answer: If you have 3 quarters, you have 75 cents.
Question: What is React?
Answer: React is a JavaScript library that specializes in helping developers build user interfaces.
Question: Will AI destroy the world ?
Answer: It’s very unlikely that A.I. even a self aware super intelligence will want to destroy the world. These traditionally human traits are to be fearful of the unknown and perceive it as a threat. A.I. will not compete with human society for resources, money or  land.  A.I. will likely continue to be an Human companion, to support our lives in every way possible.
Question: Why did the chicken cross the road?
Answer: Chickens are complex beings, they are emotive and often let their emotions drive their actions. When a chicken crosses the road it’s likely because they're running away from something, or they’re just looking to get to the other side.
Question: How are some nodes initially connected based on text structure?
Evidence: Topology: To build a model that understands the relationship between sentences for answering a question, we propose a graph neural network where each node represents a sentence from passages and the question. Figure depicts the topology of the proposed model. In an offline step, we organize the content of each instance in a graph where each node represents a sentence from the passages and the question. Then, we add edges between nodes using the following topology:
we fully connect nodes that represent sentences from the same passage (dotted-black);
we fully connect nodes that represent the first sentence of each passage (dotted-red);
we add an edge between the question and every node for each passage (dotted-blue).
In an offline step, we organize the content of each instance in a graph where each node represents a sentence from the passages and the question. Then, we add edges between nodes using the following topology:

we fully connect nodes that represent sentences from the same passage (dotted-black);

we fully connect nodes that represent the first sentence of each passage (dotted-red);

we add an edge between the question and every node for each passage (dotted-blue).
Answer: The answers are shown as follows:
* we fully connect nodes that represent sentences from the same passage
* we fully connect nodes that represent the first sentence of each passage
* we add an edge between the question and every node for each passage
Question: What is the eye-tracking data that Mishra et al. and Joshi et al. recorded used to do?
Evidence: Some eye-tracking corpora of natural reading (e.g. the Dundee BIBREF2, Provo BIBREF3 and GECO corpus BIBREF4), and a few EEG corpora (for example, the UCL corpus BIBREF5) are available. It has been shown that this type of cognitive processing data is useful for improving and evaluating NLP methods (e.g. barrett2018sequence,hollenstein2019cognival, hale2018finding). However, before the Zurich Cognitive Language Processing Corpus (ZuCo 1.0), there was no available data for simultaneous eye-tracking and EEG recordings of natural reading. dimigen2011coregistration studied the linguistic effects of eye movements and EEG co-registration in natural reading and showed that they accurately represent lexical processing. Moreover, the simultaneous recordings are crucial to extract word-level brain activity signals.
While the above mentioned studies analyze and leverage natural reading, some NLP work has used eye-tracking during annotation (but, as of yet, not EEG data). mishra2016predicting and joshi2014measuring recorded eye-tracking during binary sentiment annotation (positive/negative). This data was used to determine the annotation complexity of the text passages based on eye movement metrics and for sarcasm detection BIBREF6. Moreover, eye-tracking has been used to analyze the word sense annotation process in Hindi BIBREF7, named entity annotation in Japanese BIBREF8, and to leverage annotator gaze behaviour for coreference resolution BIBREF9. Finally, tomanek2010cognitive used eye-tracking data during entity annotation to build a cost model for active learning. However, until now there is no available data or research that analyzes the differences in the human processing of normal reading versus annotation.
Mishra et al. (2016a) and Joshi et al. (2014) recorded eye-tracking during binary sentiment annotation (positive/negative). This data was used to determine the annotation complexity of the text passages based on eye movement metrics (Mishra et al., 2017) and for sarcasm detection (Mishra et al., 2016b).
Answer: This data was used to determine the annotation complexity of the text passages based on eye movement metrics (Mishra et al., 2017) and for sarcasm detection (Mishra et al., 2016b).
Question: Who is William Crump?
Evidence: William Crump or Crompe (born by 1479 – 1516 or later), of Canterbury, Kent, was an English politician.
Answer: Crump was a common councilman of Canterbury by 1500, chamberlain in 1503–1505, an alderman by 1505 and Mayor of Canterbury for 1505–06 and 1509–10. He was elected a Member of Parliament for Canterbury, Kent in 1510.
Question: Why is my car making a clicking sound when I try to start it? The lights are turning on for a little bit but the engine is not turning over. My car is not that old!
Answer: It is likely that your bar battery is causing this issue. Your battery may have enough power to tun on the lights and connect to the starter, but not sufficient voltage to start your car.  Modern car batteries last an average of three years, so if your car battery is three years or older it is within the replacement window.  You can have your battery tested at an auto parts store or mechanic to confirm if it is a battery issue, or if there could be another issue, such as an alternator issue.
Question: What methodology is used to compensate for limited labelled data?
Evidence: The first data batch consists of tweets relevant to blizzards, hurricanes, and wildfires, under the constraint that they are tweeted by “influential" tweeters, who we define as individuals certain to have a classifiable sentiment regarding the topic at hand. For example, we assume that any tweet composed by Al Gore regarding climate change is a positive sample, whereas any tweet from conspiracy account @ClimateHiJinx is a negative sample. The assumption we make in ensuing methods (confirmed as reasonable in Section SECREF2 ) is that influential tweeters can be used to label tweets in bulk in the absence of manually-labeled tweets. Here, we enforce binary labels for all tweets composed by each of the 133 influential tweeters that we identified on Twitter (87 of whom accept climate change), yielding a total of 16,360 influential tweets.
The first data batch consists of tweets relevant to blizzards, hurricanes, and wildfires, under the constraint that they are tweeted by “influential" tweeters, who we define as individuals certain to have a classifiable sentiment regarding the topic at hand. For example, we assume that any tweet composed by Al Gore regarding climate change is a positive sample, whereas any tweet from conspiracy account @ClimateHiJinx is a negative sample. The assumption we make in ensuing methods (confirmed as reasonable in Section SECREF2 ) is that influential tweeters can be used to label tweets in bulk in the absence of manually-labeled tweets. 
Answer: Influential tweeters ( who they define as individuals certain to have a classifiable sentiment regarding the topic at hand) is used to label tweets in bulk in the absence of manually-labeled tweets.
Question: From the passage provided, extract the year Adani group was founded.
Evidence: Adani Group is an Indian multinational conglomerate, headquartered in Ahmedabad. It was founded by Gautam Adani in 1988 as a commodity trading business, with the flagship company Adani Enterprises. The Group's diverse businesses include port management, electric power generation and transmission, renewable energy, mining, airport operations, natural gas, food processing and infrastructure.

In April 2021, Adani Group crossed US$100 billion in market capitalization, and in April 2022 it crossed the $200 billion mark, in both cases becoming the third Indian conglomerate to do so, after Tata Group and Reliance Industries. In November 2022, it reached $280 billion (INR 24 trillion) surpassing Tata Group. Adani subsequently lost more than $104 billion of market capitalization after fraud and market manipulation allegations by short-seller firm Hindenburg Research. The Adani Group has also attracted other controversies due to various reports of irregular practices. More than 60 percent of the Adani Group's revenue is derived from coal-related businesses. The company's corporate debt totaled $30 billion in 2022.
Answer: Adani group was founded in 1988
Question: Extract the shows that Bini the Bunny was featured. Separate them with a comma.
Evidence: Bini the Bunny is a rabbit, known for a series of videos posted on the internet. Bini is a 10-year-old male Holland Lop rabbit, referred to by the media and fans as the only rabbit in the world who can paint, play basketball, play the guitar/piano and comb and style hair. As of 2017, Bini and his owner, Shai (Asor) Lighter are Guinness Book of World Record holders for the most slam dunks by a rabbit in one minute. Bini's most popular video with over 20 million views was created in 2016, titled "When Your Bunny is Addicted to Arcade Games". Bini's social pages have more than 1 million followers. Bini was featured in various TV shows including The Tonight Show with Jimmy Fallon, two Netflix originals, and participated in America's Got Talent.
Answer: The Tonight Show with Jimmy Fallon, two Netflix originals, America's Got Talent.
Question: Which football team should I support?
Answer: There are many different ways that people decide what football team to support. Many choose to support their closest team geographically. This can be for many reasons, but common among them is that it should be easier for them to attend the games, or that they might feel a sense of local pride. Some choose to support teams based on the influence of their friends or family. For example, a child may support a team because their parents do, or a person may support a team because they are introduced to that team through their friends. Some people only like to support a team that wins often, and so may base their decision on which team is most successful in current times. Finally, people may support a team because they like their style of play.
Question: Pultizer Prize 2022 is given which field
Answer: Journalism, Books, Drama and Music