DataHammer/scimrc huggingface dataset:
Question: From this reference text, what political party does Taylor Swift belong to?
Evidence: Swift refrained from discussing politics early in her career, fearing it might influence people. Critics took issue with her previously apolitical stance despite her wealth and celebrity. Swift publicly voiced her political opinion for the first time in the 2018 United States elections, when she endorsed Democratic candidates in her home state, Tennessee. In 2019, Swift told The Guardian that when she started her country music career, she was advised against discussing politics by her label executives because of the consequences of the Dixie Chicks controversy, but finally decided to speak up after she became disillusioned with contemporary American politics and moved out of Big Machine.

She identifies as a pro-choice feminist, and is one of the founding signatories of the Time's Up movement against sexual harassment. She criticized the US Supreme Court's decision to overturn Roe v. Wade (1973) and end federal abortion rights in 2022. Swift advocates for LGBT rights, and has called for the passing of the Equality Act, which prohibits discrimination based on sex, sexual orientation, and gender identity. The New York Times wrote her 2011 music video for "Mean" had a positive impact on the LGBTQ+ community. Swift performed during WorldPride NYC 2019 at the Stonewall Inn, frequently deemed the birthplace of the modern gay rights movement. She has donated to the LGBT organizations Tennessee Equality Project and GLAAD.
Answer: Taylor Swift is a democrat.
Question: What do they use to implement a nearest neighbor estimator?
Evidence: We employ three estimation schemes based on the textual contexts of concepts. The first relies on the context via pre-trained word embeddings of the concepts, which, in turn, are derived from the concepts' distributional properties in large samples of free texts. The other two schemes directly access the sentence-level contexts of the concepts.
Nearest neighbors (NN) Estimator: We used the pre-trained GloVe embeddings BIBREF11 of concepts to implement a nearest-neighbor estimator as follows. Given a concept $c$, we extract all labeled concepts within a given radius $r$ (cosine similarity $0.3$). In one variant, $c$'s controversiality score is taken to be the fraction of controversial concepts among them. In another variant, labeled concepts are weighted by their cosine similarity to $c$.
Naive Bayes (NB) Estimator: A Naive Bayes model was learned, with a bag-of-words feature set, using the word counts in the sentences of our training data – the contexts of the controversial and non-controversial concepts. The controversiality score of a concept $c$ for its occurrence in a sentence $s$, is taken as the posterior probability (according to the NB model) of $s$ to contain a controversial concept, given the words of $s$ excluding $c$, and taking a prior of $0.5$ for controversiality (as is the case in the datasets). The controversiality score of $c$ is then defined as the average score over all sentences referencing $c$.
Recurrent neural network (RNN): A bidirectional RNN using the architecture suggested in BIBREF10 was similarly trained. The network receives as input a concept and a referring sentence, and outputs a score. The controversiality score of a concept is defined, as above, to be the average of these scores.
Nearest neighbors (NN) Estimator: We used the pre-trained GloVe embeddings (Pennington et al., 2014) of concepts8 to implement a nearestneighbor estimator as follows.
Answer: The pre-trained GloVe embeddings
Question: Where is Meerut?
Evidence: Meerut (pronunciation (help·info), IAST: Meraṭh) is a city in Meerut district of the western part of the Indian state of Uttar Pradesh. The city lies 80 km (50 mi) northeast of the national capital New Delhi, within the National Capital Region and 480 km (300 mi) west of the state capital Lucknow.
Answer: Meerut is a city in India and part of Uttar Pradesh state close to new Delhi
Question: Extract the different foods a giant panda eats from this passage in wild and captive. Place them in two separate bulleted lists with heading as 'Wild' and 'Captive'
Evidence: The giant panda (Ailuropoda melanoleuca, sometimes panda bear or simply panda) is a bear species endemic to China. It is characterised by its bold black-and-white coat and rotund body. The name "giant panda" is sometimes used to distinguish it from the red panda, a neighboring musteloid. Though it belongs to the order Carnivora, the giant panda is a folivore, with bamboo shoots and leaves making up more than 99% of its diet. Giant pandas in the wild occasionally eat other grasses, wild tubers, or even meat in the form of birds, rodents, or carrion. In captivity, they may receive honey, eggs, fish, yams, shrub leaves, oranges, or bananas along with specially prepared food.
Answer: Wild
- Bamboo shoots
- Leaves
- Grasses
- Wild tubers
- Meat of birds, rodents or carrion

Captive
- Honey
- Eggs
- Fish
- Yams
- Shrub leaves
- Oranges
- Bananas
Question: What makes bread rise?
Answer: There are various techniques and ingredients used in the bread making process to make it rise. Yeast can be used in the dough which will activate and the dough will rise over a number of hours (or even days) depending on the type of bread you are making. Another method is natural fermentation ie sourdough. Sourdough bread rises by using a "pre-ferment" or sourdough starter when mixing the dough. Typically sourdough requires a longer rise time for the development of the air bubbles and the sour taste by which the name comes from.
Question: Which rivers were the birthplaces of the first great civilizations?
Answer: The first great civilizations arose near rivers, where they could develop agriculture. The Egyptian civilization arose around the Nile, the Sumerian civilization in Mesopotamia, between the Tigris and Euphrates rivers; the Harappa and Mohenjo Daro cultures arose along the Indus river (Pakistan), and the ancient Chinese civilization arose around the Yangtze river.
Question: Which album Freya Clausen release?
Evidence: Freya Christine Clausen also known as Freya (born 17 November 1978) is a Danish singer/songwriter and television personality, based in Copenhagen. She worked as an MTV VJ, then moved to VH1 in 2010. She has released two solo albums, Tea with the Queen and Chasing My Tale.

Since 2010 she has hosted the pan-Scandinavian TV channel Star!'s broadcasts from Stockholm Fashion Week by Berns.

Freya released a new single called "Into The Fire" on 29 September 2014.
Answer: Freya Clausen released 2 albums: Tea with the Queen and Chasing My Tale.
Question: What is the performance proposed model achieved on AlgoList benchmark?
Evidence: FLOAT SELECTED: Table 2: Results of AlgoLisp dataset
Generating Lisp programs requires sensitivity to structural information because Lisp code can be regarded as tree-structured. Given a natural-language query, we need to generate code containing function calls with parameters. Each function call is a relational tuple, which has a function as the relation and parameters as arguments. We evaluate our model on the AlgoLisp dataset for this task and achieve state-of-the-art performance. The AlgoLisp dataset BIBREF17 is a program synthesis dataset. Each sample contains a problem description, a corresponding Lisp program tree, and 10 input-output testing pairs. We parse the program tree into a straight-line sequence of tuples (same style as in MathQA). AlgoLisp provides an execution script to run the generated program and has three evaluation metrics: the accuracy of passing all test cases (Acc), the accuracy of passing 50% of test cases (50p-Acc), and the accuracy of generating an exactly matching program (M-Acc). AlgoLisp has about 10% noisy data (details in the Appendix), so we report results both on the full test set and the cleaned test set (in which all noisy testing samples are removed). TP-N2F is compared with an LSTM seq2seq with attention model, the Seq2Tree model in BIBREF17, and a seq2seq model with a pre-trained tree decoder from the Tree2Tree autoencoder (SAPS) reported in BIBREF18. As shown in Table TABREF18, TP-N2F outperforms all existing models on both the full test set and the cleaned test set. Ablation experiments with TP2LSTM and LSTM2TP show that, for this task, the TP-N2F Decoder is more helpful than TP-N2F Encoder. This may be because lisp codes rely more heavily on structure representations.
FLOAT SELECTED: Table 2: Results of AlgoLisp dataset
As shown in Table TABREF18, TP-N2F outperforms all existing models on both the full test set and the cleaned test set.
Answer: Full Testing Set Accuracy: 84.02
Cleaned Testing Set Accuracy: 93.48
Question: What are the top 5 comedic movies of all time
Answer: The debate for the best comedies is subjective and open to debate.  What is considered humorous can vary by region and evolves over time.  With that said, here are 5 popular comedies:
 - Dumb and Dumber
 - Anchorman
 - Monty Python and the Holy Grail
 - Airplane!
 - Planes, Trains and Automobiles
Question: Create a bulleted list of Hawking's accomplishments based on this passage.
Evidence: Hawking achieved commercial success with several works of popular science in which he discussed his theories and cosmology in general. His book A Brief History of Time appeared on the Sunday Times bestseller list for a record-breaking 237 weeks. Hawking was a Fellow of the Royal Society, a lifetime member of the Pontifical Academy of Sciences, and a recipient of the Presidential Medal of Freedom, the highest civilian award in the United States. In 2002, Hawking was ranked number 25 in the BBC's poll of the 100 Greatest Britons. He died in 2018 at the age of 76, after living with motor neurone disease for more than 50 years.
https://en.wikipedia.org/wiki/Stephen_Hawking
Answer: -His book was a bestseller for 237 weeks, which was a record
-He was a fellow of the Royal Society
-He was a Pontifical Academy of Sciences lifetime member
-He received the Presidential Medal of Freedom
-He was 25 on the 100 Greatest Britons list put out by the BBC
Question: What are the words of House Tallhart?
Answer: "Proud and Free"
Question: Suggest a vacation trip for a couple based in California who love spending time outdoors
Answer: The American west is home to spectacular national parks. The national parks in California, Utah and Arizona are unique in their variety of landscapes and ecology. One possible trip is to Yosemite, Sequoia and Death Valley National Parks.  All of these parks provide a range of options for people of varying abilities. For the hardcore hikers a multi-day backpacking trip along the John Muir trip would take them through some of the most scenic mountain terrain in the country. Day hikers have dozens of shorter trails to choose from in each park. One point to keep in mind is that Death Valley would be uncomfortably hot in the summer months - May through September.
Question: Identify which instrument is string or percussion: Balafon, Chrotta
Answer: Chrotta is string, Balafon is percussion.
Question: What is Summer House the TV show?
Evidence: Summer House follows a group of nine friends who share a summer house in the Hamptons town of Southampton, New York.
Answer: Summer House is a reality TV show on Bravo that follows a group of friends that leaves New York City every weekend during the summer to go to the Hamptons. In the Hamptons, they'll go out to dinners, nightclubs, throw parties while the production staff films them in these events. The casts changes every year except they have a few similar people Kyle Cooke, Lindsay Hubbard, Carl Radke, and Amanda Batula. There have been a couple of spinoffs including Winter House where they include other members of Bravo TV shows.
Question: Name some famous jazz songs?
Answer: "Take Five" by Dave Brubeck, "So What" by Miles Davis, "Take The A Train" by Duke Ellington, "Round Midnight" by Thelonious Monk and "My Favorite Things" by John Coltrane are all considered to be very famous jazz songs.
Question: The Global Climate Emergency report is based on scientific analysis of more than how many years?
Answer: 40 years and the report is call "world scientists" submitted by a group of 11258 scientists
Question: What is the second oldest ballpark in Major League Baseball?
Answer: The second oldest ballpark in Major League Baseball is Wrigley Field in Chicago, IL. It was built in 1914, two years after Fenway Park opened in Boston, MA.
Question: Q: How can I prevent COVID-19?
Answer: A: The best way to prevent COVID-19 is to get vaccinated with an FDA-approved or FDA-authorized COVID-19 vaccine and stay up to date on your COVID-19 vaccines.

In addition, the CDC recommends everyday preventive actions to help prevent the spread of COVID-19. 

Learn how to protect your family in this Consumer Update.
Question: How do they evaluate interpretability in this paper?
Evidence: Let all word vectors be normalized and $W$ be the word matrix. Inspired by BIBREF21 , where vector space models are used for evaluating topic coherence, we suggest to estimate the interpretability of $k$ th component as $ \operatorname{interp}_k W = \sum _{i,j=1}^N W_{i,k} W_{j,k} \left(W_i \cdot W_j \right). $
Let all word vectors be normalized and $W$ be the word matrix. Inspired by BIBREF21 , where vector space models are used for evaluating topic coherence, we suggest to estimate the interpretability of $k$ th component as $ \operatorname{interp}_k W = \sum _{i,j=1}^N W_{i,k} W_{j,k} \left(W_i \cdot W_j \right). $ 
Answer: The answers are shown as follows:
* we suggest to estimate the interpretability of $k$ th component as $ \operatorname{interp}_k W = \sum _{i,j=1}^N W_{i,k} W_{j,k} \left(W_i \cdot W_j \right). $
Question: Identify which animal is domesticated or wild: Turkey, European goldfinch
Answer: Turkey is domesticated, European goldfinch is wild.
Question: How does a zipper work?
Answer: A zipper is used to repeatedly open and close two edges of a surface together, typically on garment bags, luggage, coats, and pants. Zippers refer to the system of two surface edges and a slider, where a human pulls the slider one direction to open and another direction to close. The slider interlocks "teeth" together to make the surfaces bind when they close, and disconnects the teeth apart when then open. Zippers are usually made from metal or plastic.
Question: How many types of recurrent neural layers are there in the generalized multi-task learning architecture proposed by the authors?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification.
Answer: Four.
Question: Summarize what it is means to feel threatened?
Evidence: From Wikipedia, the free encyclopedia
For other uses, see Threat (disambiguation).

Threats can be subtle or overt. Actor Justus D. Barnes, in The Great Train Robbery
A threat is a communication of intent to inflict harm or loss on another person. Intimidation is a tactic used between conflicting parties to make the other timid or psychologically insecure for coercion or control. The act of intimidation for coercion is considered as a threat.

Threatening or threatening behavior (or criminal threatening behavior) is the crime of intentionally or knowingly putting another person in fear of bodily injury. "Threat of harm generally involves a perception of injury...physical or mental damage...act or instance of injury, or a material and detriment or loss to a person."

Some of the more common types of threats forbidden by law are those made with an intent to obtain a monetary advantage or to compel a person to act against their will. In most US states, it is an offense to threaten to (1) use a deadly weapon on another person; (2) injure another's person or property; or (3) injure another's reputation.
Answer: Feeling threatened implies that someone or something is communicating or suggesting that they will inflict harm on you or your being.  This is generally considered to be an intimidation tactic suggesting that if you do not comply, you will be harmed.  Threats are not always physical, they can be emotional, financial, spiritual amongst many other types.  Making threats depending on their severity can be considered illegal.
Question: What are commonly used datasets for systems fit for precise reasoning?
Evidence: Reasoning is part of our every day routine: we hear Natural Language (NL) sentences, we participate in dialogues, we read books or legal documents. Successfully understanding, participating or communicating with others in these situations presupposes some form of reasoning: about individual sentences, whole paragraphs of legal documents, small or bigger pieces of dialogue and so on. The human reasoning performed in these different situations cannot be explained by a single rigid system of reasoning, plainly because reasoning is performed in different ways in each one of them. Consider the following example:
Three representatives are needed.
If a human reasoner with expert knowledge was to interpret the above utterance in a legal context, s/he will most probably judge that a situation where more than three references are provided could be compatible with the semantics of the utterance. To the contrary, if the same reasoner was to interpret the above as part of a casual, everyday conversation, then three would most likely be interpreted as exactly three, making the same situation incompatible with the utterance. In this paper, we want to focus on precise inference, inference which is either performed by experts or normal people after taking some time to consider the inferences that follow or not from a set of premises. The problem one encounters in testing systems of this type of reasoning is the fact that no large scale datasets of this sort of inference exist. The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
Answer: The FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
Question: please give me a short description about SVB collapse
Evidence: On Friday, March 10, 2023, Silicon Valley Bank (SVB) failed after a bank run, marking the second-largest bank failure in United States history and the largest since the 2007–2008 financial crisis. It was one of three March 2023 United States bank failures.
Answer: It's due to risk mismanagement of SVB Bank, where the bank bet on long dated treasury bond. When the bank runs happen, SVB don't have a choice but to sell those long dated bond at a steep discount and incur huge lossses.
Question: Where can I go to register to vote in Wisconsin, or to update my registration details, such as my current address?
Answer: The MyVote.wi.gov site has several resources to help Wisconsin voters.  If you believe you are registered to vote, you can enter in your name and birth date to verify your registration status. Voting rolls do get updated and records that are believed to be outdated do get removed, so it is helpful to verify your status before going to vote.  While current rules allow you to register at your polling site on the day of voting, you do need to have appropriate identification and proof of residence which you might not typically have on hand if you weren't expecting to need to register. You can also register to vote ahead of time through this website.  This website is provided by the Wisconsin Elections Commission, which is a bipartisan commission charged with implementing Wisconsin's election statutes.
Question: What evaluation metrics do they use?
Evidence: For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below. We will compute both the binary INLINEFORM1 score and the three metrics for each PHI type for both data sets. Note that binary INLINEFORM2 score calculates whether or not a token was identified as a PHI as opposed to correctly predicting the right PHI type. For de-identification, we place more importance on identifying if a token was a PHI instance with correctly predicting the right PHI type as a secondary objective. INLINEFORM3 INLINEFORM4
For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below.
Answer: The answers are shown as follows:
* Precision, Recall and INLINEFORM0 score
Question: What language pairs did they experiment with?
Evidence: We perform experiments (§ SECREF5 ) on two English-Japanese translation corpora to evaluate the method's utility in improving translation accuracy and reducing the time required for training.
We perform experiments (§ SECREF5 ) on two English-Japanese translation corpora to evaluate the method's utility in improving translation accuracy and reducing the time required for training.
Answer: The answers are shown as follows:
* English-Japanese