DataHammer/scimrc huggingface dataset:
Question: As of 2022, how many member states make up the European Union?
Evidence: The European Union (EU) is a political and economic union of 27 member states that are signatories to the founding treaties of the union and thereby share in the privileges and obligations of membership.
Answer: 27
Question: Give me some ideas for Halloween costumes.
Answer: Traditionally Halloween is associated with scary costumes such ghosts, zombies or skeletons. Dressing up as video game or movie characters is also popular.
Question: Write a short paragraph to help convince someone to purchase an espresso machine.
Answer: For many people who start their day with coffee, a simple, ordinary cup just isn't enough to satisfy them. The most ardent of coffee hobbyists are often those who consume espresso and making espresso at home is the sign of a true aficionado. Making espresso requires specialized equipment including an espresso machine and an espresso-specific coffee grinder. While these are generally expensive pieces of equipment, the ability to fine-tune aspects of the espresso making process such as grind size and temperature can become an addictive hobby for those who are looking to pull the perfect espresso shot. Not to mention the ability to make a vast array of milk-based espresso drinks such as cappuccinos, lattes, flat whites and cortados at home can help save a lot of money.
Question: Why would anyone go river rafting?
Answer: River rafting offers an opportunity to see nature from a new point of view.  You enjoy scenery a the pace of the river current, whether on flat water, through gentle riffles or in whitewater.  The flora and fauna that you see will vary wildly depending on the ecosystem and area of the river you float.  For example, in Oregon alone you can raft rivers flowing down in conditions ranging from semi desert to deep forest.
Question: The French flair is the way the French see life, it has to be grandiose in the sense that even the small thing can be beautiful, stylish, and unique. There is a special French flair in Rugby, where it comes with the sense that anything can happen, even the more desperate attack would be fully supported and played like a final rally, full of bravery. This type of behavior is feared as it is based on improvisation, and has been in the past very efficient even against the very best.
Answer: What is the French Flair?
Question: Classify each country as landlocked or not landlocked: China, Bolivia, Paraguay, Morocco, Ireland, Austria, Switzerland
Answer: China: Not Landlocked
Bolivia: Landlocked
Paraguay: Landlocked
Morocco: Not Landlocked
Ireland: Not Landlocked
Austria: Landlocked
Switzerland: Landlocked
Question: Inspired by LSTM-minus's success in dependency and selection parsing, what model did they propose?
Evidence: The LSTM-Minus method is first proposed in BIBREF6 as a novel way to learn sentence segment embeddings for graph-based dependency parsing, i.e. estimating the most likely dependency tree given an input sentence. For each dependency pair, they divide a sentence into three segments (prefix, infix and suffix), and LSTM-Minus is used to represent each segment. They apply a single LSTM to the whole sentence and use the difference between two hidden states INLINEFORM0 to represent the segment from word INLINEFORM1 to word INLINEFORM2 . This enables their model to learn segment embeddings from information both outside and inside the segments and thus enhancing their model ability to access to sentence-level information. The intuition behind the method is that each hidden vector INLINEFORM3 can capture useful information before and including the word INLINEFORM4 .
Shortly after, lstm-minusconstituency use the same method on the task of constituency parsing, as the representation of a sentence span, extending the original uni-directional LSTM-Minus to the bi-directional case. More recently, inspired by the success of LSTM-Minus in both dependency and constituency parsing, lstm-minusdiscourse extend the technique to discourse parsing. They propose a two-stage model consisting of an intra-sentential parser and a multi-sentential parser, learning contextually informed representations of constituents with LSTM-Minus, at the sentence and document level, respectively.
Similarly, in this paper, when deciding if a sentence should be included in the summary, the local context of that sentence is captured by applying LSTM-Minus at the document level, to represent the sub-sequence of sentences of the document (i.e., the topic/section) the target sentence belongs to.
More recently, inspired by the success of LSTM-Minus in both dependency and constituency parsing, lstm-minusdiscourse extend the technique to discourse parsing. They propose a two-stage model consisting of an intra-sentential parser and a multi-sentential parser, learning contextually informed representations of constituents with LSTM-Minus, at the sentence and document level, respectively.
Answer: They propose a two-stage model consisting of an intra-sentential parser and a multi-sentential parser, learning contextually informed representations of constituents with LSTM-Minus, at the sentence and document level, respectively.
Question: How to lose weight?
Answer: 1.	Drink Plenty of water
2.	Take proper sleep
3.	Eat high fibre foods
4.	Avoid junk foods
5.	Eat Regular meals
6.	Eat plenty of fruit and veggies
7.	Exercise thrice a week
Question: Classify each of the following as a eukaryote or prokaryote: E. Coli, worm, grouper, algae, salmonella, porcupine
Answer: E. Coli: Prokaryote
Worm: Eukaryote
Grouper: Eukaryote
Algae: Eukaryote
Salmonella: Prokaryote
Porcupine: Eukaryote
Question: What dataset of tweets is used?
Evidence: Our dataset contains tweets about `ObamaCare' in USA collected during march 2010. It is divided into three subsets (train, dev, and test). Some tweets are manually annotated with one of the following classes.
Our dataset contains tweets about `ObamaCare' in USA collected during march 2010. 
Answer: The answers are shown as follows:
* tweets about `ObamaCare' in USA collected during march 2010
Question: Which of the following instruments are brass instruments? Flute, trumpet, clarinet, piano, French horn, guitar.
Answer: The trumpet and French horn are brass instruments, which also includes the trombone and tuba.
Question: What does their system consist of?
Evidence: In this paper, in spite of previous statements, we present a system that uses rule-based and dictionary-based methods combined (in a way we prefer to call resource-based). Our final goals in the paper are two-fold: on the one hand, to describe our system, developed for the PharmaCoNER shared task, dealing with the annotation of some of the nes in health records (namely, pharmacological, chemical and biomedical entities) using a revisited version of rule- and dictionary-based approaches; and, on the other hand, to give pause for thought about the quality of datasets (and, thus, the fairness) with which systems of this type are evaluated, and to highlight the key role of resource-based systems in the validation and consolidation of both the annotation guidelines and the human annotation practices.
In this paper, in spite of previous statements, we present a system that uses rule-based and dictionary-based methods combined (in a way we prefer to call resource-based). 
Answer: The answers are shown as follows:
* rule-based and dictionary-based methods
Question: Which off-the-shelf tools do they use on QuaRel?
Evidence: We use four systems to evaluate the difficulty of this dataset. (We subsequently present two new models, extending the baseline neural semantic parser, in Sections SECREF36 and SECREF44 ). The first two are an information retrieval system and a word-association method, following the designs of BIBREF26 Clark2016CombiningRS. These are naive baselines that do not parse the question, but nevertheless may find some signal in a large corpus of text that helps guess the correct answer. The third is a CCG-style rule-based semantic parser written specifically for friction questions (the QuaRel INLINEFORM0 subset), but prior to data being collected. The last is a state-of-the-art neural semantic parser. We briefly describe each in turn.
We use four systems to evaluate the difficulty of this dataset.
 The first two are an information retrieval system and a word-association method, following the designs of BIBREF26 Clark2016CombiningRS. These are naive baselines that do not parse the question, but nevertheless may find some signal in a large corpus of text that helps guess the correct answer. The third is a CCG-style rule-based semantic parser written specifically for friction questions (the QuaRel INLINEFORM0 subset), but prior to data being collected. The last is a state-of-the-art neural semantic parser. We briefly describe each in turn.
Answer: The answers are shown as follows:
* information retrieval system
* word-association method
*  CCG-style rule-based semantic parser written specifically for friction questions
* state-of-the-art neural semantic parser
Question: What is a chord in guitar music?
Answer: A chord is composed of 3 or more tones, typically. The interval between each tone determines the type of chord. For instance, a major chord is composed of 1, 3, and 5 (tones on the scale of the root, or 1, tone). A minor chord, on the other hand, is composed of 1, flatted 3, and 5. The C major chord is comprised of C, E, and G tones whereas the C minor chord is comprised of C, E minor and G tones.
Question: Is this work the first to apply EGL to speech recognition in particular?
Evidence: State-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize. Labeling thousands of hours of audio, however, is expensive and time-consuming. A natural question to ask is how to achieve better generalization with fewer training examples. Active learning studies this problem by identifying and labeling only the most informative data, potentially reducing sample complexity. How much active learning can help in large-scale, end-to-end ASR systems, however, is still an open question.
The speech recognition community has generally identified the informativeness of samples by calculating confidence scores. In particular, an utterance is considered informative if the most likely prediction has small probability BIBREF1 , or if the predictions are distributed very uniformly over the labels BIBREF2 . Though confidence-based measures work well in practice, less attention has been focused on gradient-based methods like Expected Gradient Length (EGL) BIBREF3 , where the informativeness is measured by the norm of the gradient incurred by the instance. EGL has previously been justified as intuitively measuring the expected change in a model's parameters BIBREF3 .We formalize this intuition from the perspective of asymptotic variance reduction, and experimentally, we show EGL to be superior to confidence-based methods on speech recognition tasks. Additionally, we observe that the ranking of samples scored by EGL is not correlated with that of confidence scoring, suggesting EGL identifies aspects of an instance that confidence scores cannot capture.
In BIBREF3 , EGL was applied to active learning on sequence labeling tasks, but our work is the first we know of to apply EGL to speech recognition in particular. Gradient-based methods have also found applications outside active learning. For example, BIBREF4 suggests that in stochastic gradient descent, sampling training instances with probabilities proportional to their gradient lengths can speed up convergence. From the perspective of variance reduction, this importance sampling problem shares many similarities to problems found in active learning.
In [4], EGL was applied to active learning on sequence labeling tasks, but our work is the first we know of to apply EGL to speech recognition in particular.
Answer: Yes, it is.
Question: give me a summary of the American revolution
Evidence: n/a
Answer: The American Revolution was a five-year long war that started in 1775. The colonists of Great Britain were tired of being taxed endlessly by King George lll. The revolution started when some of Britain's forces assaulted revolutionary colonists at Lexington. This convinced many people to fight for the colonists. Finally, after many long battles and suffering, at the battle of Yorktown in 1781, Great Britain's forces surrendered to General Roshambo and General Washington. King George lll passed the Treaty of Paris in 1783. The colonists, now free from Britain, started America. The political system was also created, with George Washington becoming the first president.
Question: Are there any inherent modeling challenges in forecasting emerging conversational properties?
Evidence: Online discussions often derail into toxic exchanges between participants. Recent efforts mostly focused on detecting antisocial behavior after the fact, by analyzing single comments in isolation. To provide more timely notice to human moderators, a system needs to preemptively detect that a conversation is heading towards derailment before it actually turns toxic. This means modeling derailment as an emerging property of a conversation rather than as an isolated utterance-level event.  ::: Forecasting emerging conversational properties, however, poses several inherent modeling challenges. First, since conversations are dynamic, a forecasting model needs to capture the flow of the discussion, rather than properties of individual comments. Second, real conversations have an unknown horizon: they can end or derail at any time; thus a practical forecasting model needs to assess the risk in an online fashion, as the conversation develops. In this work we introduce a conversational forecasting model that learns an unsupervised representation of conversational dynamics and exploits it to predict future derailment as the conversation develops. By applying this model to two new diverse datasets of online conversations with labels for antisocial events, we show that it outperforms state-of-the-art systems at forecasting derailment.
Forecasting emerging conversational properties, however, poses several inherent modeling challenges. First, since conversations are dynamic, a forecasting model needs to capture the flow of the discussion, rather than properties of individual comments. Second, real conversations have an unknown horizon: they can end or derail at any time; thus a practical forecasting model needs to assess the risk in an online fashion, as the conversation develops.
Answer: A forecasting model needs to capture the flow of the discussion and real conversations can end or derail at any time.
Question: How was the San Francisco bay created?
Evidence: Until the last ice age, the basin which is now filled by the San Francisco Bay was a large linear valley with small hills, similar to most of the valleys of the Coast Ranges. As the great ice sheets began to melt, around 11,000 years ago, the sea level started to rise. By 5000 BC the sea level rose 300 feet (90 m), filling the valley with water from the Pacific. The valley became a bay, and the small hills became islands.
Answer: Around 11,000 years ago the earth began to wam and melt the great ice sheets, raising sea levels and filling the valley that the San Francisco Bay now lives in.
Question: List 6 different types of rain in Seattle
Answer: 1. Mist
2. Drizzle
3. Sleet
4. Downpour
5. Freezing Rain
6. Sun Shower
Question: What color is Perpetual Spinach?
Evidence: "Chard or Swiss chard (/tʃɑːrd/; Beta vulgaris subsp. vulgaris, Cicla Group and Flavescens Group) is a green leafy vegetable. In the cultivars of the Flavescens Group, the leaf stalks are large and often prepared separately from the leaf blade; the Cicla Group is the leafy spinach beet. The leaf blade can be green or reddish; the leaf stalks are usually white, yellow or red.

Chard, like other green leafy vegetables, has highly nutritious leaves. Chard has been used in cooking for centuries, but because it is the same species as beetroot, the common names that cooks and cultures have used for chard may be confusing; it has many common names, such as silver beet, perpetual spinach, beet spinach, seakale beet, or leaf beet."
Answer: The leaf blade can be green or reddish; the leaf stalks are usually white, yellow or red.
Question: According to this paragraph about UVA, what is the PAC room?
Evidence: The student life building is called Newcomb Hall. It is home to the Student Activities Center (SAC) and the Media Activities Center (MAC), where student groups can get leadership consulting and use computing and copying resources, as well as several meeting rooms for student groups. Student Council, the student self-governing body, holds meetings Tuesdays at 6:30 p.m. in the Newcomb South Meeting Room. Student Council, or "StudCo", also holds office hours and regular committee meetings in the newly renovated Newcomb Programs and Council (PAC) Room. The PAC also houses the University Programs Council and Class Councils. Newcomb basement is home to both the office of the independent student newspaper The Declaration, The Cavalier Daily, and the Consortium of University Publications.
Answer: The PAC room is the Newcomb Programs and Council Room, that also houses the University Programs Council and Class Councils.
Question: Have the candidates given their consent to have their videos used for the research?
Evidence: We have decided to focus on only one specific type of job: sales positions. After filtering based on specific job titles from the ROME Database, a list of positions was selected and verified by the authors and an expert from the Human Resources (HR). Finally, in a collaboration with an HR industry actor, we have obtained a dataset of French video interviews comprising more than 475 positions and 7938 candidates. As they watch candidates' videos, recruiters can like, dislike, shortlist candidates, evaluate them on predefined criteria, or write comments. To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class. If multiple annotators have annotated the same candidates, we proceed with a majority vote. In case of a draw, the candidate is considered hirable. It is important to note that the videos are quite different from what could be produced in a laboratory setup. Videos can be recorded from a webcam, a smartphone or a tablet., meaning noisy environments and low quality equipment are par for the course. Due to these real conditions, feature extraction may fail for a single modality during a candidate's entire answer. One example is the detection of action units when the image has lighting problems. We decided to use all samples available in each modality separately. Some statistics about the dataset are available in Table TABREF33 . Although the candidates agreed to the use of their interviews, the dataset will not be released to public outside of the scope of this study due to the videos being personal data subject to high privacy constraints.
Although the candidates agreed to the use of their interviews, the dataset will not be released to public outside of the scope of this study due to the videos being personal data subject to high privacy constraints.
Answer: Yes.
Question: Can you tell me something about Stanley J. Goldberg
Evidence: Stanley J. Goldberg (born Maryland, 1939) is a special trial judge of the United States Tax Court.

Goldberg attended public schools in Baltimore, MD. He earned a B.S. from the University of Maryland, School of Business and Public Administration in 1960 and an LL.B. from the University of Maryland School of Law in 1964. He did graduate work in Federal Income Taxation at New York University. Goldberg was admitted to practice in Maryland and New Jersey, 1964 and 1967, and Federal District Court. He began his career as a Tax Attorney in January 1965 with the United States Department of Treasury, Office of Chief Counsel, Internal Revenue Service, in New York City and was initially assigned to the General Litigation function. In 1967, he was reassigned to the Tax Litigation function. In 1976, he was promoted to Special Trial Attorney, and then to Assistant District Counsel in 1984. He was appointed a Special Trial Judge of the United States Tax Court on August 4, 1985.

In 1992, Goldberg commented on the trend of tax protester arguments being brought before the Tax Court and dismissed with penalties.
Answer: Stanley J. Goldberg (born Maryland, 1939) is a special trial judge of the United States Tax Court.

Goldberg attended public schools in Baltimore, MD. He earned a B.S. from the University of Maryland, School of Business and Public Administration in 1960 and an LL.B. from the University of Maryland School of Law in 1964. He did graduate work in Federal Income Taxation at New York University. Goldberg was admitted to practice in Maryland and New Jersey, 1964 and 1967, and Federal District Court. 

He began his career as a Tax Attorney in January 1965 with the United States Department of Treasury, Office of Chief Counsel, Internal Revenue Service, in New York City and was initially assigned to the General Litigation function. In 1967, he was reassigned to the Tax Litigation function. In 1976, he was promoted to Special Trial Attorney, and then to Assistant District Counsel in 1984. He was appointed a Special Trial Judge of the United States Tax Court on August 4, 1985.

In 1992, Goldberg commented on the trend of tax protester arguments being brought before the Tax Court and dismissed with penalties.
Question: What tool should I use to cut wood
Answer: The answer to this largely depends on what kind of wood and what type of cut is intended. When cutting wood thinner, a table saw is the best tool. When cutting long wood to be shorter, a miter saw would be better. A circular saw with a straight edge can also be used in place of a miter saw. If cutting designs or circles, a jigsaw or scroll saw would be best. It is best to use the proper type of saw for the cut at hand in order to get the best result and prevent injuries.
Question: What are some tips for traveling with a toddler?
Answer: Toddlers are innately adventurous. They like to explore new things, places and people as it stimulates their brain. With some planning, traveling with a toddler can actually be hugely rewarding. They make memories and get exposed to different cultures, developing more empathy and learning language early on. Here are some tips for making travel easy with a toddler:

1. Carry light weight activities they are currently into. For example, sticker books, coloring or puzzles.
2. Carry a lovey in your backpack/hand luggage.
3. Carry a few of their night time reading books. 
4. Keep energy snacks handy at all times. 
5. Use a carrier if you are going to be outdoors. For example, hiking or trekking.
6. Book a morning flight if it is over 7-8 hours. Book a flight during their nap time if shorter than 7-8 hours.
7. Ask for an upgrade at the airport. 
8. Make them walk at the airport instead of using a stroller, that way they will get tired and sleep in the plane.
Question: How they compare varioius mechanisms in terms of naturalness?
Evidence: We evaluate CBA, LSA, DCA, and GMMv2b using mean opinion score (MOS) naturalness judgments produced by a crowd-sourced pool of raters. Scores range from 1 to 5, with 5 representing “completely natural speech”. The Lessac and LJ models are evaluated on their respective test sets (hence in-domain), and the results are shown in Table TABREF17. We see that for these utterances, the LSA, DCA, and GMMV2b mechanisms all produce equivalent scores around 4.3, while the content-based mechanism is a bit lower due to occasional catastrophic attention failures.
We evaluate CBA, LSA, DCA, and GMMv2b using mean opinion score (MOS) naturalness judgments produced by a crowd-sourced pool of raters. Scores range from 1 to 5, with 5 representing “completely natural speech”.
Answer: The answers are shown as follows:
* using mean opinion score (MOS) naturalness judgments produced by a crowd-sourced pool of raters