DataHammer/scimrc huggingface dataset:
Question: per the context given, how would illiterate politicians able to vote?
Evidence: A lettering guide template is a special type of template used to write uniform characters. It consists of a sheet of plastic or other material with cut-outs of letters, numbers, and other shapes used especially for creating technical drawings. For decades they have been essential for lettering a drawing nameplate so text and other designs could be made quickly and uniformly.

Although they have been superseded by the use of computers, during the greater part of the last century they were used to relatively ease the lettering process in the creation of technical drawings. They were an indispensable tool for architects and technical illustrators in general, for labeling their drawings and plans but also for the description of projects, in which it was good practice to use a lettering template to achieve uniform and well-written text.

A lettering template could also be used by people illiterate or semi-illiterate to learn to type, or improve their handwriting. In the course of political history some politicians, such as Bettino Craxi, have used them to help people with writing difficulties. They distributed cardboard templates with the sequence of characters of their last name, so they could be easily written during the voting process.
Answer: They used lettering guide templates made of cardboard with characters of their last name
Question: Extract the Greek terms in the following paragraph, along with a short definition, in bullet points.
Evidence: Meno (/ˈmiːnoʊ/; Greek: Μένων, Ménōn) is a Socratic dialogue by Plato. Meno begins the dialogue by asking Socrates whether virtue is taught, acquired by practice, or comes by nature. In order to determine whether virtue is teachable or not, Socrates tells Meno that they first need to determine what virtue is. When the characters speak of virtue, or rather arete, they refer to virtue in general, rather than particular virtues, such as justice or temperance. The first part of the work showcases Socratic dialectical style; Meno, unable to adequately define virtue, is reduced to confusion or aporia. Socrates suggests that they seek an adequate definition for virtue together. In response, Meno suggests that it is impossible to seek what one does not know, because one will be unable to determine whether one has found it.

Socrates challenges Meno's argument, often called "Meno's Paradox" or the "Learner's Paradox", by introducing the theory of knowledge as recollection (anamnesis). As presented in the dialogue, the theory proposes that souls are immortal and know all things in a disembodied state; learning in the embodied is actually a process of recollecting that which the soul knew before it came into a body. Socrates demonstrates recollection in action by posing a mathematical puzzle to one of Meno's slaves. Subsequently, Socrates and Meno return to the question of whether virtue is teachable, employing the method of hypothesis. Near the end of the dialogue, Meno poses another famous puzzle, called "The Meno Problem" or "The Value Problem for Knowledge", which questions why knowledge is valued more highly than true belief. In response, Socrates provides a famous and somewhat enigmatic distinction between knowledge and true belief.
Answer: - arete: virtues in general, i.e. instead of a particular virtue
- aporia: confusion
- anamnesis: recollection of knowledge
Question: What do the authors use for prereordering English sentences?
Evidence: In this section, we describe the languages experimented with, datasets used, the network hyper-parameters used in our experiments.
We use CFILT-preorder3 for prereordering English sentences.
Answer: CFILT-preorder3
Question: Whats the difference between a sloop, a cutter, and a ketch?
Answer: These are all categorizations of different types of sailing boats. A sloop is the most common. A sloop is a monohull sailboat with a single- rig mast supporting a main sail and and headsail. These are great boats to learn sailing on as their simple, easy to handle, and have great wind performance. 

A cutter at first look is similar to a sloop in that it also has a single mast positioned near the middle of the boat. However, a cutter uses two headsails, instead of a mainsail and headsail, and generally includes a bowsprit. This allows the boat to have better stability in rough weather due to the headsails providing a lower center of gravity. The complex rigging makes this boat more challenging to sail than the sloop, but provides an excellent option for an experienced sailor. 

A ketch has two masts. The main mast in the front stands taller than the mizzen mast behind it. The mizzen mast stands forward of the rudder post. The ketch has smaller sails and shorter masts. While this makes them slower than a sloop, they are very controllable in rough weather which makes them excellent boats for off shore sailing.
Question: What is Photosynthesis
Evidence: Photosynthesis is a process used by plants and other organisms to convert light energy into chemical energy that, through cellular respiration, can later be released to fuel the organism's activities. Some of this chemical energy is stored in carbohydrate molecules, such as sugars and starches, which are synthesized from carbon dioxide and water – hence the name photosynthesis, from the Greek phōs (φῶς), "light", and synthesis (σύνθεσις), "putting together". Most plants, algae, and cyanobacteria perform photosynthesis; such organisms are called Photoautotroph. Photosynthesis is largely responsible for producing and maintaining the oxygen content of the Earth's atmosphere, and supplies most of the energy necessary for life on Earth.
Answer: Photosynthesis is performed differently by different species but the process always begins when energy from light is absorbed by proteins called reaction centers that contain green chlorophyll (and other colored) pigments/chromophores. In plants, these proteins are held inside organelles called chloroplasts, which are most abundant in leaf cells, while in bacteria they are embedded in the plasma membrane. In these light-dependent reactions, some energy is used to strip electrons from suitable substances, such as water, producing oxygen gas. The hydrogen freed by the splitting of water is used in the creation of two further compounds that serve as short-term stores of energy, enabling its transfer to drive other reactions: these compounds are reduced nicotinamide adenine dinucleotide phosphate (NADPH) and adenosine triphosphate (ATP), the "energy currency" of cells.

In plants, algae and cyanobacteria, sugars are synthesized by a subsequent sequence of light-independent reactions called the Calvin cycle. In the Calvin cycle, atmospheric carbon dioxide is incorporated into already existing organic carbon compounds, such as ribulose bisphosphate (RuBP). Using the ATP and NADPH produced by the light-dependent reactions, the resulting compounds are then reduced and removed to form further carbohydrates, such as glucose. In other bacteria, different mechanisms such as the reverse Krebs cycle are used to achieve the same end.
Question: Why do the authors propose a novel Dual-channel Word Embedding (DWE) model?
Evidence: Word embeddings are fixed-length vector representations for words BIBREF0 , BIBREF1 . In recent years, the morphology of words is drawing more and more attention BIBREF2 , especially for Chinese whose writing system is based on logograms.
UTF8gbsn With the gradual exploration of the semantic features of Chinese, scholars have found that not only words and characters are important semantic carriers, but also stroke feature of Chinese characters is crucial for inferring semantics BIBREF3 . Actually, a Chinese word usually consists of several characters, and each character can be further decomposed into a stroke sequence which is certain and changeless, and this kind of stroke sequence is very similar to the construction of English words. In Chinese, a particular sequence of strokes can reflect the inherent semantics. As shown in the upper half of Figure FIGREF3 , the Chinese character “驾" (drive) can be decomposed into a sequence of eight strokes, where the last three strokes together correspond to a root character “马" (horse) similar to the root “clar" of English word “declare" and “clarify".
Moreover, Chinese is a language originated from Oracle Bone Inscriptions (a kind of hieroglyphics). Its character glyphs have a spatial structure similar to graphs which can convey abundant semantics BIBREF4 . Additionally, the critical reason why Chinese characters are so rich in morphological information is that they are composed of basic strokes in a 2-D spatial order. However, different spatial configurations of strokes may lead to different semantics. As shown in the lower half of Figure 1, three Chinese characters “入" (enter), “八" (eight) and “人" (man) share exactly a common stroke sequence, but they have completely different semantics because of their different spatial configurations.
In addition, some biological investigations have confirmed that there are actually two processing channels for Chinese language. Specifically, Chinese readers not only activate the left brain which is a dominant hemisphere in processing alphabetic languages BIBREF5 , BIBREF6 , BIBREF7 , but also activate the areas of the right brain that are responsible for image processing and spatial information at the same time BIBREF8 . Therefore, we argue that the morphological information of characters in Chinese consists of two parts, i.e., the sequential information hidden in root-like strokes order, and the spatial information hidden in graph-like character glyphs. Along this line, we propose a novel Dual-channel Word Embedding (DWE) model for Chinese to realize the joint learning of sequential and spatial information in characters. Finally, we evaluate DWE on two representative tasks, where the experimental results exactly validate the superiority of DWE in capturing the morphological information of Chinese.
Along this line, we propose a novel Dual-channel Word Embedding (DWE) model for Chinese to realize the joint learning of sequential and spatial information in characters.
Answer: To realize the joint learning of sequential and spatial information in characters.
Question: Which part of the United States is known as the "four corners" area?
Answer: The term four corners applies to the area where Colorado, New Mexico, Utah, and Arizona come together.
Question: What kind of tuple is a suitable one?
Evidence: Despite the similarity of the Open IE paradigm, not every extracted tuple is a suitable proposition for a concept map. To reduce the effort in the subsequent steps, we therefore want to filter out unsuitable ones. A tuple is suitable if it (1) is a correct extraction, (2) is meaningful without any context and (3) has arguments that represent proper concepts. We created a guideline explaining when to label a tuple as suitable for a concept map and performed a small annotation study. Three annotators independently labeled 500 randomly sampled tuples. The agreement was 82% ( INLINEFORM0 ). We found tuples to be unsuitable mostly because they had unresolvable pronouns, conflicting with (2), or arguments that were full clauses or propositions, conflicting with (3), while (1) was mostly taken care of by the confidence filtering in § SECREF21 .
Due to the high number of tuples we decided to automate the filtering step. We trained a linear SVM on the majority voted annotations. As features, we used the extraction confidence, length of arguments and relations as well as part-of-speech tags, among others. To ensure that the automatic classification does not remove suitable propositions, we tuned the classifier to avoid false negatives. In particular, we introduced class weights, improving precision on the negative class at the cost of a higher fraction of positive classifications. Additionally, we manually verified a certain number of the most uncertain negative classifications to further improve performance. When 20% of the classifications are manually verified and corrected, we found that our model trained on 350 labeled instances achieves 93% precision on negative classifications on the unseen 150 instances. We found this to be a reasonable trade-off of automation and data quality and applied the model to the full dataset.
The classifier filtered out 43% of the propositions, leaving 1622 per topic. We manually examined the 17k least confident negative classifications and corrected 955 of them. We also corrected positive classifications for certain types of tuples for which we knew the classifier to be imprecise. Finally, each topic was left with an average of 1554 propositions (47k in total).
A tuple is suitable if it (1) is a correct extraction, (2) is meaningful without any context and (3) has arguments that represent proper concepts.
Answer: It must be a correct extraction that is meaningful and independent and has arguments that represent proper concepts.
Question: What links the names Botvinik, Tal, Karpov, Fischer
Answer: Chess World Champs
Question: What are some good body weight exercises for the upper body?
Answer: Some good body weight exercises for the upper body include parallel bar dips, pull ups, one arm planks, and push ups.
Question: Is the semantic hierarchy representation used for any task?
Evidence: An extrinsic evaluation was carried out on the task of Open IE BIBREF7. It revealed that when applying DisSim as a preprocessing step, the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall, i.e. leading to a lower information loss and a higher accuracy of the extracted relations. For details, the interested reader may refer to niklaus-etal-2019-transforming.
An extrinsic evaluation was carried out on the task of Open IE BIBREF7.
Answer: Yes, Open IE
Question: Who won the Golden Shoe Award for the season 2018-2019?
Answer: Lionel Messi won this award for the 6th time.
Question: How does their model utilize contextual information for each work in the given sentence in a multi-task setting? setting?
Evidence: Prior work have shown that contextual information could be useful for SF. They utilize contextual information either in word level representation (i.e., via contextualize embedding e.g., BERT BIBREF0) or in the model computation graph (e.g., concatenating the context feature to the word feature BIBREF1). However, such methods fail to capture the explicit dependence between the context of the word and its label. Moreover, such limited use of contextual information (i.e., concatenation of the feature vector and context vector) in the model cannot model the interaction between the word representation and its context. In order to alleviate these issues, in this work, we propose a novel model to explicitly increase the predictability of the word label using its context and increasing the interactivity between word representations and its context. More specifically, in our model we use the context of the word to predict its label and by doing so our model learns label-aware context for each word in the sentence. In order to improve the interactivity between the word representation and its context, we increase the mutual information between the word representations and its context. In addition to these contributions, we also propose an auxiliary task to predict which labels are expressed in a given sentence. Our model is trained in a mutli-tasking framework. Our experiments on a SF dataset for identifying semantic concepts from natural language request to edit an image show the superiority of our model compared to previous baselines. Our model achieves the state-of-the-art results on the benchmark dataset by improving the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction.
More specifically, in our model we use the context of the word to predict its label and by doing so our model learns label-aware context for each word in the sentence. In order to improve the interactivity between the word representation and its context, we increase the mutual information between the word representations and its context.
Answer: The answers are shown as follows:
* we use the context of the word to predict its label and by doing so our model learns label-aware context for each word in the sentence
Question: Why is Captain America Steve Rogers the leader of the Avengers?
Answer: Steve Rogers also know as Captain America is the leader of the Avengers because he is the first Avenger. He was a the first and most successful created super solider in the era of world war 2. He is a master of warfare. He has trained in every technique of martial arts. He has a strong sense of right and wrong. He is courageous, committed and honorable. His experience in World War 2 leading the Howling Commandos gives him the most knowledge to lead the Avengers. He protects the team and understands how to make the difficult decisions.
Question: what does it mean to have brunette hair?
Answer: A person with brunette hair, or a brunette, is a person with brown or black hair color. People typically fall into the category of blonde, brunette, and red hair. It is sometimes misconceived to be only brown hair but it signifies all darker hair including black hair color.
Question: Which episode of Game of Thrones does Dany go to the House of the Undying?
Answer: Daenerys Targaryen visits the House of the Undying in season two, episode five ("Harrenhal")
Question: What dataset do they use for training FastQA?
Evidence: BioASQ is a semantic indexing, question answering (QA) and information extraction challenge BIBREF0 . We participated in Task B of the challenge which is concerned with biomedical QA. More specifically, our system participated in Task B, Phase B: Given a question and gold-standard snippets (i.e., pieces of text that contain the answer(s) to the question), the system is asked to return a list of answer candidates.
The fifth BioASQ challenge is taking place at the time of writing. Five batches of 100 questions each were released every two weeks. Participating systems have 24 hours to submit their results. At the time of writing, all batches had been released.
The questions are categorized into different question types: factoid, list, summary and yes/no. Our work concentrates on answering factoid and list questions. For factoid questions, the system's responses are interpreted as a ranked list of answer candidates. They are evaluated using mean-reciprocal rank (MRR). For list questions, the system's responses are interpreted as a set of answers to the list question. Precision and recall are computed by comparing the given answers to the gold-standard answers. F1 score, i.e., the harmonic mean of precision and recall, is used as the official evaluation measure .
Most existing biomedical QA systems employ a traditional QA pipeline, similar in structure to the baseline system by weissenborn2013answering. They consist of several discrete steps, e.g., named-entity recognition, question classification, and candidate answer scoring. These systems require a large amount of resources and feature engineering that is specific to the biomedical domain. For example, OAQA BIBREF1 , which has been very successful in last year's challenge, uses a biomedical parser, entity tagger and a thesaurus to retrieve synonyms.
Our system, on the other hand, is based on a neural network QA architecture that is trained end-to-end on the target task. We build upon FastQA BIBREF2 , an extractive factoid QA system which achieves state-of-the-art results on QA benchmarks that provide large amounts of training data. For example, SQuAD BIBREF3 provides a dataset of $\approx 100,000$ questions on Wikipedia articles. Our approach is to train FastQA (with some extensions) on the SQuAD dataset and then fine-tune the model parameters on the BioASQ training set.
Note that by using an extractive QA network as our central component, we restrict our system's responses to substrings in the provided snippets. This also implies that the network will not be able to answer yes/no questions. We do, however, generalize the FastQA output layer in order to be able to answer list questions in addition to factoid questions.
Our approach is to train FastQA (with some extensions) on the SQuAD dataset and then fine-tune the model parameters on the BioASQ training set.
Answer: SQuAD dataset.
Question: Which state is the city of Springfield from in the Simpsons?
Answer: The popular and long running show The Simpsons have intentionally left out from which state the home town of Springfield is with the intention of relating to everyone's Springfield.  There have been several references to possible states to keep fans guessing and has become part of the playful nature of the show.   Alas, in 2012, the show's creator revealed that it is based on Springfield, Oregon.
Question: what is considered the birthplace of mountain biking?
Answer: Fairfax California is considered the birthplace of mountain biking.  Local high school students who grew up in the area would take old single-speed bikes with coaster brakes up local fire roads and then ride them down the hills as fast as they could.  Eventually they started adding gears to the bikes so that they could pedal them up the hill as well.  The most famous trail was named Repack, because often after the races riders would have to re-pack the coaster brakes with grease because the grease would vaporize from the heat from the hard braking as they raced down the hill.  Modern bike brands like Fisher, Ritchey, Klein, Breezer, and Specialized all had their start in the Marin County foothills north of San Francisco, in the small town of Fairfax.
Question: What is castling in chess?
Answer: Castling is a unique move that can be used to protect the king and develop a rook at the same time. It is the only move in chess where two pieces can move at the same time. During castling, the king moves two steps to its right or left and the rook moves across the king and goes to the position next to the king. Castling can be done only when the following two conditions are satisfied. First, there must not be any other pieces in between the king and the rook. Second, the king and the rook must not have been moved before.
Question: What datasets are used for testing sentiment classification and reading comprehension?
Evidence: In the sentiment classification task, we tried our model on the following public datasets.
MR Movie reviews with one sentence each. Each review is classified into positive or negative BIBREF18.
IMDB Movie reviews from IMDB website, where each movie review is labeled with binary classes, either positive or negative BIBREF19. Note that each movie review may contain several sentences.
SUBJ$^1$ Movie review labeled with subjective or objective BIBREF20.
We also tested our CRU model in the cloze-style reading comprehension task. We carried out experiments on the public datasets: CBT NE/CN BIBREF25. The CRU model used in these experiments is the deep-enhanced type with the convolutional filter length of 3. In the re-ranking step, we also utilized three features: Global LM, Local LM, Word-class LM, as proposed by BIBREF10, and all LMs are 8-gram trained by SRILM toolkit BIBREF27. For other settings, such as hyperparameters, initializations, etc., we closely follow the experimental setups as BIBREF10 to make the experiments more comparable.
In the sentiment classification task, we tried our model on the following public datasets.
MR Movie reviews with one sentence each.
IMDB Movie reviews from IMDB website, where each movie review is labeled with binary classes, either positive or negative BIBREF19.
SUBJ$^1$ Movie review labeled with subjective or objective BIBREF20.
We also tested our CRU model in the cloze-style reading comprehension task. We carried out experiments on the public datasets: CBT NE/CN BIBREF25.
Answer: The answers are shown as follows:
* CBT NE/CN
* MR Movie reviews
* IMDB Movie reviews
* SUBJ
Question: What are a few different ways to spend a free weekend
Answer: - Write LLM Prompts
- Go hiking
- Play Video Games
- Watch Netflix
- Cook Molecular Gastronomy dishes