DataHammer/scimrc huggingface dataset:
Question: The paper "Attention is all you need" proposed the Transformer model for the first time, which had a profound impact on the subsequent LLM. Please give the main core idea of Transformer described in this paper
Evidence: A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing (NLP) and computer vision (CV).

Like recurrent neural networks (RNNs), transformers are designed to process sequential input data, such as natural language, with applications towards tasks such as translation and text summarization. However, unlike RNNs, transformers process the entire input all at once. The attention mechanism provides context for any position in the input sequence. For example, if the input data is a natural language sentence, the transformer does not have to process one word at a time. This allows for more parallelization than RNNs and therefore reduces training times.

Transformers were introduced in 2017 by a team at Google Brain and are increasingly becoming the model of choice for NLP problems, replacing RNN models such as long short-term memory (LSTM). The additional training parallelization allows training on larger datasets. This led to the development of pretrained systems such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), which were trained with large language datasets, such as the Wikipedia Corpus and Common Crawl, and can be fine-tuned for specific tasks.
Answer: This paper is by Vaswani, Ashish & Shazeer, Noam & Parmar, Niki & Uszkoreit, Jakob & Jones, Llion & Gomez, Aidan & Kaiser, Lukasz & Polosukhin, Illia. and published in 2017. It laid the foundation for the subsequent GPT series of models and BERT series of models. The GPT model uses the decoder part of Transfomer, while the BERT model uses the encoder part of Transfomer. The main core architecture of the Transformer model includes: Self-Attention Mechanism, Positional Encoding, Encoder-Decoder Architecture, Multi-Head Attension.
Transformer effectively solves the difficult parallel problem of the previous RNN/LSTM model by using the Attention mechanism. Using Self-Attention solves the problem of limited labeled data in NLP. The sequence information in the input text is preserved through Positional Encoding. And Multi-Head Attention allows Transformer to learn different patterns of training data similar to Channel concept in CNN model.
Question: What classification approaches were experimented for this task?
Evidence: We conducted cross-validation experiments using various values of $t$ and $m$. Table TABREF26 shows the results for the best values of $t$ and $m$ obtained. The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. To enable a fair comparison we used the same input features in all systems. These input features combine information from the question and the input sentence and are shown in Fig. FIGREF16. The features are based on BIBREF12, and are the same as in BIBREF1, plus the addition of the position of the input snippet. The best SVC and SVR parameters were determined by grid search.
The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: “NNC top 5” uses classification labels as described in Section SECREF3, and “NNC SU4 F1” uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence. Of interest is the fact that “NNC SU4 F1” outperforms the neural regressor. We have not explored this further and we presume that the relatively good results are due to the fact that ROUGE values range between 0 and 1, which matches the full range of probability values that can be returned by the sigmoid activation of the classifier final layer.
The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively.
The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: “NNC top 5” uses classification labels as described in Section SECREF3, and “NNC SU4 F1” uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence.
Answer: The answers are shown as follows:
* NNC SU4 F1
* NNC top 5
* Support Vector Classification (SVC)
Question: What do the authors hypothesize in the domains such as student reflections?
Evidence: Recently, with the emergence of neural seq2seq models, abstractive summarization methods have seen great performance strides BIBREF0, BIBREF1, BIBREF2. However, complex neural summarization models with thousands of parameters usually require a large amount of training data. In fact, much of the neural summarization work has been trained and tested in news domains where numerous large datasets exist. For example, the CNN/DailyMail (CNN/DM) BIBREF3, BIBREF4 and New York Times (NYT) datasets are in the magnitude of 300k and 700k documents, respectively. In contrast, in other domains such as student reflections, summarization datasets are only in the magnitude of tens or hundreds of documents (e.g., BIBREF5). We hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models, and we will indeed later show that this is the case for student reflections.
To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, we combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
In contrast, in other domains such as student reflections, summarization datasets are only in the magnitude of tens or hundreds of documents (e.g., (Luo and Litman 2015)). We hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models, and we will indeed later show that this is the case for student reflections
Answer: The authors hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models.
Question: Identify which instrument is string or percussion: Skrabalai, Zeze
Answer: Zeze is string, Skrabalai is percussion.
Question: What are the administrative divisions in Morocco ?
Evidence: Morocco is officially divided into 12 regions, which, in turn, are subdivided into 62 provinces and 13 prefectures.
Answer: Tanger-Tetouan-Al Hoceima
Oriental
Fès-Meknès
Rabat-Salé-Kénitra
Béni Mellal-Khénifra
Casablanca-Settat
Marrakesh-Safi
Drâa-Tafilalet
Souss-Massa
Guelmim-Oued Noun
Laâyoune-Sakia El Hamra
Dakhla-Oued Ed-Dahab
Question: What is the name of the national museum of the Netherlands?
Answer: The national museum of the Netherlands is called the Rijksmuseum.
Question: What is the depth of focus of a camera?
Answer: depth of focus is the range of distances that will be in focus in a picture taken by the camera with specific settings. Depending on the settings of the camera, the depth of focus can be very small, ranging in millimeters or centimeters to very large - hundreds of meters. The most important setting that will determine the depth of focus is the aperture used in the lens when the picture is taken. The aperture is how widely open the lens is and how much light is allowed to pass through it. A low f-stop or big aperture will minimize the depth of field, while a high f-stop or small aperture will maximize it. Depth of field is an element that photographers use in composition to blur or sharpen elements of the photograph, thus emphasizing or de-emphasizing certain aspects.
Question: Should I buy a convertible?
Answer: The decision to buy a convertible depends mainly on how you plan to use the car and the climate you live in. If you live in a warm climate year-round, a convertible is a great choice for enjoying the weather while you drive. If you live in a location with more traditional seasons, consider a convertible that has a hard-top rather than a soft-top. This will be more robust against rain and snow, although these vehicles tend to be a little more expensive. Also consider how you plan to use the car: if you find yourself stuck in traffic most of the time during a commute, for example, you may not enjoy owning a convertible as much because you may feel hot and the pollution may bother your lungs.
Question: What are the two primary dimensions that events are coded on?
Evidence: As has been noted, events are coded on two primary dimensions: event codes and actors. Most political event datasets are dominated by low-level political events that lack a strong valence. These are usually routine events such as statements that occur often. Figures 4 and 5 show the breakdown of event types within the current Phoenix data, both of which confirm this existing pattern. The addition of the 0 quad class category was designed to capture these types of events so that they can be easily removed to allow end users to easily focus on more substantive political events. Following these lower-level event types, the event codes 19 and 17, “Fight” and “Coerce” respectively, are the next most common. The prevalence of 19 codes is unsurprising given that the underlying dictionaries were structured in such a way that many events defaulted to this category.
Issue coding based on simple keyword lookups is used in Phoenix to provide further context to events. Figure 5 shows that the most common theme in the issue codings is terrorist organizations, followed by general security topics and the European Union. The hope for these issue codings is that events that might not have clear actors can be further illuminated by an issue coding, such as in the case of an attack against an unspecified armed group that could also have the issue coding of “Terror Group.”
As has been noted, events are coded on two primary dimensions: event codes and actors.
Answer: Event codes and actors.
Question: Identify which instrument is string or woodwind: Igil, Koudi
Answer: Koudi is woodwind, Igil is string.
Question: What are some examples of diseconomies of scale?
Evidence: In microeconomics, economies of scale are the cost advantages that enterprises obtain due to their scale of operation, and are typically measured by the amount of output produced per unit of time. A decrease in cost per unit of output enables an increase in scale. At the basis of economies of scale, there may be technical, statistical, organizational or related factors to the degree of market control. This is just a partial description of the concept.

Economies of scale apply to a variety of the organizational and business situations and at various levels, such as a production, plant or an entire enterprise. When average costs start falling as output increases, then economies of scale occur. Some economies of scale, such as capital cost of manufacturing facilities and friction loss of transportation and industrial equipment, have a physical or engineering basis.

The economic concept dates back to Adam Smith and the idea of obtaining larger production returns through the use of division of labor. Diseconomies of scale are the opposite.

Economies of scale often have limits, such as passing the optimum design point where costs per additional unit begin to increase. Common limits include exceeding the nearby raw material supply, such as wood in the lumber, pulp and paper industry. A common limit for a low cost per unit weight commodities is saturating the regional market, thus having to ship product uneconomic distances. Other limits include using energy less efficiently or having a higher defect rate.

Large producers are usually efficient at long runs of a product grade (a commodity) and find it costly to switch grades frequently. They will, therefore, avoid specialty grades even though they have higher margins. Often smaller (usually older) manufacturing facilities remain viable by changing from commodity-grade production to specialty products.

Economies of scale must be distinguished from economies stemming from an increase in the production of a given plant. When a plant is used below its optimal production capacity, increases in its degree of utilization bring about decreases in the total average cost of production. As noticed, among the others, by Nicholas Georgescu-Roegen (1966) and Nicholas Kaldor (1972) these economies are not economies of scale.
Answer: Examples of diseconomies of scale include: using energy less efficiently, over-saturating a regional market, exceeding the local raw material supply, higher production switching costs leading to inability to pivot to higher margin products (reduced agility), and having a higher defect rate.
Question: Tell me the mascots for the Generation 2 Pokemon games
Answer: The mascots for the Generation 2 Pokemon games are Lugia, Ho-oh and Suicune
Question: Add additional details about what chess is
Evidence: Isidore Censer was a Belgian chess player.
Answer: Isidore Censer was a Belgian chess player.  Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king.
Question: I am planning to buy a bicycle; can u advise what to look out for when buying?
Answer: Firstly it depends on what you want to use the bicycle for. Would it be for work, grocery, marathon or just for some casual runs around the neighborhood park. Based on that, look a few brands and their prices. Get an idea of the price and set aside a budget. Pick a few models around that budget and look at customer reviews, video reviews are more helpful than written reviews in my opinion.
Question: What are common methods for processing coffee?
Answer: Natural, washed and honey are three common methods for processing coffee. Honey wash often comes in three varieties, yellow, red, and black based on the amount of pulp left on the bean. There are other less common processing methods like carbonic maceration, acetic and lactic, Kenyan processing among others.
Question: Which datasets are used for evaluation?
Evidence: In our experiments, we use as input the 2210 tokenized sentences of the Stanford Sentiment Treebank test set BIBREF2 , preprocessing them by lowercasing as was done in BIBREF8 . On five-class sentiment prediction of full sentences (very negative, negative, neutral, positive, very positive) the model achieves 46.3% accuracy, and for binary classification (positive vs. negative, ignoring neutral sentences) the test accuracy is 82.9%.
In our experiments, we use as input the 2210 tokenized sentences of the Stanford Sentiment Treebank test set BIBREF2 , preprocessing them by lowercasing as was done in BIBREF8 .
Answer: The answers are shown as follows:
* Stanford Sentiment Treebank
Question: Identify which instrument is string or percussion: Babendil, Bowed guitar
Answer: Bowed guitar is string, Babendil is percussion.
Question: What are the five biggest centers of Sufism?
Evidence: Sufism in Bangladesh is more or less similar to that in the whole Indian subcontinent. India, it is claimed, is one of the five great centers of Sufism, the other four being Persia (including central Asia), Baghdad, Syria, and North Africa. Sufi saints flourished in Hindustan (India) preaching the mystic teachings of Sufism that easily reached the common people, especially the spiritual truth seekers in India. Sufism in Bangladesh is also called pirism, after the pirs or teachers in the Sufi tradition (also called Fakir).
Answer: The five main centers of Sufism are India, Persia, Baghdad, Syria, and North Africa.
Question: Who were the Sumerians?
Answer: The Sumerian culture (3000-2350 BC) is regarded as humanity's first urban civilization. It originated in southern Mesopotamia, between the Tigris and Euphrates rivers, and is related to Neolithic cultures, particularly the Uruk culture. They invented the wheel and writing, had a division of labor-based organization, and were incredible builders. Their Sumerian language is unrelated to any other known language. Sumerian is a cuneiform language, with ideograms, symbols, and abstract forms representing ideas rather than specific words or sounds.
Question: What does it take for a leader to notice & appreciate you in the organization
Answer: A leader's time is usually busy and a leader is usually spending time thinking about the organization's highest priorities. Helping the leader deliver on organization's priorities is a smart way to get him to notice & appreciate you in the organization
Question: Identify which instrument is string or percussion: Kenong, Zeze
Answer: Zeze is string, Kenong is percussion.
Question: Identify which instrument is string or percussion: Kepyak, Koto
Answer: Koto is string, Kepyak is percussion.
Question: What translationese effects are seen in the analysis?
Evidence: Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set.
We also characterised the originals and translations according to coreference features such as total number of chains and mentions, average chain length and size of the longest chain. We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do. As future work, we consider a more detailed comparison of the human and machine translations, and analyse the purpose of the additional mentions added by the NMT systems. It would be also interesting to evaluate of the quality of the automatically computed coreferences chains used for S3.
Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set.
We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do.
Answer: The answers are shown as follows:
* potentially indicating a shining through effect
* explicitation effect
Question: How many richly annotated questions are contained in the dataset?
Evidence: Understanding and applying qualitative knowledge is a fundamental facet of intelligence. For example, we may read that exercise improves health, and thus decide to spend more time at the gym; or that larger cars cause more pollution, and thus decide to buy a smaller car to be environmentally sensitive. These skills require understanding the underlying qualitative relationships, and being able to apply them in specific contexts.
To promote research in this direction, we present the first open-domain dataset of qualitative relationship questions, called QuaRTz (“Qualitative Relationship Test set”). Unlike earlier work in qualitative reasoning, e.g., BIBREF0, the dataset is not restricted to a small, fixed set of relationships. Each question $Q_i$ (2-way multiple choice) is grounded in a particular situation, and is paired with a sentence $K_i$ expressing the general qualitative knowledge needed to answer it. $Q_i$ and $K_i$ are also annotated with the properties being compared (Figure FIGREF1). The property annotations serve as supervision for a potential semantic parsing based approach. The overall task is to answer the $Q_i$ given the corpus $K = \lbrace K_i\rbrace $.
We test several state-of-the-art (BERT-based) models and find that they are still substantially (20%) below human performance. Our contributions are thus (1) the dataset, containing 3864 richly annotated questions plus a background corpus of 400 qualitative knowledge sentences; and (2) an analysis of the dataset, performance of BERT-based models, and a catalog of the challenges it poses, pointing the way towards solutions.
Our contributions are thus (1) the dataset, containing 3864 richly annotated questions plus a background corpus of 400 qualitative knowledge sentences; and (2) an analysis of the dataset, performance of BERT-based models, and a catalog of the challenges it poses, pointing the way towards solutions
Answer: 3864 richly annotated questions.
Question: Can their method of creating more informative visuals be applied to tasks other than turn taking in conversations?
Evidence: Although attention in deep neural networks was not initially introduced to inform observers, but to help a model make predictions, it can also be used to inform. In the instances where a model thinks all historical samples should be considered equally important in a sequential analysis task, we must look elsewhere for a computationally inexpensive means to understand what happened at the stopping point. In this paper, we have introduced such a means by monitoring attention changes over the sequential analysis to inform observers. This method introduces negligible overhead, an important consideration in real-time systems, and is not tied to the implementation details or task of the model, other than the prerequisite of an attention layer.
In the instances where a model thinks all historical samples should be considered equally important in a sequential analysis task, we must look elsewhere for a computationally inexpensive means to understand what happened at the stopping point.
Answer: The answers are shown as follows:
* computationally inexpensive means to understand what happened at the stopping point
Question: Why are in person events important to business growth?
Answer: In person events are incredibly important to business growth because they allow a company to connect with it's customers and prospects directly.  The business can create experiences, connections between it's employees and customers, and between customers to help form a community of people who align based on self identifiers like interests, technologies, industry and solutions.  These connections help build strong bonds between customer and brand by creating strong feelings of connectedness and inclusion through experiences during their in person event.
Question: What dimensions of word embeddings do they produce using factorization?
Evidence: As is common in the literature BIBREF4 , BIBREF8 , we use 300-dimensional vectors for our embeddings and all word vectors are normalized to unit length prior to evaluation.
As is common in the literature BIBREF4 , BIBREF8 , we use 300-dimensional vectors for our embeddings and all word vectors are normalized to unit length prior to evaluation.
Answer: The answers are shown as follows:
* 300-dimensional vectors