DataHammer/scimrc huggingface dataset:
Question: Please describe what Business Process Mapping is and why it is used
Evidence: Business process mapping refers to activities involved in defining what a business entity does, who is responsible, to what standard a business process should be completed, and how the success of a business process can be determined.

The main purpose behind business process mapping is to assist organizations in becoming more effective. A clear and detailed business process map or diagram allows outside firms to come in and look at whether or not improvements can be made to the current process.

Business process mapping takes a specific objective and helps to measure and compare that objective alongside the entire organization's objectives to make sure that all processes are aligned with the company's values and capabilities.

International Organization for Standardization or ISO 9001 : 2015 encourages a process approach to quality management. It is important to understand how each process relates to other processes within the organization and how those interactions impact Quality Management.
Answer: Business Process Mapping describes the capture of a set of business activities that deliver an outcome.  Mapping is usually performed using visual tools that use standardized shapes to indicate entities.  Mapping involves capturing activities, the roles performing the activities, systems and tools used to execute the activities and inputs and outputs from the activities.  Process mapping is frequently conducted in cross functional workshops with a facilitator capturing the activities performed by Subject Matter Experts (SMEs).  Common uses of process mapping include:
* automation
* compliance documentation
* business process improvement
* standardization
Question: What are the challenges now?
Evidence: Neural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, such as English-French BIBREF0, BIBREF1 and Chinese-English BIBREF2. However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
The understanding of NMT models has been approached primarily from two complementary perspectives. The first thread of work aims to understand the importance of representations by analyzing the linguistic information embedded in representation vectors BIBREF3, BIBREF4 or hidden units BIBREF5, BIBREF6. Another direction focuses on understanding the importance of input words by interpreting the input-output behavior of NMT models. Previous work BIBREF7 treats NMT models as black-boxes and provides explanations that closely resemble the attention scores in NMT models. However, recent studies reveal that attention does not provide meaningful explanations since the relationship between attention scores and model output is unclear BIBREF8.
In this paper, we focus on the second thread and try to open the black-box by exploiting the gradients in NMT generation, which aims to estimate the word importance better. Specifically, we employ the integrated gradients method BIBREF9 to attribute the output to the input words with the integration of first-order derivatives. We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
We analyze the linguistic behaviors of words with the importance and show its potential to improve NMT models. First, we leverage the word importance to identify input words that are under-translated by NMT models. Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods. Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language. For example, nouns are more important for Chinese$\Rightarrow $English translation, while prepositions are more important for English-French and -Japanese translation. This finding can inspire better design principles of NMT architectures for different language pairs. For instance, a better architecture for a given language pair should consider its own language characteristics.
However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
Answer: So far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
Question: Given this paragraph about autonomous buildings, why would they be safer during a military attack?
Evidence: Advocates of autonomous building describe advantages that include reduced environmental impacts, increased security, and lower costs of ownership. Some cited advantages satisfy tenets of green building, not independence per se (see below). Off-grid buildings often rely very little on civil services and are therefore safer and more comfortable during civil disaster or military attacks. For example, Off-grid buildings would not lose power or water if public supplies were compromised.
Answer: Autonomous buildings often rely on less civil services and are therefore safer and more comfortable during civil disasters or military attacks.
Question: What is Ethiopia known for from agriculture perspective?
Answer: Ethiopia is well known for its exports of coffee, sesame seeds, gold, cut flowers, and zinc(World bank data). In terms of geography and climate, Ethiopia is slightly less than twice the size of Texas and has a tropical monsoon climate with wide topographic-induced variation (World factbook). The country is located on a high plateau with a central mountain range that is divided by the Great Rift Valley. The typical family size in Ethiopia is five members, and the type of dwelling they live in depends on whether they are in a rural or urban area. In Ethiopia, many families live in traditional thatched-roof houses made of mud or wattle and daub, while urban dwellings are more likely to be made of brick or concrete. However, they may still be relatively small and basic, with limited amenities, and many families in urban areas live in overcrowded and informal settlements that lack basic infrastructure and services. (International Labour Organization)
Question: Identify which instrument is string or woodwind: Gehu, Venu
Answer: Venu is woodwind, Gehu is string.
Question: What is quantum mechanics?
Answer: In a more general sense, the word ​“quantum” can refer to the smallest possible amount of something. Quantum mechanics  is a fundamental theory in physics that describes the behavior of particles such as atoms, electrons, photons and almost everything in the molecular and sub-molecular realm.  Quantum mechanics is the best tool we have to understand how the universe works on its smallest scales. Heisenberg's Uncertainty Principle is one of the most celebrated results of quantum mechanics and states that one (often, but not always) cannot know all things about a particle (as it is defined by it's wave function) at the same time.
Question: What's the function of the monolingual sequence-to-sequence model (DocRepair)?
Evidence: Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences.
Answer: To correct sentence-level translations of adjacent sentences.
Question: What is the difference between open-toed and close-toed shoes?
Answer: Open-toed shoes refer to footwear that exposes the toes. They can reveal just the toes (think of a show with the toe portion cut out) or the entire foot (e.g., sandals). Closed toe shoes are any shoes that cover the toes.
Question: Which team won the 2023 NCAA Men's National Championship?
Evidence: The 2023 NCAA Division I men's basketball tournament involved 68 teams playing in a single-elimination tournament that determined the National Collegiate Athletic Association (NCAA) Division I men's basketball national champion for the 2022–23 season. The 84th annual edition of the tournament began on March 14, 2023, and concluded with the UConn Huskies defeating the San Diego State Aztecs, 76–59 in the championship game on April 3 at NRG Stadium in Houston, Texas.
Answer: The University of Connecticut Huskies
Question: What breed is most likely to win a dog show
Evidence: Through the 134th Westminster Show (February 2010), Best in Show has been won by the Terrier group 45 out of the 103 times that the prize has been awarded since 1907, more than twice as many wins as any other group. The single breed that has won the most is the Wire Fox Terrier, which has won 15 times. Two of the most popular dog breeds in the United States—the Labrador Retriever and Golden Retriever—have never won Best in Show. The dogs are marked for Best in Breed and then proceed to compete in Best in Group, and finally in the grand prize competition of Best in Show
Answer: The Wire Fox Terrier is to date, the winningest breed at the Westminster Show with 15 total wins. Terriers in general have won over 40% of the Best in Show competitions across the long history of the show.
Question: What Python library is used by listeners?
Evidence: In this section, we outline the technical details of ParityBot. The system consists of: 1) a Twitter listener that collects and classifies tweets directed at a known list of women candidates, and 2) a responder that sends out positivitweets when hateful tweets are detected.
We collect tweets from Twitter's real-time streaming API. The stream listener uses the open-source Python library Tweepy BIBREF8. The listener analyses tweets in real-time by firing an asynchronous tweet analysis and storage function for each English tweet mentioning one or more candidate usernames of interest. We limit the streaming to English as our text analysis models are trained on English language corpora. We do not track or store retweets to avoid biasing the analysis by counting the same content multiple times. Twitter data is collected and used in accordance with the acceptable terms of use BIBREF9.
The tweet analysis and storage function acts as follows: 1) parsing the tweet information to clean and extract the tweet text, 2) scoring the tweet using multiple text analysis models, and 3) storing the data in a database table. We clean tweet text with a variety of rules to ensure that the tweets are cleaned consistent with the expectations of the analysis models (see Appdx SECREF9).
The text analysis models classify a tweet by using, as features, the outputs from Perspective API from Jigsaw BIBREF10, HateSonar BIBREF11, and VADER sentiment models BIBREF12. Perspective API uses machine learning models to score the perceived impact a tweet might have BIBREF10. The outputs from these models (i.e. 17 from Perspective, 3 from HateSonar, and 4 from VADER) are combined into a single feature vector for each tweet (see Appdx SECREF10). No user features are included in the tweet analysis models. While these features may improve classification accuracy they can also lead to potential bias BIBREF13.
We measure the relative correlation of each feature with the hateful or not hateful labels. We found that Perspective API's TOXICITY probability was the most consistently predictive feature for classifying hateful tweets. Fig. FIGREF5 shows the relative frequencies of hateful and non-hateful tweets over TOXICITY scores. During both elections, we opted to use a single Perspective API feature to trigger sending positivitweets. Using the single TOXICITY feature is almost as predictive as using all features and a more complex model SECREF14. It was also simpler to implement and process tweets at scale. The TOXICITY feature is the only output from the Perspective API with transparent evaluation details summarized in a Model Card BIBREF14, BIBREF15.
We collect tweets from Twitter’s real-time streaming API. The stream listener uses the open-source Python library Tweepy [22].
Answer: Tweepy.
Question: How/where are the natural question generated?
Evidence: Our method is based on a sequence to sequence network BIBREF38 , BIBREF12 , BIBREF39 . The sequence to sequence network has a text sequence as input and output. In our method, we take an image as input and generate a natural question as output. The architecture for our model is shown in Figure FIGREF4 . Our model contains three main modules, (a) Representation Module that extracts multimodal features (b) Mixture Module that fuses the multimodal representation and (c) Decoder that generates question using an LSTM-based language model.
In our method, we take an image as input and generate a natural question as output. The architecture for our model is shown in Figure FIGREF4 . Our model contains three main modules, (a) Representation Module that extracts multimodal features (b) Mixture Module that fuses the multimodal representation and (c) Decoder that generates question using an LSTM-based language model.
Answer: The answers are shown as follows:
* Decoder that generates question using an LSTM-based language model
Question: What is one of your favorite hobbies?
Answer: I'm an avid reader, especially of audiobooks. I read or listen to more than a book a week, and will dabble in a lot of different genres. I've recently been reading a lot of sci-fi and fantasy, but also enjoy a good biography, military history, sociological, or general work of fiction as long as they are well written.
Question: What colours are on the flag of the Netherlands?
Answer: The flag of the Netherlands is predominantly blue with a white Dutch lion on a golden background.
Question: Can I learn to ski as an adult?
Answer: Yes. Alpine downhill skiing is a sport that can be learned at any age. While many learn to ski when they're young, there are lots of people that only started to ski at an older age. With the right motivation and a willingness to put time and effort, almost anyone can learn to ski. Also, advances to ski equipment and slope grooming technology make skiing a lot safer than it used to be years ago.

Learning to ski as an adult may take longer than learning as a child. It is recommended that you start with some lessons first to gain the right technique. This could be group lessons or private lessons. You could also learn from friends who know how to ski, or learn by watching online tutorials and applying them yourself on the mountain. Once you have the basic skills, you should be able to ski easy slopes and start enjoying skiing as a fun activity. 

In addition to lessons, you will also need the right gear to ski. This includes appropriate clothing that can keep you warm on the mountain while you ski, as well as ski equipment that you will use. It is recommended that you get a pair of skis and poles, good fitting boots, a helmet for safety, and goggles to protect your eyes from the elements. Renting your equipment is a good option when you're starting. That way you are not making an expensive commitment upfront. You could also buy your equipment either new or from any of the second-hand ski shops and ski swaps.
 
With the right gear and knowledge of the basics, you can take your learning in any direction you want. Many people choose to stay at a beginner or intermediate level and just enjoy the occasional ski trip with friends. While others take their passion forward with more practice, hours on the mountain, and likely more lessons to get to advanced levels.

Whichever path you pick, skiing can be a fun sport to learn and enjoy for the rest of your life. Have fun skiing. You got this!
Question: What function do they use for the projection?
Evidence: For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice. However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive. To address this issue, several recent papers propose specially designed invertible networks that are easily trainable yet still powerful BIBREF16 , BIBREF17 , BIBREF19 . Inspired by these works, we use the invertible transformation proposed by BIBREF16 , which consists of a series of “coupling layers”. This architecture is specially designed to guarantee a unit Jacobian determinant (and thus the invertibility property).
From Eq. ( EQREF22 ) we know that only INLINEFORM0 is required for accomplishing learning and inference; we never need to explicitly construct INLINEFORM1 . Thus, we directly define the architecture of INLINEFORM2 . As shown in Figure FIGREF24 , the nonlinear transformation from the observed embedding INLINEFORM3 to INLINEFORM4 represents the first coupling layer. The input in this layer is partitioned into left and right halves of dimensions, INLINEFORM5 and INLINEFORM6 , respectively. A single coupling layer is defined as: DISPLAYFORM0 
where INLINEFORM0 is the coupling function and can be any nonlinear form. This transformation satisfies INLINEFORM1 , and BIBREF16 show that its Jacobian matrix is triangular with all ones on the main diagonal. Thus the Jacobian determinant is always equal to one (i.e. volume-preserving) and the invertibility condition is naturally satisfied.
To be sufficiently expressive, we compose multiple coupling layers as suggested in BIBREF16 . Specifically, we exchange the role of left and right half vectors at each layer as shown in Figure FIGREF24 . For instance, from INLINEFORM0 to INLINEFORM1 the left subset INLINEFORM2 is unchanged, while from INLINEFORM3 to INLINEFORM4 the right subset INLINEFORM5 remains the same. Also note that composing multiple coupling layers does not change the volume-preserving and invertibility properties. Such a sequence of invertible transformations from the data space INLINEFORM6 to INLINEFORM7 is also called normalizing flow BIBREF20 .
For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice.
Answer: An arbitrary invertible function.
Question: Is Spain a good place to live?
Answer: Spain could be a great place to live depending on what you are looking for. 

Spain has amazing weather, very friendly people, amazing food and a good amount of history to learn about. As a tourist or short term visitor, you could have a blast. 

On the other hand, for a long term stay where you need to earn some income… unemployment is a big problem in Spain for the youth.
Question: What is an enzyme inhibitor?
Answer: An enzyme inhibitor is a molecule that binds to an enzyme's active site, or another site on the enzyme, and blocks the enzyme's catalysis of the reaction. Enzyme inhibitors are generally specific to one enzyme and control that enzyme's activity. They also control essential enzymes such as proteases or nucleases that, if left unchecked, may damage a cell. 

Many poisons produced by animals or plants are enzyme inhibitors, and many drug molecules are enzyme inhibitors that inhibit an aberrant human enzyme or an enzyme critical for the survival of a pathogen. Since anti-pathogen inhibitors generally target only one enzyme, such drugs are highly specific and generally produce few side effects in humans. 

Medicinal enzyme inhibitors often have low dissociation constants, meaning a minute amount of the inhibitor will inhibit the enzyme. The discovery of enzyme inhibitors and their refinement are researched in biochemistry and pharmacology.
Question: Based on the passage provided in which years the movie named 'A star is Born' was released?
Evidence: A Star Is Born (1937 film), starring Janet Gaynor and Fredric March, directed by William A. Wellman
A Star Is Born (1954 film), starring Judy Garland and James Mason, directed by George Cukor
A Star Is Born (1976 film), starring Barbra Streisand and Kris Kristofferson, directed by Frank Pierson
A Star Is Born (2018 film), starring Lady Gaga and Bradley Cooper, directed by Bradley Cooper
Answer: 1937, 1954, 1976 and 2018.
Question: Identify which instrument is string or percussion: Tamborim, Veena
Answer: Veena is string, Tamborim is percussion.