DataHammer/scimrc huggingface dataset:
Question: Is the resource that the test suite uses freely available?
Evidence: Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis).
Answer: Yes, it is.
Question: How should I learn guitar?
Answer: Guitar is learned best with a teacher and through many hours of practice. In addition, there are many helpful online resources available to learn guitar.
Question: Create a LinkedIn post for sharing the news of an achievement in company.
Answer: "Hello everyone, 

I am excited to share some great news! Our company has been chosen as a finalist for the esteemed XYZ award. I am so proud of our team's hard work and dedication that has led to this recognition. This wouldn't have been possible without the efforts of each and every one of us. Thank you for your continuous support and contributions towards our success. Let's keep up the momentum! #XYZaward #teamwork #hardworkpaysoff"
Question: What exactly was the action of 12 December 1782
Evidence: The action of 12 December 1782 was a naval engagement fought off the coast of Spain near Ferrol, in which the British 40-gun fifth rate HMS Mediator successfully attacked a convoy of five armed ships. Mediator succeeded in capturing one American privateer, the Alexander, and then captured the French ex-ship of the line La Ménagère. The convoy was part of Pierre Beaumarchais's supply chain to the American colonists.
Answer: The action of 12 December 1782 was a naval engagement fought off the coast of Spain near Ferrol, in which the British 40-gun fifth rate HMS Mediator successfully attacked a convoy of five armed ships. 

Mediator succeeded in capturing one American privateer, the Alexander, and then captured the French ex-ship of the line La Menagere. The convoy was part of Pierre Beaumarchais's supply chain to the American colonists.
Question: Give me a title of one of the "Harry Potter" novels.
Answer: Harry Potter and the Half-Blood Prince
Question: What baselines is the proposed model compared against?
Evidence: Experimental Studies ::: Comparison with State-of-the-art Methods
Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.
Experimental Studies ::: Comparison with State-of-the-art Methods
Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large.
Answer: The answers are shown as follows:
* BERT-Base
* QANet
Question: What is the Cleverbot in this paper?
Evidence: In this section we discuss the state of the art on conversational systems in three perspectives: types of interactions, types of architecture, and types of context reasoning. Then we present a table that consolidates and compares all of them.
ELIZA BIBREF11 was one of the first softwares created to understand natural language processing. Joseph Weizenbaum created it at the MIT in 1966 and it is well known for acting like a psychotherapist and it had only to reflect back onto patient's statements. ELIZA was created to tackle five "fundamental technical problems": the identification of critical words, the discovery of a minimal context, the choice of appropriate transformations, the generation of appropriate responses to the transformation or in the absence of critical words, and the provision of an ending capacity for ELIZA scripts.
Right after ELIZA came PARRY, developed by Kenneth Colby, who is psychiatrist at Stanford University in the early 1970s. The program was written using the MLISP language (meta-lisp) on the WAITS operating system running on a DEC PDP-10 and the code is non-portable. Parts of it were written in PDP-10 assembly code and others in MLISP. There may be other parts that require other language translators. PARRY was the first system to pass the Turing test - the psychiatrists were able to make the correct identification only 48 percent of the time, which is the same as a random guessing.
A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) BIBREF12 appeared in 1995 but current version utilizes AIML, an XML language designed for creating stimulus-response chat robots BIBREF13 . A.L.I.C.E. bot has, at present, more than 40,000 categories of knowledge, whereas the original ELIZA had only about 200. The program is unable to pass the Turing test, as even the casual user will often expose its mechanistic aspects in short conversations.
Cleverbot (1997-2014) is a chatbot developed by the British AI scientist Rollo Carpenter. It passed the 2011 Turing Test at the Technique Techno-Management Festival held by the Indian Institute of Technology Guwahati. Volunteers participate in four-minute typed conversations with either Cleverbot or humans, with Cleverbot voted 59.3 per cent human, while the humans themselves were rated just 63.3 per cent human BIBREF14 .
Cleverbot (1997-2014) is a chatbot developed by the British AI scientist Rollo Carpenter. It passed the 2011 Turing Test at the Technique Techno-Management Festival held by the Indian Institute of Technology Guwahati.
Answer: Cleverbot (1997-2014) is a chatbot developed by the British AI scientist Rollo Carpenter. It passed the 2011 Turing Test at the Technique Techno-Management Festival held by the Indian Institute of Technology Guwahati.
Question: Has GluonCV/NLP provided models for common computer vision or natural language processing tasks?
Evidence: Building upon those modular APIs, GluonCV/NLP provide pre-trained state-of-the-art models, training scripts, and training logs via the model zoo to enable fast prototyping and promote reproducible research. As of the time of writing, GluonCV/NLP have provided over 100 models for common computer vision and natural language processing tasks, such as image classification, object detection, semantic segmentation, instance segmentation, pose estimation, word embedding, language model, machine translation, sentiment analysis, natural language inference, dependency parsing, and question answering.
As of the time of writing, GluonCV/NLP have provided over 200 models for common computer vision and natural language processing tasks, such as image classification, object detection, semantic segmentation, instance segmentation, pose estimation, video action recognition, word embedding, language model, machine translation, sentiment analysis, natural language inference, dependency parsing, and question answering.
Answer: Yes, over 200 models, such as image classification, object detection, and semantic segmentation.
Question: What is the Krusty Krab?
Evidence: The Krusty Krab is a fictional fast food restaurant in the American animated television series SpongeBob SquarePants. It is famous for its signature burger, the Krabby Patty, the formula to which is a closely guarded trade secret.
Answer: The restaurant was founded by Eugene H. Krabs (often referred to as Mr. Krabs), who is also the owner and manager. SpongeBob SquarePants (who works as the fry cook) and Squidward Tentacles (the cashier) are the only full-time employees. The primary competitor to the Krusty Krab is the Chum Bucket across the street, which is operated by Plankton, a plankton and Karen, a waterproof supercomputer and Plankton's wife.
Question: What is Maven?
Answer: Maven is a build automation tool used primarily for Java projects.
Question: Is the model able to predict dogmatism in comments？
Evidence: We now show how we can use the linguistic feature sets we have described to build a classifier that predicts dogmatism in comments. A predictive model further validates our feature sets, and also allows us to analyze dogmatism in millions of other Reddit comments in a scalable way, with multiple uses in ongoing, downstream analyses.
Prediction task. Our goal is (1) to understand how well we can use the strategies in Section 3 to predict dogmatism, and (2) to test the domain-independence of these strategies. First, we test the performance of our model under cross-validation within the Reddit comment dataset. We then evaluate the Reddit-based model on a held out corpus of New York Times comments annotated using the technique in Section 2. We did not refer to this second dataset during feature construction.
For classification, we consider two classes of comments: dogmatic and non-dogmatic. As in the prior analysis, we draw these comments from the top and bottom quartiles of the dogmatism distribution. This means the classes are balanced, with 2,500 total comments in the Reddit training data and 500 total comments in the New York Times testing data.
We compare the predictions of logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features. BOW and SENT provide baselines for the task. We compute BOW features using term frequency-inverse document frequency (TF-IDF) and category-based features by normalizing counts for each category by the number of words in each document. The BOW classifiers are trained with regularization (L2 penalties of 1.5).
Classification results. We present classification accuracy in Table 2 . BOW shows an AUC of 0.853 within Reddit and 0.776 on the held out New York Times comments. The linguistic features boost classification results within Reddit (0.881) and on the held out New York Times comments (0.791). While linguistic signals by themselves provide strong predictive power (0.801 AUC within domain), sentiment signals are much less predictive.
These results suggest that linguistic features inspired by prior efforts in psychology are useful for predicting dogmatism in practice and generalize across new domains.
We now show how we can use the linguistic feature sets we have described to build a classifier that predicts dogmatism in comments.
Answer: Yes.
Question: Tell me some of the rivers that flow through Europe, in alphabetical order.
Answer: In alphabetical order, some of the major European rivers are: Danube River, Elbe River, Loire River, Oder River, Rhine River, River Shannon and the Tagus River.
Question: Give me a list of Premier League winners and runners-up in the last 5 years.
Answer: 2021-2022: Winner Manchester City, Runner-up Liverpool
2020-2021: Winner Manchester City, Runner-up Manchester United
2019-2020: Liverpool, Runner-up Manchester City
2018-2019: Manchester City, Runner-up Liverpool
2017-2018: Manchester City, Runner-up Manchester United
Question: Who is the all time leading goal scorer for Manchester United?
Answer: The all time leading goal scorer for Manchester United is Wayne Rooney.
Question: Which NFL teams have gone to the Super Bowl only once and won?
Answer: New Orleans Saints and New York Jets
Question: What is the most popular breed of dog to have as a pet in the United States?
Answer: Labrador Retriever
Question: Steps to apply LLM & Other Law Programs
Answer: Create an account
Arrange to have your official academic records sent to LSAC
Request letters of recommendation
Register for an English proficiency exam (if applicable)
Apply to law schools electronically
Purchase CAS Reports through your account
Monitor your file status through your account
Question: Why do people foster dogs?
Answer: Fostering animals of any kind can be a great way to reduce the stress and suffering of an animal that has not adjusted to life in a shelter. Fostering can also reduce the number of animals that a shelter is forced to put down, which is a tremendous positive impact to have on a community.
Question: Can the EGL identify aspects of an instance that uncertainty-based measurements cannot capture?
Evidence: We empirically validate EGL on speech recognition tasks. In our experiments, the RNN takes in spectrograms of utterances, passing them through two 2D-convolutional layers, followed by seven bi-directional recurrent layers and a fully-connected layer with softmax activation. All recurrent layers are batch normalized. At each timestep, the softmax activations give a probability distribution over the characters. CTC loss BIBREF8 is then computed from the timestep-wise probabilities.
A base model, INLINEFORM0 , is trained on 190 hours ( INLINEFORM1 100K instances) of transcribed speech data. Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset. We query labels for the selected subset and incorporate them into training. Learning rates are tuned on a small validation set of 2048 instances. The trained model is then tested on a 156-hour ( INLINEFORM3 100K instances) test set and we report CTC loss, Character Error Rate (CER) and Word Error Rate (WER).
The confidence score methods BIBREF1 , BIBREF2 can be easily extended to our setup. Specifically, from the probabilities over the characters, we can compute an entropy per timestep and then average them. This method is denoted as entropy. We could also take the most likely prediction and calculate its CTC loss, normalized by number of timesteps. This method is denoted as pCTC (predicted CTC) in the following sections.
We implement EGL by marginalizing over the most likely 100 labels, and compare it with: 1) a random selection baseline, 2) entropy, and 3) pCTC. Using the same base model, each method queries a variable percentage of the unlabeled dataset. The queries are then included into training set, and the model continues training until convergence. Fig. FIGREF9 reports the metrics (Exact values are reported in Table TABREF12 in the Appendix) on the test set as the query percentage varies. All the active learning methods outperform the random baseline. Moreover, EGL shows a steeper, more rapid reduction in error than all other approaches. Specifically, when querying 20% of the unlabeled dataset, EGL has 11.58% lower CER and 11.09% lower WER relative to random. The performance of EGL at querying 20% is on par with random at 40%, suggesting that using EGL can lead to an approximate 50% decrease in data labeling.
This suggests EGL is able to identify aspects of an instance that uncertainty-based measurements cannot capture.
Answer: Yes, it can.
Question: Are there any eco-friendly alternatives to cremation?
Answer: Water cremation, also know as Aquamation or alkaline hydrolysis,  is an eco-friendly alternative to cremation.  It involves a gentle process where the body of the deceased is reduced to ashes using water.  Aquamation preserves over 90% of energy as opposed to flame-based cremation.  Most people find this new technique to be more comforting as water is often associated with grown and new life.  Many pet owners are choosing this method for their beloved companions as another benefit of Aquamation is that owners are able to retain more of their pets ashes due to the omission of flames as way to reduce bone into ash.
Question: What is used extensively as a proxy for the informativeness of training samples?
Evidence: Intuitively, an instance can be considered informative if it results in large changes in model parameters. A natural measure of the change is gradient length, INLINEFORM0 . Motivated by this intuition, Expected Gradient Length (EGL) BIBREF3 picks the instances expected to have the largest gradient length. Since labels are unknown on INLINEFORM1 , EGL computes the expectation of the gradient norm over all possible labelings. BIBREF3 interprets EGL as “expected model change”. In the following section, we formalize the intuition for EGL and show that it follows naturally from reducing the variance of an estimator.
Confidence scoring has been used extensively as a proxy for the informativeness of training samples.
Answer: Confidence scoring has been used extensively as a proxy for the informativeness of training samples.
Question: What of the following component part of either Data Engineering or  Machine learning? Delta Live Table (DLT), MLFlow, Autoloader, AutoML
Answer: Delta Live Table (DLT) is used to Data Engineering, MLFlow is used for Machine Learning, Autoloader is used to Data Engineering, AutoML is used for Machine Learning
Question: Who are the main cast of It's Always Sunny in Philadelphia?
Answer: The main cast of the TV show It's Always Sunny in Philadelphia are Charlie Day, Rob McElhenney, Glenn Howerton, Kaitlin Olson, Danny DeVito, and Mary Elizabeth Ellis.
Question: What does a geologist do?
Answer: A geologist studies rocks that come from the earth.  They evaluate the composition of rocks in order to determine how long ago layers in rock formations were formed.  They are able to give us a clear objective history of how the earth was formed and how it has adapted over time.
Question: What's the difference between splitboarding and snowboarding?
Answer: Splitboarding is a more specialized form of snowboarding that also enables uphill and lateral travel, in addition to conventional downhill (alpine) travel. Snowboarders will typically ride within a developed ski resort using powered chairlifts, while a splitboarder typically ventures into undeveloped areas backcountry areas.

From an equipment perspective, a splitboard looks like a conventional snowboard for downhill travel, but for uphill travel it splits into two ski-like surfaces that, with grippy climbing skins attached on the base that contacts the snow, enables people to step and glide uphill on snow.

In addition to conventional downhill snowboarding skills, splitboarding requires additional skills in uphill technique, safely navigating the backcountry (including avalanche risk).
Question: Label each of the following as either a movie or a documentary: RRR, John Wick, Man on Wire, Creed, Our Planet, Tales by Light, Luther, Becoming.
Answer: RRR: Movie

John Wick: Movie

Man on Wire: Documentary

Creed: Movie

Our Planet: Documentary

Tales by Light: Documentary

Luther: Movie

Becoming: Documentary
Question: Which is bigger, a beagle or a basset?
Answer: Beagles and Basset Hounds are both breeds of dogs. There are two varieties of Beagles - one averaging 13 inches tall, and the other averaging 15 inches tall. The average height for Basset Hounds is also 15 inches tall. However, when comparing weight, Basset Hounds tend to be larger than Beagles. The average weight for Basset hounds is 45-60 pounds, while beagles generally weigh in at 20-30 pounds.
Question: Which elements of the platform are modular?
Evidence: In this paper we provide a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform copes with the three aforementioned challenges in several ways. Firstly, it provides a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way. Secondly, the proposed platform offers an approach that supports sentiment analysis based on VADER which can respond to large amounts of natural language processing queries in real time. Thirdly, the platform supports a predictive approach based on online learning in which a machine learning model adapts its weights to cope with new prices and sentiments. Finally, the platform is modular and integrative in the sense that it combines these different solutions to provide novel real-time tool support for bitcoin price prediction that is more scalable, data-rich, and proactive, and can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
n this paper we provide a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform copes with the three aforementioned challenges in several ways. Firstly, it provides a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way. Secondly, the proposed platform offers an approach that supports sentiment analysis based on VADER which can respond to large amounts of natural language processing queries in real time. Thirdly, the platform supports a predictive approach based on online learning in which a machine learning model adapts its weights to cope with new prices and sentiments. Finally, the platform is modular and integrative in the sense that it combines these different solutions to provide novel real-time tool support for bitcoin price prediction that is more scalable, data-rich, and proactive, and can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
Answer: handling large volume incoming data, sentiment analysis on tweets and predictive online learning