DataHammer/scimrc huggingface dataset:
Question: Summarize why critics liked Kanye West’s album My Beautiful Dark Twisted Fantasy.
Evidence: Rolling Stone chief critic Rob Sheffield called My Beautiful Dark Twisted Fantasy West's best and most wildly inspired album upon its release, asserting that no other act was recording music as dark or uncanny; he added the rapper transgresses the very conventions he had established for rap and pop music in the past five years. Steve Jones of USA Today echoed this sentiment, declaring that West "unleashes an array of sonic flavors — old school hip-hop, progressive rock, R&B, classical music — and deftly mixes and matches them", concluding his only predictability is a consistent drive to make every project his best. The Village Voice's Sean Fennessey found the album skillfully engineered and sequenced because of the way each song transitions over "like some long night out into the hazy morning after". Esteemed reviewer Robert Christgau, in MSN Music, hailed the album as a "world-beating return to form" for West. Pitchfork awarded the album a 10/10, the first perfect score the publication had given to a new release since Wilco's Yankee Hotel Foxtrot in 2002.
Answer: Several top music critical outlets including Rolling Stone, USA Today, The Village Voice, MSN music, and Pitchfork praised Kanye’s album, My Beautiful Dark Twisted Fantasy.  One critic praised Kanye’s willingness to diverge from past albums as well as trends in rap music that Kanye himself had helped establish.  Another praised Kanye’s “array of sonic flavors” with elements across multiple genres skillfully mixed and matched.  Notably, Pitchfork awarded the album a perfect 10/10 rating, its first for a new release since 2002.
Question: Using the provided passage, provide a bulleted list of the areas most effected by climate change.
Evidence: Economic damages due to climate change may be severe and there is a chance of disastrous consequences. Climate change has likely already increased global economic inequality, and this trend is projected to continue. Most of the severe impacts are expected in sub-Saharan Africa, where most of the local inhabitants are dependent upon natural and agricultural resources and South-East Asia. The World Bank estimates that climate change could drive over 120 million people into poverty by 2030.

Current inequalities based on wealth and social status have worsened due to climate change. Major difficulties in mitigating, adapting, and recovering to climate shocks are faced by marginalized people who have less control over resources. Indigenous people, who are subsistent on their land and ecosystems, will face endangerment to their wellness and lifestyles due to climate change. An expert elicitation concluded that the role of climate change in armed conflict has been small compared to factors such as socio-economic inequality and state capabilities.

Low-lying islands and coastal communities are threatened by sea level rise, which makes flooding more common. Sometimes, land is permanently lost to the sea. This could lead to statelessness for people in island nations, such as the Maldives and Tuvalu. In some regions, the rise in temperature and humidity may be too severe for humans to adapt to. With worst-case climate change, models project that almost one-third of humanity might live in extremely hot and uninhabitable climates, similar to the current climate found in the Sahara. These factors can drive environmental migration, both within and between countries. More people are expected to be displaced because of sea level rise, extreme weather and conflict from increased competition over natural resources. Climate change may also increase vulnerability, leading to "trapped populations" who are not able to move due to a lack of resources.
Answer: - sub-Saharan Africa
- South-East Asia
- Low-lying islands and coastal communities
- Maldives
- Tuvalu
Question: What is the origins of mens lacrosse?
Answer: Lacrosse was started by the Native American Indians and was originally known as stickball. The game was initially played in the St. Lawrence Valley area by the Algonquian tribe and they were followed by other tribes in the eastern half of North America, and around the western Great Lakes.
What began as stickball, a native American Indian contest played by tribal warriors for training, recreation and religious reasons, has developed over the years into the interscholastic, professional and international sport of lacrosse
Question: Does MNMT usually perform worse than the pivot-based method in the zero-shot translation setting?
Evidence: In recent years, zero-shot translation in NMT has attracted widespread attention in academic research. Existing methods are mainly divided into four categories: pivot-based method, transfer learning, multilingual NMT, and unsupervised NMT.
Pivot-based Method is a common strategy to obtain a source$\rightarrow $target model by introducing a pivot language. This approach is further divided into pivoting and pivot-synthetic. While the former firstly translates a source language into the pivot language which is later translated to the target language BIBREF4, BIBREF5, BIBREF12, the latter trains a source$\rightarrow $target model with pseudo data generated from source-pivot or pivot-target parallel data BIBREF13, BIBREF14. Although the pivot-based methods can achieve not bad performance, it always falls into a computation-expensive and parameter-vast dilemma of quadratic growth in the number of source languages, and suffers from the error propagation problem BIBREF15.
Transfer Learning is firstly introduced for NMT by BIBREF6, which leverages a high-resource parent model to initialize the low-resource child model. On this basis, BIBREF7 and BIBREF8 use shared vocabularies for source/target language to improve transfer learning, while BIBREF16 relieve the vocabulary mismatch by mainly using cross-lingual word embedding. Although these methods are successful in the low-resource scene, they have limited effects in zero-shot translation.
Multilingual NMT (MNMT) enables training a single model that supports translation from multiple source languages into multiple target languages, even those unseen language pairs BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21. Aside from simpler deployment, MNMT benefits from transfer learning where low-resource language pairs are trained together with high-resource ones. However, BIBREF22 point out that MNMT for zero-shot translation easily fails, and is sensitive to the hyper-parameter setting. Also, MNMT usually performs worse than the pivot-based method in zero-shot translation setting BIBREF23.
Unsupervised NMT (UNMT) considers a harder setting, in which only large-scale monolingual corpora are available for training. Recently, many methods have been proposed to improve the performance of UNMT, including using denoising auto-encoder, statistic machine translation (SMT) and unsupervised pre-training BIBREF24, BIBREF25, BIBREF26, BIBREF11. Since UNMT performs well between similar languages (e.g., English-German translation), its performance between distant languages is still far from expectation.
Our proposed method belongs to the transfer learning, but it is different from traditional transfer methods which train a parent model as starting point. Before training a parent model, our approach fully leverages cross-lingual pre-training methods to make all source languages share the same feature space and thus enables a smooth transition for zero-shot translation.
Also, MNMT usually performs worse than the pivot-based method in zero-shot translation setting (Arivazhagan et al. 2018).
Answer: Yes.
Question: Extract what units are strong against countering cavalry from the following text
Evidence: The Age of Kings also includes five types of military units: infantry, archers, cavalry, siege weapons, and naval units. Certain types of infantry, archers, and cavalry are "counter units" with special defenses against other types of units. The three human classes of military generally follow a rock-paper-scissors model. For example, infantry are generally powerful against buildings but weak against cavalry, thus the infantry counter units—spearmen and pikemen—have attack bonuses against cavalry.
Answer: Spearman and Pikemen are effective counters to Cavalry
Question: How to eliminate spurious training data entries?
Evidence: Question answering on tabular data is an important problem in natural language processing. Recently, a number of systems have been proposed for solving the problem using the WikiTableQuestions dataset BIBREF1 (henceforth called WTQ). This dataset consists of triples of the form INLINEFORM0 question, table, answer INLINEFORM1 where the tables are scraped from Wikipedia and questions and answers are gathered via crowdsourcing. The dataset is quite challenging, with the current best model BIBREF0 (henceforth called KDG) achieving a single model accuracy of only 43.3% . This is nonetheless a significant improvement compared to the 34.8% accuracy achieved by the previous best single model BIBREF2 .
We sought to analyze the source of the improvement achieved by the KDG model. The KDG paper claims that the improvement stems from certain aspects of the model architecture.
In this paper, we find that a large part of the improvement also stems from a certain pruning of the data used to train the model. The KDG system generates its training data using an algorithm proposed by BIBREF3 . This algorithm applies a pruning step (discussed in Section SECREF3 ) to eliminate spurious training data entries. We find that without this pruning of the training data, accuracy of the KDG model drops to 36.3%. We consider this an important finding as the pruning step not only accounts for a large fraction of the improvement in the state-of-the-art KDG model but may also be relevant to training other models. In what follows, we briefly discuss the pruning algorithm, how we identified its importance for the KDG model, and its relevance to further work.
This algorithm applies a pruning step (discussed in Section SECREF3 ) to eliminate spurious training data entries.
Answer: The algorithm applies a pruning step to eliminate spurious training data entries.
Question: Is it easy for American technology companies to open an office in Dubai?
Evidence: Dubai has also developed Internet and Media free zones, offering 100% foreign ownership, no tax office space for the world's leading ICT and media companies, with the latest communications infrastructure to service them. Many of the world's leading companies have now set up branch offices, and even changed headquarters to there.
Answer: Yes, the United Arab Emirates - especially the Emirate of Dubai - is investing heavily in diversifying their economy away from their previous reliance on Petroleum and natural gas exports and revenue. This has resulted in investments to grow tourism, creating a regulatory environment to attract financial services into Dubai and developing free zones to attract technology and media companies to invest in opening offices and hiring people in the country. These free zones enable foreign technology companies to own 100% of the local subsidiary, and benefit from tax free office space supported by latest communications infrastructure to create regional headquarters supporting their broader Middle East and Africa customers and operations.
Question: How much is decoding speed increased by increasing encoder and decreasing decoder depth?
Evidence: Table TABREF24 shows that while the acceleration of trading decoder layers for encoding layers in training is small, in decoding is significant. Specifically, the Transformer with 10 encoder layers and 2 decoder layers is $2.32$ times as fast as the 6-layer Transformer while achieving a slightly higher BLEU.
Table TABREF24 shows that while the acceleration of trading decoder layers for encoding layers in training is small, in decoding is significant. Specifically, the Transformer with 10 encoder layers and 2 decoder layers is $2.32$ times as fast as the 6-layer Transformer while achieving a slightly higher BLEU.
Answer: The answers are shown as follows:
* the Transformer with 10 encoder layers and 2 decoder layers is $2.32$ times as fast as the 6-layer Transformer
Question: What are the words of House Baratheon?
Answer: "Ours is the Fury"
Question: Tell me which of these items would be helpful on a camping trip: Golf Club, Tent, Matches, Axe, Soccer Ball, Xbox, Water bottle
Answer: The following items would be helpful on a camping trip: Tent, Matches, Axe, Water bottle.
Question: What are the baseline models in this paper?
Evidence: In the section, we present the performance of two well-established baseline models to automatically infer the phenotype based on the patient note, which we approach as a multi-label, multi-class text classification task BIBREF18. Each of the baseline model is a binary classifier indicating whether a given phenotype is present in the input patient note. As a result, we train a separate model for each phenotype.
Bag of Words + Logistic Regression We convert each patient note into a bag of words, and give as input to a logistic regression. Convolutional Neural Network (CNN) We follow the CNN architecture proposed by Collobert et al. (2011) and Kim (2014). We use the convolution widths from 1 to 4, and for each convolution width we set the number of filters to 100. We use dropout with a probability of 0.5 to reduce overfitting (Srivastava et al., 2014). The trainable parameters were initialized using a uniform distribution from −0.05 to 0.05. The model was optimized with adadelta (Zeiler, 2012). We use word2vec (Mikolov et al., 2013) as the word embeddings, which we pretrain on all the notes of MIMIC III v3.
Answer: The baselines are Bag of Words + Logistic Regression and Convolutional Neural Network (CNN).
Question: Given this paragraph, where is The Walt Disney Company headquarters?
Evidence: The Walt Disney Company, commonly known as Disney (/ˈdɪzni/), is an American multinational, mass media and entertainment conglomerate that is headquartered at the Walt Disney Studios complex in Burbank, California. Disney was founded on October 16, 1923, by brothers Walt and Roy O. Disney as Disney Brothers Studio; it also operated under the names Walt Disney Studio and Walt Disney Productions before changing its name to The Walt Disney Company in 1986. Early in its existence, the company established itself as a leader in the animation industry, with the creation of the widely popular character Mickey Mouse, who first appeared in Steamboat Willie, which used synchronized sound, to become the first post-produced sound cartoon. The character would go on to become the company's mascot.
Answer: According to this text, The Walt Disney Company is headquartered in Burbank, California.
Question: How to realize the use of the same data with an ASR system?
Evidence: The Finnish text data used for the language modeling task is provided by BIBREF13. The dataset consists mainly of newspapers and books of around 144 million word tokens and 4.2 million unique tokens. We use a Morfessor 2.0 BIBREF14 using the basic unsupervised Morfessor Baseline algorithm BIBREF15 with a corpus weight parameter ($\alpha $) of 0.001. We have a vocabulary of 34K subword tokens for the left+right-marked (+m+) markings and 19K subword tokens for the left-marked (+m) markings. We also pre-process the data to remove any punctuation marks such that we can use the same data with an ASR system. The input is one sentence per line and we shuffle the sentences at each epoch. The data is randomly divided into- training dataset and a validation dataset. The test dataset consists of 2850 Finnish news articles obtained from the Finnish national broadcaster YLE.
We also pre-process the data to remove any punctuation marks such that we can use the same data with an ASR system.
Answer: They pre-process the data to remove any punctuation marks.
Question: How many NBA championships does Lebron James have?
Answer: 4 nba championships
Question: Why democracy doesn't works and in old times we had autocracy?
Answer: Corruption, polarization, and misinformation all have the potential to easily influence democracy, which is a complicated system with many moving parts. It additionally will in general be sluggish and wasteful because of the requirement for agreement building and split the difference among various groups.

On the other hand, because there is no need to engage in a prolonged decision-making process or accommodate divergent viewpoints, autocracy can be more effective and decisive in implementing policies. Singapore, where the government has been able to achieve rapid economic growth and development through strong and centralized leadership, is a good case for autocracy. The nation has an exclusive expectation of living, low degrees of wrongdoing, and a well-working public framework.
Question: Why do people enjoy baseball?
Answer: Baseball is known as "America's Pastime" because it started during the industrial revolution as America grew and expanded across the continental United States.  Baseball is relatively easy to begin playing but extremely difficult to master, which allows for broad appeal and interest.  In addition to the sport gaining popularity there was significant growth in major US cities, where teams would play.  This led to the rise of twenty, thirty and even sixty-thousand seat stadiums where people of all ages could enjoy the game outdoors.  Ultimately helping the sport maintain its popularity and growth to where it is today - a global top 4 sport.
Question: Explain the Fibonacci sequence
Answer: The Fibonacci sequence is a sequence of numbers, where each number is the sum of the two numbers before that number. The Fibonacci sequence usually starts with zero followed by one. The next number in the sequence is then the sum of the preceding numbers, one and zero, which is one again. The following number is the sum of one and one: two. According to the same logic, the first ten numbers of the Fibonacci sequence are: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34.
Question: What corpus is collected for NER pre-training?
Evidence: NER (Named Entity Recognition) is the first task in the joint multi-head selection model. It is usually formulated as a sequence labeling problem using the BIO (Beginning, Inside, Outside) encoding scheme. Since there are different entity types, the tags are extended to B-type, I-type and O. Linear-chain CRF BIBREF15 is widely used for sequence labeling in deep models. In our method, CRF is built on the top of BERT. Supposed $y\in {\left\lbrace B-type,I-type,O \right\rbrace }$ is the label, score function $ s(X,i)_{y_{i}} $ is the output of BERT at $ i_{th}$ character and $ b_{y_{i-1}y_{i}} $ is trainable parameters, the probability of a possible label sequence is formalized as:
By solving Eq DISPLAY_FORM11 we can obtain the optimal sequence tags:
Previous works show that introducing extra data for distant supervised learning usually boost the model performance. For this task, we collect a large-scale Baidu Baike corpus (about 6 million sentences) for NER pre-training. As shown in figure FIGREF12, each sample contains the content and its title. These samples are auto-crawled so there is no actual entity label. We consider the title of each sample as a pseudo label and conduct NER pre-training using these data. Experimental results show that it improves performance.
For this task, we collect a large-scale Baidu Baike corpus (about 6 million sentences) for NER pre-training.
Answer: It is a large-scale Baidu Baike corpus (about 6 million sentences).
Question: Does their combination of a non-parametric retrieval and neural network get trained end-to-end?
Evidence: The Transformer baselines are trained on 16 GPUs, with the learning rate, warm-up schedule and batching scheme described in BIBREF6 . The semi-parametric models were trained on 32 GPUs with each replica split over 2 GPUs, one to train the translation model and the other for computing the CSTM. We used a conservative learning rate schedule (3, 40K) BIBREF8 to train the semi-parametric models.
The semi-parametric models were trained on 32 GPUs with each replica split over 2 GPUs, one to train the translation model and the other for computing the CSTM.
Answer: Yes.
Question: Where do they collect the synthetic data?
Evidence: By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section. For automatic translation, we utilize a pre-trained and publicly released NMT model for En $\rightarrow $ De and train another NMT model for En $\rightarrow $ Fr using the WMT'15 En-Fr parallel corpus BIBREF19 . A beam of size 5 is used to generate synthetic sentences. Lastly, to match the size of the training data, PSEUDOmix is established by randomly sampling half of each Fr*-De and Fr-De* corpus and mixing them together.
By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section. For automatic translation, we utilize a pre-trained and publicly released NMT model for En $\rightarrow $ De and train another NMT model for En $\rightarrow $ Fr using the WMT'15 En-Fr parallel corpus BIBREF19 .
Answer: Yes.
Question: Classify each of the following as either a faction from The Divergent Series or luchadore wrestlers: Erudite, Candor, Rey Mysterio, Abnegation, Blue Demon, Bengala, Mistico, Dauntless, Amity, Bam Bam
Answer: The Divergent Series factions: Erudite, Candor, Abnegation, Dauntless, Amity
Luchadores: Rey Mysterio, Blue Demon, Bengala, Mistico, Bam Bam
Question: What characteristics of the blue oak enable it to survive long periods of drought?
Evidence: Quercus douglasii, known as blue oak, is a species of oak endemic to California, common in the Coast Ranges and the foothills of the Sierra Nevada. It is California's most drought-tolerant deciduous oak, and is a dominant species in the blue oak woodland ecosystem. It is occasionally known as mountain oak and iron oak.

Quercus douglasii is the most drought-tolerant of California's deciduous oaks. It has a smaller canopy than less drought-tolerant relatives, and invests proportionally more growth into roots rather than leaves throughout its life cycle. The leathery blue-green leaves contribute to its drought resistance; during drought, the leaf color is more pronounced. Trees can also drop their leaves in summer rather than fall in dry years, but usually continue to develop their acorns through the fall. Drought may cause trees not to flower in spring.

Native Californians commonly gathered Quercus douglasii acorns, which they considered good-tasting, and processed them into acorn flour. They made baskets out of blue oak seedlings, utensils such as bowls from the wood, and dye from the acorns. Commercially, the blue oak is mainly limited to use as firewood. The acorns are eaten by wildlife and livestock. They can be eaten but, if bitter, may need to have the tannins leached.
Answer: The blue oak is a deciduous oak native to California. It tolerates drought better than other oaks due its smaller canopy. This oak invests more in developing its root system rather than leaves. This enables it to tolerate long periods of drought.