DataHammer/scimrc huggingface dataset:
Question: What are the highlights of the Apollo 11 mission?
Evidence: Apollo 11 (July 16–24, 1969) was the American spaceflight that first landed humans on the Moon. Commander Neil Armstrong and lunar module pilot Buzz Aldrin landed the Apollo Lunar Module Eagle on July 20, 1969, at 20:17 UTC, and Armstrong became the first person to step onto the Moon's surface six hours and 39 minutes later, on July 21 at 02:56 UTC. Aldrin joined him 19 minutes later, and they spent about two and a quarter hours together exploring the site they had named Tranquility Base upon landing. Armstrong and Aldrin collected 47.5 pounds (21.5 kg) of lunar material to bring back to Earth as pilot Michael Collins flew the Command Module Columbia in lunar orbit, and were on the Moon's surface for 21 hours, 36 minutes before lifting off to rejoin Columbia.
Answer: Apollo 11 was the culmination of a multi year space race instigated by the Cold War, leading to the United States landing the first humans on the Moon. The mission had 3 personnel, required multiple years of preparation, and consisted of 8 key phases; launch, flight, lunar orbit, lunar descent, landing, lunar surface operations, lunar ascent, return, splashdown, and Quarantine. The Apollo 11 mission remains the most famous mission ever in the NASA program. The 3 astronauts were Michael Collins, Buzz Aldrin, and Neil Armstrong.
Question: Did Elon Musk create Tesla
Evidence: Tesla was incorporated in July 2003 by Martin Eberhard and Marc Tarpenning as Tesla Motors. The company's name is a tribute to inventor and electrical engineer Nikola Tesla. In February 2004, via a $6.5 million investment, Elon Musk became the largest shareholder of the company. He has served as CEO since 2008. According to Musk, the purpose of Tesla is to help expedite the move to sustainable transport and energy, obtained through electric vehicles and solar power. Tesla began production of its first car model, the Roadster sports car, in 2008.
Answer: No, Tesla was incorporate in July 2003 July 2003 by Martin Eberhard and Marc Tarpenning. Elon Musk became the largest shareholder in February 2004 when he invested $6.5 million into the company.
Question: What are all the locations that walnut trees grow, listed in this article.
Evidence: Walnut trees, genus Juglans, are plants which mostly grow in the northern hemisphere. They can reach ten to forty meters (30-130 ft) in height. They are used for timber. The wood is very hard, and dark in color. It is mostly used in furniture.

The fruit is eaten and can be used to make oil. The fruit of Juglans are called walnuts. Material of the walnut's outer casing was used by Native American tribes to poison fish.

Walnut trees grow from Europe eastwards to the Himalayas and southwest China. In America they are found in places from Canada to Argentina.
Answer: Walnut trees grow East-West between Europe and China as well as North-South between Canada to Argentina.
Question: Why is ICE not evaluted on the larger dataset like ImageNet?
Evidence: Three datasets Cifar-10 (Krizhevsky et al., 2009), Cifar-100, and TieredIma_x005f_x005f_x005f_x005f_x0002_geNet (Ren et al., 2018a) are used to build the testing scenes of GTA. Both Cifar-10 and Cifar-100 contain 60,000 images with the resolution of 32×32. TieredImageNet is a subset sampled from ImageNet (Deng et al., 2009).
Answer: Thanks for your advice. We will include the experiment on ImageNet in our final version. In the current version, we have evaluated ICE on TieredImageNet, which is a subset of ImageNet. TieredImageNet contains 608 categories and each category contains about 1300 images. Therefore the experiments on TieredImageNet can partly represent the experiment on ImageNet.
Question: Please provide a short summary on Harshad Mehta scam.
Evidence: Harshad Shantilal Mehta (29 July 1954 — 31 December 2001) was an Indian stockbroker and a convicted fraudster. Mehta's involvement in the 1992 Indian securities scam made him infamous as a market manipulator.
Answer: Of the 27 criminal charges brought against Mehta, he was only convicted of four, before his death (by sudden heart attack) at age 47 in 2001. It was alleged that Mehta engaged in a massive stock manipulation scheme financed by worthless bank receipts, which his firm brokered for "ready forward" transactions between banks. Mehta was convicted by the Bombay High Court and the Supreme Court of India for his part in a financial scandal valued at ₹100 billion (US$1.3 billion) which took place on the Bombay Stock Exchange (BSE). The scandal exposed the loopholes in the Indian banking system and the Bombay Stock Exchange (BSE) transaction system, and consequently the SEBI introduced new rules to cover those loopholes. He was on trial for 9 years, until he died at the end of 2001 from a heart attack.
Question: List the ingredients to make the best Greek salad
Answer: Salad: Mixed greens, tomato, cucumber, red onion, Kalamata olives, lots of feta cheese, marinated artichoke hearts, hearts of palm, hemp seeds, beets, pink salt, fresh ground black pepper, lots of oregano 
Dressing: Olive oil, fresh squeezed lemon juice
Question: Describe a morning routine from a 2 year olds perspective
Answer: I wake up and I'm alone in my bed, it's dark and I feel afraid. I call out for my mommy and feel so relieved when I hear her footsteps to my bedroom. She snuggles in beside me and I feel happy and content. I wake again with sunshine in my eyes and know it's morning time as the sun is up. I rollover and see my mommy is still sleeping beside me. I sit up and tell her it's morning time. My brother comes running in and asks me to play in the playroom. I'm so excited to go play with my toys! I race down the hall and pick out my favorite puzzle, it has all the colors of the rainbow! I ask my brother for help to complete the puzzle and feel proud when it's done. I hear my mommy calling us from downstairs, she is saying breakfast is ready. My tummy feels hungry and I go downstairs and to the kitchen island where she brings my favorite, a big bowl of porridge! As I'm eating, I feel some fall on my chin. I point this out to mommy and she wipes it with a cloth. When I'm finished, mommy says it's time to get dressed. I want to help so I take off my pajamas all by myself!
Question: Which is an imperial or a metric measurement: ounce, gram
Answer: gram is metric, ounce is imperial
Question: What are some of the subjects that a school student might study.
Answer: Students are known to take classes in mathematics, science, art, sport, foreign languages, history and geography.
Question: Name a few popular metal genres
Answer: Thrash Metal, Death Metal, Melodic Death Metal, Power Metal, Crossover, Grindcore, Symphonic Metal, Doom Metal, Death Core
Question: Do they compare results against state-of-the-art language models?
Evidence: All models outperform previously reported results for mlstm BIBREF8 despite lower parameter counts. This is likely due to our relatively small batch size. However, they perform fairly similarly. Encouraged by these results, we built an mgru with both hidden and intermediate state sizes set to that of the original mlstm (700). This version highly surpasses the previous state of the art while still having fewer parameters than previous work.
This version highly surpasses the previous state of the art while still having fewer parameters than previous work.
Answer: Yes.
Question: How did Draymond Green's minutes increase over time?
Evidence: Green was selected with the 35th overall pick in the 2012 NBA draft by the Golden State Warriors. On July 30, 2012, he signed a three-year, $2.6 million contract with the Warriors. In his NBA debut in the Warriors' season opener on October 31 against the Phoenix Suns, Green played one minute, made one defensive rebound, and committed one foul. Green gradually received more playing time in subsequent games, especially in the wake of injuries to Brandon Rush and Richard Jefferson. After getting only marginal floor time at the beginning of the season, by November 22, Green was playing 15–20 minutes per game, and by December 9, as Green gained a bigger role on the team, the Warriors had won 8 of 10 games. On December 12, Green made the winning layup with 0.9 seconds left in the Warriors' 97–95 win over the defending champion Miami Heat.
Answer: In his NBA debut in the Warriors' season opener on October 31, 2012, against the Phoenix Suns, Green played one minute. Green gradually received more playing time in subsequent games, especially in the wake of injuries to Brandon Rush and Richard Jefferson. After getting only marginal floor time at the beginning of the season, by November 22, 2012, Green was playing 15–20 minutes per game.

It is not clear how Green's minutes increased over time beyond November 22, 2012 based on the provided text.
Question: Which characters belong to DC or Marvel Universe? Falcon, Hawkman
Answer: Hawkman is DC, Falcon is Marvel
Question: What does the raw corpus contain?
Evidence: Given the six controversial topics and four different registers, we compiled a collection of plain-text documents, which we call the raw corpus. It contains 694,110 tokens in 5,444 documents. As a coarse-grained analysis of the data, we examined the lengths and the number of paragraphs (see Figure FIGREF43 ). Comments and forum posts follow a similar distribution, being shorter than 300 tokens on average. By contrast, articles and blogs are longer than 400 tokens and have 9.2 paragraphs on average. The process of compiling the raw corpus and its further statistics are described in detail in Appendix UID158 .
Given the six controversial topics and four different registers, we compiled a collection of plain-text documents, which we call the raw corpus. It contains 694,110 tokens in 5,444 documents.
Answer: It contains 694,110 tokens in 5,444 documents.
Question: I have a large ball, what games can I play?
Answer: You can play basketball, soccer, netball or volleyball with a large ball.
Question: Can you write a High Five to a colleague for the partnership at a customer account and the hard work he did?
Answer: Thanks for always being there to help on customer account. I can't imagine how difficult our discussions would have been if we didn't have you.
Question: What are some ways that an enterprise software company can cut costs?
Answer: There are many different types of enterprise software companies, and no one right way to cut costs. Here are a few ideas:

- Reduce Cost of Customer Acquisition. Sales and marketing cost a percentage of revenue is often the largest expense for enterprise software companies. Improving the efficiency of sales people, reducing sales commissions, or reducing supporting staff could all lead to lower cost of customer acquisition.

- Reduce research and development spend. R&D can be a large expense for enterprise software companies. By reducing the number of software engineers, supporting cloud costs, or improving productivity with better tooling and process, you can reduce R&D costs.

- Improve revenue margins by reducing cost of goods sold. COGS will increase with revenue and can be a large expense. Reducing support engineers, improving customer service efficiency, or reducing operational costs - such as cloud costs - can improve revenue margins. 

These are some ideas for how to cut costs, although every business is different and requires different approaches.
Question: Do the BERT-based embeddings improve results?
Evidence: Table TABREF25 contains the best hyper-parameters and Table TABREF26 contains micro-average F1 scores for both datasets on dev and test sets. We only consider positive pairs, i.e. correct predictions on NONE pairs are excluded for evaluation. In general, the baseline model CAEVO is outperformed by both NN models, and NN model with BERT embedding achieves the greatest performance. We now provide more detailed analysis and discussion for each dataset.
In general, the baseline model CAEVO is outperformed by both NN models, and NN model with BERT embedding achieves the greatest performance.
Answer: Yes.
Question: Identify which animal is domesticated or wild: Hedgehog, Sea-monkey
Answer: Hedgehog is domesticated, Sea-monkey is wild.
Question: Which is a species of fish? Sardine or Sardinia
Answer: Sardine
Question: How do they determine similarity between predicted word and topics?
Evidence: To test the proposed methods ability to generate unsupervised words, it was necessary to devise a method of measuring word relevance. Topic modeling was used based on the assumption that words found in the same topic are more relevant to one another then words from different topics BIBREF14. The complete 200k headline dataset BIBREF11 was modeled using a Naïve Bayes Algorithm BIBREF15 to create a word-category co-occurrence model. The top 200 most relevant words were then found for each category and used to create the topic table SECREF12. It was assumed that each category represented its own unique topic.
The number of relevant output words as a function of the headline’s category label were measured, and can be found in figure SECREF4. The results demonstrate that the proposed method could correctly identify new words relevant to the input topic at a signal to noise ratio of 4 to 1.
To test the proposed methods ability to generate unsupervised words, it was necessary to devise a method of measuring word relevance. Topic modeling was used based on the assumption that words found in the same topic are more relevant to one another then words from different topics BIBREF14. The complete 200k headline dataset BIBREF11 was modeled using a Naïve Bayes Algorithm BIBREF15 to create a word-category co-occurrence model. The top 200 most relevant words were then found for each category and used to create the topic table SECREF12. It was assumed that each category represented its own unique topic.

The number of relevant output words as a function of the headline’s category label were measured, and can be found in figure SECREF4. The results demonstrate that the proposed method could correctly identify new words relevant to the input topic at a signal to noise ratio of 4 to 1.
Answer: The answers are shown as follows:
* number of relevant output words as a function of the headline’s category label
Question: Provide a brief summary of how the ISO 27001 standard works
Evidence: Most organizations have a number of information security controls. However, without an information security management system (ISMS), controls tend to be somewhat disorganized and disjointed, having been implemented often as point solutions to specific situations or simply as a matter of convention. Security controls in operation typically address certain aspects of information technology (IT) or data security specifically; leaving non-IT information assets (such as paperwork and proprietary knowledge) less protected on the whole. Moreover, business continuity planning and physical security may be managed quite independently of IT or information security while Human Resources practices may make little reference to the need to define and assign information security roles and responsibilities throughout the organization.

ISO/IEC 27001 requires that management:

Systematically examine the organization's information security risks, taking account of the threats, vulnerabilities, and impacts;
Design and implement a coherent and comprehensive suite of information security controls and/or other forms of risk treatment (such as risk avoidance or risk transfer) to address those risks that are deemed unacceptable; and
Adopt an overarching management process to ensure that the information security controls continue to meet the organization's information security needs on an ongoing basis.
What controls will be tested as part of certification to ISO/IEC 27001 is dependent on the certification auditor. This can include any controls that the organisation has deemed to be within the scope of the ISMS and this testing can be to any depth or extent as assessed by the auditor as needed to test that the control has been implemented and is operating effectively.

Management determines the scope of the ISMS for certification purposes and may limit it to, say, a single business unit or location. The ISO/IEC 27001 certificate does not necessarily mean the remainder of the organization, outside the scoped area, has an adequate approach to information security management.

Other standards in the ISO/IEC 27000 family of standards provide additional guidance on certain aspects of designing, implementing and operating an ISMS, for example on information security risk management (ISO/IEC 27005).
Answer: The ISO 27001 standard works by requiring management of the information security management system to examine security risks, implement a comprehensive suite of security controls to manage the identified risks, and operate ongoing processes to measure control effectiveness and risk levels.
Question: How many episodes of the first season of Game of Thrones did Daniel Minahan direct?
Answer: Daniel Minahan directed three episodes of the first season of Game of Thrones: A Golden Crown, You Win or You Die, and The Pointy End.
Question: Name some of the great TV shows of the 1970s, 1980s and 1990s
Answer: Perfect Strangers, Three's Company, Cheers, Seinfeld, Mash, Small Wonder, Family Matters, Full House, The Montel Williams Show, Oprah, Sally Jesse Raphael, Dynasty, General Hospital
Question: How do you determine the market capitalization of a publicly traded company?
Answer: The market capitalization of a publicly traded company is calculated by the share price multiplied by the number of outstanding shares.
Question: What are new best results on standard benchmark?
Evidence: Table TABREF15 shows the final results on Vecmap. We first compare our model with the state-of-the-art unsupervised methods. Our model based on procrustes (Ours-Procrustes) outperforms Sinkhorn-BT on all test language pairs, and shows better performance than Adv-C-Procrustes on most language pairs. Adv-C-Procrustes gives very low precision on DE-EN, FI-EN and ES-EN, while Ours-Procrustes obtains reasonable results consistently. A possible explanation is that dual learning is helpful for providing good initiations, so that the procrustes solution is not likely to fall in poor local optima. The reason why Unsup-SL gives strong results on all language pairs is that it uses a robust self-learning framework, which contains several techniques to avoid poor local optima.
FLOAT SELECTED: Table 4: Accuracy (P@1) on Vecmap. The best results are bolded. †Results as reported in the original paper. For unsupervised methods, we report the average accuracy across 10 runs.
Table TABREF15 shows the final results on Vecmap.
FLOAT SELECTED: Table 4: Accuracy (P@1) on Vecmap. The best results are bolded. †Results as reported in the original paper. For unsupervised methods, we report the average accuracy across 10 runs.
Answer: New best results of accuracy (P@1) on Vecmap:
Ours-GeoMMsemi: EN-IT 50.00 IT-EN 42.67 EN-DE 51.60 DE-EN 47.22 FI-EN 39.62 EN-ES 39.47 ES-EN 36.43
Question: How does nextsum work?
Evidence: This work proposes an extractive summarization system that focuses on capturing rich summary-internal structure. Our key idea is that since summaries in a domain often follow some predictable structure, a partial summary or set of summary sentences should help predict other summary sentences. We formalize this intuition in a model called NextSum, which selects the next summary sentence based not only on properties of the source text, but also on the previously selected sentences in the summary. An example choice is shown in Table 1 . This setup allows our model to capture summary-specific discourse and topic transitions. For example, it can learn to expand on a topic that is already mentioned in the summary, or to introduce a new topic. It can learn to follow a script or discourse relations that are expected for that domain's summaries. It can even learn to predict the end of the summary, avoiding the need to explicitly define a length cutoff.
We formalize this intuition in a model called NextSum, which selects the next summary sentence based not only on properties of the source text, but also on the previously selected sentences in the summary.
Answer: The answers are shown as follows:
* selects the next summary sentence based not only on properties of the source text, but also on the previously selected sentences in the summary
Question: What is the pre-training batch size?
Evidence: We use a pre-trained Xnlg with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations BIBREF26. In the first pre-training stage, we directly use the 15-language pre-trained XLM BIBREF5 to initialize the parameters of our encoder and decoder. In the second stage, we use Wikipedia as the monolingual data for the DAE objective, and MultiUN BIBREF27 as the parallel data for the XAE objective. The DAE loss is trained with a weight of $0.5$. We train a two-language (English/Chinese) and a three-language (English/French/Chinese) Xnlg for two downstream NLG tasks, respectively. Following BIBREF5, we use the tokenizer provided by BIBREF28 for Chinese, and Moses for other languages, respectively. Then the words in all languages are split with a shared subword vocabulary learned by BPE BIBREF29. We use Adam optimizer with a linear warm-up over the first 4,000 steps and linear decay for later steps, and the learning rate is set to $10^{-4}$. The pre-training batch size is 64, and the sequence length is set to 256. It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
For fine-tuning on downstream NLG tasks, we use Adam optimizer with a learning rate of $5\times 10^{-6}$. We set the batch size as 16 and 32 for question generation and abstractive summarization, respectively. When the target language is the same as the language of training data, we fine-tune all parameters. When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder. We truncate the input sentences to the first 256 tokens. During decoding, we use beam search with beam size of 3, and limit the length of the target sequence to 80 tokens.
The pre-training batch size is 64, and the sequence length is set to 256.
Answer: 64.
Question: Given the reference text about The Sun Also Rises, when was the book written and who wrote it?
Evidence: The Sun Also Rises is a 1926 novel by American writer Ernest Hemingway, his first, that portrays American and British expatriates who travel from Paris to the Festival of San Fermín in Pamplona to watch the running of the bulls and the bullfights. An early modernist novel, it received mixed reviews upon publication. Hemingway biographer Jeffrey Meyers writes that it is now "recognized as Hemingway's greatest work" and Hemingway scholar Linda Wagner-Martin calls it his most important novel. The novel was published in the United States in October 1926 by Scribner's. A year later, Jonathan Cape published the novel in London under the title Fiesta. It remains in print.

The novel is a roman à clef: the characters are based on people in Hemingway's circle and the action is based on events, particularly Hemingway's life in Paris in the 1920s and a trip to Spain in 1925 for the Pamplona festival and fishing in the Pyrenees. Hemingway presents his notion that the "Lost Generation"—considered to have been decadent, dissolute and irretrievably damaged by World War I—was in fact resilient and strong. Hemingway investigates the themes of love and death, the revivifying power of nature and the concept of masculinity. His spare writing style, combined with his restrained use of description to convey characterizations and action, demonstrates his "Iceberg Theory" of writing.
Answer: The Sun Also Rises was written in 1926 by Ernest Hemingway.