DataHammer/scimrc huggingface dataset:
Question: Was Venice always part of Italy?
Evidence: The Republic of Venice lost its independence when Napoleon Bonaparte conquered Venice on 12 May 1797 during the War of the First Coalition. Napoleon was seen as something of a liberator by the city's Jewish population. He removed the gates of the Ghetto and ended the restrictions on when and where Jews could live and travel in the city.

Venice became Austrian territory when Napoleon signed the Treaty of Campo Formio on 12 October 1797. The Austrians took control of the city on 18 January 1798. Venice was taken from Austria by the Treaty of Pressburg in 1805 and became part of Napoleon's Kingdom of Italy. It was returned to Austria following Napoleon's defeat in 1814, when it became part of the Austrian-held Kingdom of Lombardy–Venetia. In 1848 a revolt briefly re-established the Venetian republic under Daniele Manin, but this was crushed in 1849. In 1866, after the Third Italian War of Independence, Venice, along with the rest of the Veneto, became part of the newly created Kingdom of Italy.
Answer: Venice started off with refugees from neighboring Roman cities such as Padua, Aquileia, Treviso, Altino etc., and developed into a powerful maritime empire, a republic in it's own right.   In 1797, Venice was conquered by Napoleon Bonaparte and switched several times between being an Austrian territory,  part of the Kingdom of Italy, back to being part of Austria and eventually after the Italian Independence War of 1866 to being part of Italy.
Question: Given a reference text about LIV Golf, tell me what "LIV" means, when it was started and who financed it.
Evidence: LIV Golf  is a professional golf tour. The name "LIV" refers to the Roman numerals for 54, the score if every hole on a par-72 course were birdied and the number of holes to be played at LIV events. The first LIV Golf Invitational Series event started on 9 June 2022, at the Centurion Club near London, England. The Invitational Series became the LIV Golf League in 2023.

LIV Golf is financed by the Public Investment Fund, the sovereign wealth fund of Saudi Arabia.
Answer: LIV refers to the Roman numerals for 54. The first tournament was hosted June 2022 and s funded by the wealth fund of Saudi Arabia
Question: In which certain heads was attention disabled in experiments?
Evidence: Our experiments suggest that certain heads have a detrimental effect on the overall performance of BERT, and this trend holds for all the chosen tasks. Unexpectedly, disabling some heads leads not to a drop in accuracy, as one would expect, but to an increase in performance. This is effect is different across tasks and datasets. While disabling some heads improves the results, disabling the others hurts the results. However, it is important to note that across all tasks and datasets, disabling some heads leads to an increase in performance. The gain from disabling a single head is different for different tasks, ranging from the minimum absolute gain of 0.1% for STS-B, to the maximum of 1.2% for MRPC (see fig:disableheadsall). In fact, for some tasks, such as MRPC and RTE, disabling a random head gives, on average, an increase in performance. Furthermore, disabling a whole layer, that is, all 12 heads in a given layer, also improves the results. fig:disablelayers shows the resulting model performance on the target GLUE tasks when different layers are disabled. Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%.
The gain from disabling a single head is different for different tasks, ranging from the minimum absolute gain of 0.1% for STS-B, to the maximum of 1.2% for MRPC (see fig:disableheadsall).
Furthermore, disabling a whole layer, that is, all 12 heads in a given layer, also improves the results. fig:disablelayers shows the resulting model performance on the target GLUE tasks when different layers are disabled.
Answer: The answers are shown as follows:
* single head
* disabling a whole layer, that is, all 12 heads in a given layer
Question: How can I support a loved one going through anxiety and depression?
Answer: This is a great question. As a person who has suffered from depression and anxiety in the past, I have a handful of suggestion to provide. I would suggest not to force the person into any of these things. Give them these suggestions and let them decide for themselves. Let them know you are there to help them for anything they need.
1. Meditate daily: When you start meditation, it seems useless. But overtime, you start feeling the benefits. For some people it takes days, for some months. But just learn to sit in peace with yourself.
2. Find a therapist you feel comfortable with: I went through 3-4 therapists and therapy styles till I found the right one. And I wouldn't want it any other way. I used to think therapy is useless, but now I swear by it.
3. Set time aside in the day to stress: For example, I used to set aside 8 PM to stress. When I would feel anxious about something I could not control, I would just tell myself that I will think about it at 8 PM. It takes practice but it helps.
4. Go outdoors or find a hobby group: As much as I love my bed and TV, just getting myself out there between people helped me so much. I would recommend it for everyone. Being in introvert, I used to hate being around new people. Now I just put myself out there and I know people almost everywhere I go for my hobbies.
5. It's ok to take medication. If anxiety and depression are spoiling your quality of life, it's ok to speak to a psychiatrist and see if they can recommend any medication. It almost was taboo growing up for me, but now I see the benefits.
6. Talk your partners / close friends and express your feelings. Take support from people around you. If they know you are struggling, they will be sensitive enough to help you out.
Hope this helps.
Question: How many times have the Vancouver Canucks won the Stanley cup?
Answer: The Vancouver Canucks have won the Stanley cup 0 times. They have appeared in the finals and lost 3 times in 1982, 1994, and 2011.
Question: What movies won best picture Oscars the last 5 years
Answer: 2023 Everything Everywhere All at Once
2022 CODA
2021 Nomadland 
2020 Parasite
2019 Green Book
Question: How can I make work meetings more efficient?
Answer: You will have to find the right techniques for your type of meeting, but there are some useful generic techniques that you can try.  Watch the time: keep the meeting as short as possible; time-box each discussion; and make sure each person gets to speak.  Be purposeful: set a clear agenda beforehand; only ask important stakeholders to join; and define takeaways and action items at the end.  Prepare beforehand: before the meeting, share materials for people to read or comment on, as appropriate, in order to save meeting time for important discussions.  If the meeting is simply not useful, you should eliminate it.
Question: What is an example of a prefixing language?
Evidence: Since English and Spanish are both Indo-European languages, and, thus, relatively similar, we further add a third, unrelated target language. We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing.
We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing.
Answer: The answers are shown as follows:
* Zulu
Question: Is it limited to transfer between a foreign language and English in this paper?
Evidence: There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions.
The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification.
We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English.
Answer: No, it is not limited.
Question: Which subtasks do they evaluate on?
Evidence: Semantic parsing aims to solve the problem of canonicalizing language and representing its meaning: given an input sentence, it aims to extract a semantic representation of that sentence. Abstract meaning representation BIBREF0 , or AMR for short, allows us to do that with the inclusion of most of the shallow-semantic natural language processing (NLP) tasks that are usually addressed separately, such as named entity recognition, semantic role labeling and co-reference resolution. AMR is partially motivated by the need to provide the NLP community with a single dataset that includes basic disambiguation information, instead of having to rely on different datasets for each disambiguation problem. The annotation process is straightforward, enabling the development of large datasets. Alternative semantic representations have been developed and studied, such as CCG BIBREF1 , BIBREF2 and UCCA BIBREF3 .
Abstract meaning representation BIBREF0 , or AMR for short, allows us to do that with the inclusion of most of the shallow-semantic natural language processing (NLP) tasks that are usually addressed separately, such as named entity recognition, semantic role labeling and co-reference resolution.
Answer: The answers are shown as follows:
*  entity recognition, semantic role labeling and co-reference resolution
Question: Where do recent neural TS models draw on insights from to learn lexical simplification and content reduction using the encoder-decoder model?
Evidence: Text simplification (TS) can be viewed as monolingual translation task, translating between text variations within a single language. Recent neural TS models draw on insights from neural machine translation to learn lexical simplification and content reduction using encoder-decoder model. But different from neural machine translation, we cannot obtain enough ordinary and simplified sentence pairs for TS, which are expensive and time-consuming to build. Target-side simplified sentences plays an important role in boosting fluency for statistical TS, and we investigate the use of simplified sentences to train, with no changes to the network architecture. We propose to pair simple training sentence with a synthetic ordinary sentence via back-translation, and treating this synthetic data as additional training data. We train encoder-decoder model using synthetic sentence pairs and original sentence pairs, which can obtain substantial improvements on the available WikiLarge data and WikiSmall data compared with the state-of-the-art methods.
Recent neural TS models draw on insights from neural machine translation to learn lexical simplification and content reduction using encoder-decoder model.
Answer: Recent neural TS models draw on insights from neural machine translation.
Question: Identify which instrument is string or percussion: Berimbau, Soku
Answer: Soku is string, Berimbau is percussion.
Question: From the passage provided, extract the products & services owned by Meta. Separate them with a comma.
Evidence: Meta Platforms, Inc., doing business as Meta and formerly named Facebook, Inc., and TheFacebook, Inc., is an American multinational technology conglomerate based in Menlo Park, California. The company owns Facebook, Instagram, and WhatsApp, among other products and services. Meta is one of the world's most valuable companies and among the ten largest publicly traded corporations in the United States. It is considered one of the Big Five American information technology companies, alongside Alphabet (Google), Amazon, Apple, and Microsoft.

Meta's products and services include Facebook, Instagram, WhatsApp, Messenger, and Meta Quest. It has acquired Oculus, Mapillary, CTRL-Labs, Kustomer, and has a 9.99% stake in Jio Platforms. In 2021, the company generated 97.5% of its revenue from the sale of advertising.

On October 28, 2021, the parent company of Facebook changed its name from Facebook, Inc., to Meta Platforms, Inc., to "reflect its focus on building the metaverse". According to Meta, the "metaverse" refers to the integrated environment that links all of the company's products and services.
Answer: Facebook, Instagram, WhatsApp, Messenger, and Meta Quest
Question: What is the most romantic season of the year and why?
Answer: The most romantic season of the year is winter. During winter you have all of the holidays like Thanksgiving, Christmas and New Years. Many times this is the time you really get to spend a lot of time with your loved ones and also introduce your significant other to the family. During winter you also will enjoy a lot of coffee and hot cocoa dates, going to the movies and laying around the fireplace to stay warm. With Christmas and New Years there will also be lights and decorations throughout the city, making it feel much more magical.
Question: Give me the details of Ezekiel's Temple
Evidence: Features of the temple are described in detail in the book of Ezekiel, in the form of a vision given to the prophet. Physical characteristics of the multi-level wood-panelled structure such as gateways, outer and inner courts, side chambers and vestibules, archways, doors, windows, sanctuary and altar are described. Some furnishings are described. Details of decoration are given, for example cherubim and palm trees carved on the doors and walls. The purposes of the side chambers are given, for example, for robing of the priests, for consumption of the flesh of sacrifices by the priests, and for singers. Dimensions are given based on the cubit.
Answer: The biblical book of Ezekiel includes a description of a temple structure, with specific details regarding materials used such as wood, architectural features such as archways and gateways, and interior features such as the inclusion of an altar.  Dimensions and decorative details such as natural carvings are also included in the description.
Question: Given the reference text about The Masters, where is the tournament played and what is the name of the golf course?
Evidence: The Masters Tournament (usually referred to as simply The Masters, or the U.S. Masters outside North America) is one of the four major championships in professional golf. Scheduled for the first full week of April, the Masters is the first major of the year, and unlike the others, it is always held at the same location, Augusta National Golf Club, a private course in the city of Augusta, Georgia, in the southeastern United States.

The Masters was started by amateur champion Bobby Jones and investment banker Clifford Roberts. After his grand slam in 1930, Jones acquired the former plant nursery and co-designed Augusta National with course architect Alister MacKenzie. First played 89 years ago in 1934, the tournament is an official money event on the PGA Tour, the European Tour, and the Japan Golf Tour. The field of players is smaller than those of the other major championships because it is an invitational event, held by the Augusta National Golf Club.
Answer: The Masters is played at Augusta National Golf Club in Augusta, Georgia.
Question: What is the name of the largest red-light district in Amsterdam?
Answer: The largest red-light district in Amsterdam is De Wallen.
Question: What are the new 7 Wonders Cities:
Answer: These are the new  7 Wonders Cities:

- Durban, South Africa
- Vigan, Philippines
- Havana, Cuba
- Kuala Lumpur, Malaysia
- Beirut, Lebanon
- Doha, Qatar
- La Paz, Bolivia
Question: why did Syd Barrett left the Pink Floyd?
Answer: Syd Barrett was one of the main members of the Pink Floyd. He has used drugs a lot and after a while he was not able to perform with the team. Even though the band gave him multiple chances, he could not keep up with the band. In the end, they had to ask him to leave the band. The main reason that he has left the team is his health problems and addiction to drugs.
Question: How higher are F1 scores compared to previous work?
Evidence: Among the baselines, HRL achieves significantly higher F1 scores on the two datasets. We run their model and our models five times and report the median results in Table TABREF15. Scores of other baselines in Table TABREF15 are taken from previous published papers BIBREF6, BIBREF11, BIBREF14. Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. We perform a statistical significance test (t-test) under a bootstrap pairing between HRL and our models and see that the higher F1 scores achieved by our models are statistically significant ($p < 0.001$). Next, we combine the outputs of five runs of our models and five runs of HRL to build ensemble models. For a test instance, we include those tuples which are extracted in the majority ($\ge 3$) of the five runs. This ensemble mechanism increases the precision significantly on both datasets with a small improvement in recall as well. In the ensemble scenario, compared to HRL, WDec achieves $4.2\%$ and $3.5\%$ higher F1 scores and PNDec achieves $4.2\%$ and $2.9\%$ higher F1 scores on the NYT29 and NYT24 datasets respectively.
Among the baselines, HRL achieves significantly higher F1 scores on the two datasets.
Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively.
In the ensemble scenario, compared to HRL, WDec achieves $4.2\%$ and $3.5\%$ higher F1 scores and PNDec achieves $4.2\%$ and $2.9\%$ higher F1 scores on the NYT29 and NYT24 datasets respectively.
Answer: The answers are shown as follows:
* Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively
* In the ensemble scenario, compared to HRL, WDec achieves $4.2\%$ and $3.5\%$ higher F1 scores
Question: What datasets are used in this paper?
Evidence: Given a natural language INLINEFORM0 and a table INLINEFORM1 with INLINEFORM2 columns and INLINEFORM3 rows as the input, the task is to output a SQL query INLINEFORM4 , which could be executed on table INLINEFORM5 to yield the correct answer of INLINEFORM6 . We conduct experiments on WikiSQL BIBREF8 , which provides 87,726 annotated question-SQL pairs over 26,375 web tables. In this work, we do not use either SQL queries or answers in the training process. We use execution accuracy as the evaluation metric, which measures the percentage of generated SQL queries that result in the correct answer.
Given a natural language question and a knowledge graph, the task aims to correctly answer the question with evidences from the knowledge graph. We do our study on SimpleQuestions BIBREF10 , which includes 108,442 simple questions, each of which is accompanied by a subject-relation-object triple. Questions are constructed in a way that subject and relation are mentioned in the question, and that object is the answer. The task requires predicting the entityId and the relation involved in the question.
We conduct experiments on SequentialQA BIBREF9 which is derived from the WikiTableQuestions dataset BIBREF19 . It contains 6,066 question sequences covering 17,553 question-answer pairs. Each sequence includes 2.9 natural language questions on average. Different from WikiSQL which provides the correct logical form for each question, SequentialQA only annotates the correct answer. This dataset is also harder than the previous two, since it requires complex, highly compositional logical forms to get the answer. Existing approaches are evaluated by question answering accuracy, which measures whether the predicted answer is correct or not.
We conduct experiments on WikiSQL BIBREF8 , which provides 87,726 annotated question-SQL pairs over 26,375 web tables.
We do our study on SimpleQuestions BIBREF10 , which includes 108,442 simple questions, each of which is accompanied by a subject-relation-object triple.
We conduct experiments on SequentialQA BIBREF9 which is derived from the WikiTableQuestions dataset BIBREF19 .
Answer: The answers are shown as follows:
* WikiSQL
* SimpleQuestions
* SequentialQA