DataHammer/scimrc huggingface dataset:
Question: Extract the difference between Gross Disposable income from Disposable Income
Evidence: The list below represents a national accounts derived indicator for a country or territory's gross household disposable income per capita (including social transfers in kind). According to the OECD, 'household disposable income is income available to households such as wages and salaries, income from self-employment and unincorporated enterprises, income from pensions and other social benefits, and income from financial investments (less any payments of tax, social insurance contributions and interest on financial liabilities). 'Gross’ means that depreciation costs are not subtracted.' This indicator also takes account of social transfers in kind 'such as health or education provided for free or at reduced prices by governments and not-for-profit organisations.' The data shown below is published by the OECD and is presented in purchasing power parity (PPP) in order to adjust for price differences between countries.
Answer: There is a unique difference between Gross Disposable Income and Household disposable income.  The first being that "gross" indicates the total about amount of income derived from all sources such as wages, investments, self-employment, pensions and social benefits before extracting depreciation costs.  Household disposable income includes all sources of income less social benefits, taxes, insurance, and liabilities such as rent, electricity and other ongoing financial commitments.  What is remaining is defined as household disposable income which is then indexed globally using PPP (purchasing power parity) in order to adjust for price differences in different countries.
Question: Give a short bulleted list of the academic positions held by Sechrest. Each bullet should have the name of the university and the titles that Sechrest had in that university.
Evidence: Sechrest joined the faculty of Pennsylvania State University (Penn State) in 1956 as an assistant professor of psychology. He left Penn State in 1958 to become an assistant professor at Northwestern University, where he was promoted to associate professor in 1964 and to full professor in 1967. In 1973, he became a professor of psychology at Florida State University, and in 1980, he was named director of the Center for Research on the Utilization of Scientific Knowledge in the Institute for Social Research at the University of Michigan. In 1984, he joined the University of Arizona as professor and chair of the Department of Psychology, serving as department chair until 1989. He served as president of the Society of Clinical Psychology (Division 12 of the American Psychological Association) in 1985. He continued to serve as an active faculty member at the University of Arizona until 2002, whereupon he became an emeritus professor there.
Answer: - Pennsylvania State University : assistant professor
- Northwestern University: assistant professor, associate professor, full professor
- Florida State University: professor of psychology
- University of Michigan: director of the Center for Research on the Utilization of Scientific Knowledge in the Institute for Social Research
- University of Arizona: professor, chair of the department of Pshychology, emeritus professor
Question: Who was Catelyn Stark originally betrothed to marry?
Answer: Catelyn Stark (nee Tully) was supposed to marry Brandon Stark, the heir to Winterfell, but Brandon was killed by the Mad King and instead, Catelyn married the next in line to the Winterfell claim, Eddard Stark.
Question: What hypothesis forms the crux of the team's method for sarcasm detection?
Evidence: Sarcasm often emanates from incongruity BIBREF9 , which enforces the brain to reanalyze it BIBREF10 . This, in turn, affects the way eyes move through the text. Hence, distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts. This hypothesis forms the crux of our method for sarcasm detection and we validate this using our previously released freely available sarcasm dataset BIBREF8 enriched with gaze information.
Hence, distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts. This hypothesis forms the crux of our method for sarcasm detection
Answer: Distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts.
Question: What are the country-specific drivers of international development rhetoric?
Evidence: Yet surprisingly little is known about the agenda-setting process for international development in global governance institutions. This is perhaps best demonstrated by the lack of information on how the different goals and targets of the MDGs were decided, which led to much criticism and concern about the global governance of development BIBREF1 . More generally, we know little about the types of development issues that different countries prioritise, or whether country-specific factors such as wealth or democracy make countries more likely to push for specific development issues to be put on the global political agenda.
The analysis of discussion of international development in annual UN General Debate statements therefore uncovers two principle development topics: economic development and sustainable development. We find that discussion of Topic 2 is not significantly impacted by country-specific factors, such as wealth, population, democracy, levels of ODA, and conflict (although there are regional effects). However, we find that the extent to which countries discuss sustainable development (Topic 7) in their annual GD statements varies considerably according to these different structural factors. The results suggest that broadly-speaking we do not observe linear trends in the relationship between these country-specific factors and discussion of Topic 7. Instead, we find that there are significant fluctuations in the relationship between factors such as wealth, democracy, etc., and the extent to which these states discuss sustainable development in their GD statements. These relationships require further analysis and exploration.
 More generally, we know little about the types of development issues that different countries prioritise, or whether country-specific factors such as wealth or democracy make countries more likely to push for specific development issues to be put on the global political agenda.
 We find that discussion of Topic 2 is not significantly impacted by country-specific factors, such as wealth, population, democracy, levels of ODA, and conflict (although there are regional effects). 
Answer: The answers are shown as follows:
* wealth 
* democracy 
* population
* levels of ODA
* conflict
Question: What makes the Expanse series better than other sci-fi?
Answer: What really sets The Expanse apart from other popular science fiction is the science. It's common for sci-fi writers to throw the laws of physics out the window or create a fake invention that bends the rules without any scientific basis to make their concept work. The Expanse has a realism that is unparalleled. If you've ever found yourself yelling at your book or TV saying "That's not even close to possible" then this is the sci-fi series for you.
Question: List down some facts about Shandao temple using given passage as a reference
Evidence: Shandao Temple (formerly transliterated as Shantao Temple Station until 2003), secondary station name Huashan, is a station on the Bannan line of Taipei Metro in Zhongzheng District, Taipei, Taiwan. It is named after the Shandao Temple.
Answer: 1. Shandao Temple, also known as Huashan, is a station on the Bannan line of the Taipei Metro in the Zhongzheng District of Taipei, Taiwan. It was formerly transliterated as Shantao Temple Station until 2003.
2. It bears the Shandao Temple's name.
Question: True or false, Chesapeake Bay Crab Spice are a flavor of Lays potato chip?
Answer: As hard as it may be to believe, Chesapeake Bay Crab Spice is a Lays potato chip flavor.
Question: Given this paragraph about the town of Birkenhead, what the does the name Birkenhead probably mean?
Evidence: The name Birkenhead probably means "headland overgrown with birch", from the Old English bircen meaning birch tree, of which many once grew on the headland which jutted into the river at Woodside. The name is not derived from the Birket, a stream which enters the Mersey between Birkenhead and Seacombe. The Birket is a later name which was introduced by Ordnance Survey.
Answer: It probably means "headland overgrown with birch".
Question: What is a traditional IRA or 401k?
Answer: The two primary options for retirement savings include individual retirement accounts and a defined contribution plan such as a 401k. Individuals can choose to save on their own and open an IRA, however a IRA opened by an individual won't carry the benefit of employer matching. Typically individuals contribute a percentage of their salary to their 401k, and many employers may offer matching contributions to that 401k up to a specified dollar amount.
Question: What is the fastest car?
Answer: In 2017, the Koenigsegg Agera RS reached a top speed of 447.19 km/h (277.87 mph).
Question: What team is the latest expansion in Major League Soccer?
Answer: The latest expansion team in Major League Soccer is St. Louis FC. They started off their first season in 2023 with five straight wins and a goal difference of plus fifteen.
Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?
Evidence: In a more practical scenario, text networks have been used in text classification tasks BIBREF8, BIBREF9, BIBREF10. The main advantage of the model is that it does not rely on deep semantical information to obtain competitive results. Another advantage of graph-based approaches is that, when combined with other approaches, it yields competitive results BIBREF11. A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.
While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12. In addition, semantically similar words not sharing the same lemma are mapped into distinct nodes. In order to address these issues, here we introduce a modification of the traditional network representation by establishing additional edges, referred to as “virtual” edges. In the proposed model, in addition to the co-occurrence edges, we link two nodes (words) if the corresponding word embedding representation is similar. While this approach still does not merge similar nodes into the same concept, similar nodes are explicitly linked via virtual edges.
A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.

While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12.
Answer: The answers are shown as follows:
* long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach
Question: Over what period of time were the tweets collected?
Evidence: We collected COVID-19 related Arabic tweets from January 1, 2020 until April 15, 2020, using Twitter streaming API and the Tweepy Python library. We have collected more than 3,934,610 million tweets so far. In our dataset, we store the full tweet object including the id of the tweet, username, hashtags, and geolocation of the tweet. We created a list of the most common Arabic keywords associated with COVID-19. Using Twitter’s streaming API, we searched for any tweet containing the keyword(s) in the text of the tweet. Table TABREF1 shows the list of keywords used along with the starting date of tracking each keyword. Furthermore, Table TABREF2 shows the list of hashtags we have been tracking along with the number of tweets collected from each hashtag. Indeed, some tweets were irrelevant, and we kept only those that were relevant to the pandemic.
We collected COVID-19 related Arabic tweets from January 1, 2020 until April 15, 2020, using Twitter streaming API and the Tweepy Python library. 
Answer: The answers are shown as follows:
* from January 1, 2020 until April 15, 2020
Question: what are the components of modern computers?
Answer: a modern computer consists of at least one processing element, typically a central processing unit (CPU) in the form of a microprocessor, along with some type of computer memory, typically semiconductor memory chips. The processing element carries out arithmetic and logical operations, and a sequencing and control unit can change the order of operations in response to stored information. Peripheral devices include input devices (keyboards, mice, joystick, etc.), output devices (monitor screens, printers, etc.), and input/output devices that perform both functions (e.g., the 2000s-era touchscreen). Peripheral devices allow information to be retrieved from an external source and they enable the result of operations to be saved and retrieved.
Question: Classify each of the following whether they help or hurt vision: glasses, binoculars, blindfold, sleep mask, telescope
Answer: Glasses: Help Vision
Binoculars: Help Vision
Blindfold: Hurt Vision
Sleep Mask: Hurt Vision
Telescope: Help Vision
Question: What is the happiness index report and which country tops the happiness index in the world
Evidence: The World Happiness Report is a publication that contains articles and rankings of national happiness, based on respondent ratings of their own lives, which the report also correlates with various (quality of) life factors. As of March 2023, Finland has been ranked the happiest country in the world six times in a row.

The report is a publication of the Sustainable Development Solutions Network, a global initiative of the United Nations. The report primarily uses data from the Gallup World Poll. Each annual report is available to the public to download on the World Happiness Report website. The Editors of the 2020 report are John F. Helliwell, Richard Layard, Jeffrey D. Sachs, and Jan-Emmanuel De Neve. Associate Editors are Lara Aknin, Shun Wang, and Haifang Huang
Answer: United nations every year in collaboration with sustainable development solution network perform survey and release the report global happiness index. As of March 2023, Finland has been ranked the happiest country in the world six times in a row.

The World Happiness Report is a publication that contains articles and rankings of national happiness, based on respondent ratings of their own lives, which the report also correlates with various (quality of) life factors
Question: Find the bird in this list: Arctic skua, Monkey, Lily
Answer: Arctic skua
Question: Give me a bullet list of lots of possible weather forecasts
Answer: - Sunny
- Partially cloudy
- Cloudy
- Showers
- Thunderstorms
- Hail
- Snowstorm
- Snow flurries
- Heavy fog
Question: What data do they select with pre-trained language models?
Evidence: As shown in the previous section, using the right data is critical for achieving good performance on an in-domain test set, and more data is not necessarily better. However, in real-world scenarios, the availability of data labeled by domain is limited, e.g. when working with large scale, web-crawled data. In this section we focus on a data-selection scenario where only a very small number of in-domain sentences are used to select data from a larger unlabeled parallel corpus. An established method for data selection was proposed by BIBREF4, which was also used in training the winning systems in WMT 2019 BIBREF39, BIBREF40. This method compares the cross-entropy, according to domain-specific and non-domain-specific language models, for each candidate sentence for selection. The sentences are then ranked by the cross-entropy difference, and only the top sentences are selected for training.
While the method by BIBREF4 is tried-and-true, it is based on simple n-gram language models which cannot generalize beyond the n-grams that are seen in the in-domain set. In addition, it is restricted to the in-domain and general-domain datasets it is trained on, which are usually small. On the contrary, pre-trained language models are trained on massive amounts of text, and, as we showed through unsupervised clustering, learn representations with domain-relevant information. In the following sections, we investigate whether this property of pretrained language models makes them useful for domain data selection.
In this section we focus on a dataselection scenario where only a very small number of in-domain sentences are used to select data from a larger unlabeled parallel corpus.
Answer: They focus on a dataselection scenario where only a very small number of in-domain sentences.
Question: Which model's performance does the crosslingual latent variable approach improve?
Evidence: Semantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and other NLP tasks. SRL is the problem of finding predicate-argument structure in a sentence, as illustrated below:
 INLINEFORM0 
Here, the predicate WRITE has two arguments: `Mike' as A0 or the writer, and `a book' as A1 or the thing written. The labels A0 and A1 correspond to the PropBank annotations BIBREF0 .
As the need for SRL arises in different domains and languages, the existing manually annotated corpora become insufficient to build supervised systems. This has motivated work on unsupervised SRL BIBREF1 , BIBREF2 , BIBREF3 . Previous work has indicated that unsupervised systems could benefit from the word alignment information in parallel text in two or more languages BIBREF4 , BIBREF5 , BIBREF6 . For example, consider the German translation of sentence INLINEFORM0 :
 INLINEFORM0 
If sentences INLINEFORM0 and INLINEFORM1 have the word alignments: Mike-Mike, written-geschrieben, and book-Buch, the system might be able to predict A1 for Buch, even if there is insufficient information in the monolingual German data to learn this assignment. Thus, in languages where the resources are sparse or not good enough, or the distributions are not informative, SRL systems could be made more accurate by using parallel data with resource rich or more amenable languages.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages. The model consists of individual Bayesian models for each language BIBREF3 , and crosslingual latent variables to incorporate soft role agreement between aligned constituents. This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs BIBREF4 . We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
We begin by briefly describing the unsupervised SRL pipeline and the monolingual semantic role induction model we use, and then describe our multilingual model.
This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs.
Answer: A multilingual unsupervised part-of-speech tagging model based on HMMs.
Question: What is the limitation of the chosen data?
Evidence: We base our experiments on a large collection of Bible translations crawled from the web, coming from various sources and periods of times. Any other multilingual data collection would work as well, but with the selected corpus we have the advantage that we cover the same genre and roughly the same coverage for each language involved. It is also easy to divide the data into training and test sets by using Bible verse numbers, which allows us to control for semantic similarity between languages in a way that would have been difficult in a corpus that is not multi-parallel. Altogether we have 1,303 translations in 990 languages that we can use for our purposes. These were chosen so that the model alphabet size is below 1000 symbols, which was satisfied by choosing only translations in Latin, Cyrillic or Greek script.
Certainly, there are disadvantages as well, such as the limited size (roughly 500 million tokens in total, with most languages having only one translation of the New Testament each, with roughly 200 thousand tokens), the narrow domain and the high overlap of named entities. The latter can lead to some unexpected effects when using nonsensical language vectors, as the model will then generate a sequence of random names.
The corpus deviates in some ways from an ideal multi-parallel corpus. Most translations are of the complete New Testament, whereas around 300 also contain the Old Testament (thus several times longer), and around ten contain only portions of the New Testament. Additionally, several languages have multiple translations, which are then concatenated. These translations may vary in age and style, but historical versions of languages (with their own ISO 639-3 code) are treated as distinct languages. During training we enforce a uniform distribution between languages when selecting training examples.
Altogether we have 1,303 translations in 990 languages that we can use for our purposes. These were chosen so that the model alphabet size is below 1000 symbols, which was satisfied by choosing only translations in Latin, Cyrillic or Greek script. Certainly, there are disadvantages as well, such as the limited size (roughly 500 million tokens in total, with most languages having only one translation of the New Testament each, with roughly 200 thousand tokens), the narrow domain and the high overlap of named entities.
Answer: The limited size, the narrow domain, and the high overlap of named entities.
Question: Was performance of the weakly-supervised model compared to the performance of a supervised model?
Evidence: The precision when labeling the first x ranked tweets as attack tweet is shown in the figure FIGREF39. The x-axis is the number of ranked tweets treated as attack tweets. And the y-axis is the corresponding precision. The straight line in figures FIGREF39, FIGREF43 and FIGREF51 is the result of a supervised LDA algorithm which is used as a baseline. Supervised LDA achieved 96.44 percent precision with 10 fold cross validation.
The straight line in figures FIGREF39, FIGREF43 and FIGREF51 is the result of a supervised LDA algorithm which is used as a baseline. 
Answer: Yes.