DataHammer/scimrc huggingface dataset:
Question: What external corpus do they use in their case?
Evidence: To estimate the quality of topic models, we use two main automatic measures: topic coherence and kernel uniqueness. For human content analysis, measures of topic coherence and kernel uniqueness are both important and complement each other. Topics can be coherent but have a lot of repetitions. On the other hand, generated topics can be very diverse, but incoherent within each topic.
Topic coherence is an automatic metric of interpretability. It was shown that the coherence measure has a high correlation with the expert estimates of topic interpretability BIBREF9 , BIBREF17 . Mimno BIBREF9 described an experiment comparing expert evaluation of LDA-generated topics and automatic topic coherence measures. It was found that most "bad" topics consisted of words without clear relations between each other.
Newman et al. BIBREF6 asked users to score topics on a 3-point scale, where 3=“useful” (coherent) and 1=“useless” (less coherent). They instructed the users that one indicator of usefulness is the ease by which one could think of a short label to describe a topic. Then several automatic measures, including WordNet-based measures and corpus co-occurrence measures, were compared. It was found that the best automatic measure having the largest correlation with human evaluation is word co-occurrence calculated as point-wise mutual information (PMI) on Wikipedia articles. Later Lau et al. BIBREF17 showed that normalized poinwise mutual information (NPMI) BIBREF18 calculated on Wikipedia articles correlates even more strongly with human scores.
We calculate automatic topic coherence using two measure variants. The coherence of a topic is the median PMI (NPMI) of word pairs representing the topic, usually it is calculated for INLINEFORM0 most probable elements (in our study ten elements) in the topic. The coherence of the model is the median of the topic coherence. To make this measure more objective, it should be calculated on an external corpus BIBREF17 . In our case, we use Wikipedia dumps. DISPLAYFORM0 
Human-constructed topics usually have unique main words. The measure of kernel uniqueness shows to what extent topics are different from each other and is calculated as the number of unique elements among most probable elements of topics (kernels) in relation to the whole number of elements in kernels. DISPLAYFORM0 
If uniqueness of the topic kernels is closer to zero then many topics are similar to each other, contain the same words in their kernels. In this paper the kernel of a topic means the ten most probable words in the topic. We also calculated perplexity as the measure of language models. We use it for additional checking the model quality.
The coherence of the model is the median of the topic coherence. To make this measure more objective, it should be calculated on an external corpus [19]. In our case, we use Wikipedia dumps.
Answer: Wikipedia dumps.
Question: Can you please tell some details about Malhar Rao Holkar from the given text?
Evidence: Malhar Rao Holkar (1694–1766), a Maratha chief serving Peshwa Baji Rao, established the dynasty's rule over Indore. In the 1720s, he led Maratha armies in Malwa region, and in 1733 was granted 9 parghanas in the vicinity of Indore by the Peshwa. The township of Indore had already existed as an independent principality established by Nandlal Mandloi of Kampel, Nandlal Mandloi was won by the Maratha force and allowed them to camp across the Khan River. In 1734, Malhar Rao established a camp later called Malharganj. In 1747, he started the construction of his royal palace, the Rajwada. By the time of his death, he ruled much of Malwa, and was acknowledged as one of the five virtually independent rulers of the Maratha Confederacy.

He was succeeded by Ahilyabai Holkar (r. 1767–1795), his daughter-in-law. She was born in the Chaundi village in Maharashtra. She moved the capital to Maheshwar, south of Indore on the Narmada River. Rani Ahilyabai was a prolific builder and patron of Hindu temples in Maheshwar and Indore. She also built temples at sacred sites outside her kingdom, from Dwarka in Gujarat east to the Kashi Vishwanath Temple at Varanasi on the Ganges.
Answer: 1. Malhar Rao Holkar (1694–1766), a Maratha , established the Dynasty 's rule over Indore.
2.In  the 1720s he led Maratha armies in Malwa region and in1733 was granted 9 parghanas in the vicinity of Indore by the peshwa.
3. The township of Indore had already existed as an independent principality established by Nandlal Mandloi of kampel
4. Nandlal Mandloi was won by Maratha force and allowed them to camp across the Khan river.
5.In 1734, Malhar Rao established a camp later called Malhar ganj.
6. In 1747,he started the construction of his royal palace,the Rajwada.
Question: Extract the name of the founder of Salesforce from the text
Evidence: Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California. It provides customer relationship management (CRM) software and applications focused on sales, customer service, marketing automation, e-commerce, analytics, and application development.

Founded by former Oracle executive Marc Benioff, Salesforce quickly grew into one of the largest companies in the world, making its IPO in 2004. Salesforce's continued growth makes it the first cloud computing company to reach US$1 billion in annual revenue by fiscal year 2009, and the world's largest enterprise software firm by 2022.

Today, Salesforce is one of the largest technology companies in the world, and as of September 19, 2022, is the 61st largest company in the world by market cap with a value of nearly US$153 billion. Salesforce ranked 136th on the most recent edition of the Fortune 500, making US$26.5 billion in 2022. Since 2020, Salesforce has also been a component of the Dow Jones Industrial Average.
Answer: Marc Benioff
Question: Given this paragraph about Taylor Swift re-recording her masters, who was responsible for the controversy?
Evidence: American singer-songwriter Taylor Swift has come in dispute with her former record label, Big Machine Records, its founder Scott Borchetta, and new owner Scooter Braun, over the ownership of the master recordings of her first six studio albums. It is a highly publicized conflict, drawing widespread attention and media coverage since its ignition in 2019.
Answer: Scooter Braun.
Question: From the passage provided, list the traits which are often associated with Procrastination (in a comma separated manner).
Evidence: Procrastination is the action of unnecessarily and voluntarily delaying or postponing something despite knowing that there will be negative consequences for doing so. The word has originated from the Latin word procrastinatus, which itself evolved from the prefix pro-, meaning "forward," and crastinus, meaning "of tomorrow." Oftentimes, it is a habitual human behaviour. It is a common human experience involving delay in everyday chores or even putting off salient tasks such as attending an appointment, submitting a job report or academic assignment, or broaching a stressful issue with a partner. Although typically perceived as a negative trait due to its hindering effect on one's productivity often associated with depression, low self-esteem, guilt and inadequacy, it can also be considered a wise response to certain demands that could present risky or negative outcomes or require waiting for new information to arrive.

From a cultural and a social perspective, students from both Western and non-Western cultures are found to exhibit academic procrastination, but for different reasons. Students from Western cultures tend to procrastinate in order to avoid doing worse than they have done before or from failing to learn as much as they should have, whereas students from non-Western cultures tend to procrastinate in order to avoid looking incompetent, or to avoid demonstrating a lack of ability in front of their peers. It is also important to consider how different cultural perspectives of time management can impact procrastination. For example, in cultures that have a multi-active view of time, people tend to place a higher value on making sure a job is done accurately before finishing. In cultures with a linear view of time, people tend to designate a certain amount of time on a task and stop once the allotted time has expired.
Answer: Based on the passage provided, here are the traits which are often associated with Procrastination: depression, low self-esteem, guilt, inadequacy.
Question: What is epistatis?
Answer: Epistasis is a phenomenon in genetics in which the effect of a gene mutation is dependent on the presence or absence of mutations in one or more other genes, respectively termed modifier genes. In other words, the effect of the mutation is dependent on the genetic background in which it appears. Epistatic mutations therefore have different effects on their own than when they occur together. Originally, the term epistasis specifically meant that the effect of a gene variant is masked by that of a different gene.

The concept of epistasis originated in genetics in 1907 but is now used in biochemistry, computational biology and evolutionary biology. The phenomenon arises due to interactions, either between genes (such as mutations also being needed in regulators of gene expression) or within them (multiple mutations being needed before the gene loses function), leading to non-linear effects. Epistasis has a great influence on the shape of evolutionary landscapes, which leads to profound consequences for evolution and for the evolvability of phenotypic traits.
Question: What languages did the authors define a new cross-lingual document classification task for?
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 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.
Answer: English, French, Spanish, Italian, German, Russian, Chinese and Japanese.
Question: What are the best hairstyles for a round face?
Answer: Long hair, angled cut, beach waves
Question: What problem do they apply transfer learning to?
Evidence: We took inspiration from these works to design our experiments to solve the CSKS task.
We took inspiration from these works to design our experiments to solve the CSKS task.
Answer: The answers are shown as follows:
* CSKS task
Question: Categorize the following countries based on the language their majority speaks: Iraq, Iran, Turkey, UAE, Qatar, Afghanistan, Syria
Answer: Iran and Afghanistan are majority Farsi (or a variation of it) speaking. Turkey is the majority Turkish speaking. Majority of people in Iraq, UAE, Qatar, and Syria speak Arabic. Contrary to the generally held belief that Iran and Afghanistan are a Arabic speaking countries, they are not. Farsi is a totally different language.
Question: I am working on a blog post on the effect of Snapchat on teenagers.  Can you help me think of a few titles for the post?  For background, I think Snapchat has a negative effect on our teenagers ability to focus, maintain healthy in-person relationships, and develop into productive adults.
Answer: Sure, here are a few ideas: "Snapchat negatively affects our teens focus and ability to maintain healthy relationships," "Snapchat and the decline of youth productivity," Snapchat filters hide the complexities of teenage reality," "Snapchat wins when our teenagers lose themselves online," and "But first, let me take a selfie: teens put off college and the workforce to become social influencers."
Question: What is the difference between a mechanical watch and a quartz watch?
Answer: Mechanical watches are not powered by batteries within the individual watch compartments. They are powered by gears and springs that manually need winding, maintenance, and support. This technology was developed in a time period where quartz watch technology did not exist, and as a result, needed a more simplified way of maintaining time. Since mechanical watches do not rely on a battery, they are often -/+ 2 seconds every day and need regular resetting.
Question: What are the 3 branches of science?
Answer: Modern Science is mainly constituted of three main branches, i.e. Natural Sciences, Social Sciences, and Formal Sciences as these three areas peruse the nature of our world and the universe most broadly. Thus, the three main branches of Science are:

Natural Sciences
Social Sciences
Formal Sciences
Question: What is the probability of dropout?
Evidence: We minimize the binary cross-entropy loss between the predicted outputs and the targets. We train our neural model with mini-batch size 8 using Adam BIBREF12 with learning rate INLINEFORM0 and decay the learning rate by multiplying INLINEFORM1 if the F1-Multi score on the validation set decreases during the validation. Gradient norms are clipped within 5 to prevent gradient explosion for feed-forward networks or recurrent neural networks. Since the training corpus is rather small, we use dropout BIBREF13 with probability INLINEFORM2 to prevent overfitting.
Since the training corpus is rather small, we use dropout (Srivastava et al., 2014) with probability 0.3 to prevent overfitting.
Answer: 0.3.
Question: As Chancellor for the Universities of Uppsala and Lund, what did De Geer promote?
Evidence: In 1876 De Geer became the first Prime Minister of Sweden following a reform where the previous offices of Prime Minister for Justice (which he held at the time) and Prime Minister for Foreign Affairs were changed into Minister for Justice and a Minister for Foreign Affairs. He served until April 1880, when the failure of his repeated efforts to settle the armaments issue again induced him to resign. From 1881 to 1888 he was Chancellor for the Universities of Uppsala and Lund. He was an advocate of free trade and economic liberalism. Some argue that it was De Geer who laid the foundations for the strong economic growth in Sweden from 1870 to 1970.
Answer: While holding this position, De Geer promoted free trade and economic liberalism.
Question: Retrieve and list the Sum41 albums mentioned in the text
Evidence: Sum 41 is a Canadian rock band from Ajax, Ontario. Originally called Kaspir, the band was formed in 1996 and currently consists of Deryck Whibley (lead vocals, guitars, keyboards), Dave Baksh (lead guitar, backing vocals), Jason "Cone" McCaslin (bass, backing vocals), Tom Thacker (guitars, keyboards, backing vocals), and Frank Zummo (drums, percussion, occasional backing vocals).

In 1999, Sum 41 signed an international record deal with Island Records and released its first EP, Half Hour of Power, in 2000. The band released its debut album, All Killer No Filler, in 2001. The album achieved mainstream success with its first single, "Fat Lip", which reached number one on the Billboard Modern Rock Tracks chart and remains the band's most successful single to date. The album's next singles "In Too Deep" and "Motivation" also achieved commercial success. All Killer No Filler was certified platinum in both the United States and the United Kingdom and triple platinum in Canada. In 2002, the band released Does This Look Infected?, which was also a commercial and critical success. The singles "The Hell Song" and "Still Waiting" both charted highly on the modern rock charts.

The band released its next album, Chuck, in 2004, led by singles "We're All to Blame" and "Pieces". The album proved successful, peaking at number 10 on the Billboard 200. In 2007, the band released Underclass Hero, which was met with a mixed reception, but became the band's highest-charting album to date. It was also the band's last album on Aquarius Records. The band released the album Screaming Bloody Murder, on Island Records in 2011 to a generally positive reception, though it fell short of its predecessors' commercial success. The band's sixth studio album, 13 Voices was released in 2016. IMPALA awarded the album with a double gold award for 150,000 sold copies across Europe. The band's seventh studio album Order in Decline was released on July 19, 2019. The band is in production of their eighth studio album titled Heaven and Hell. A release date has yet to be announced.
Answer: The albums mentioned in the text are:
- All Killer No Filler
- Does This Look Infected?
- Chuck
- Underclass Hero
- Screaming Bloody Murder
- 13 Voices
- Order in Decline 
- Heaven and Hell
Question: What is the batch size for FastText?
Evidence: We experimented with a dataset of 16K annotated tweets made available by the authors of BIBREF0 . Of the 16K tweets, 3383 are labeled as sexist, 1972 as racist, and the remaining are marked as neither sexist nor racist. For the embedding based methods, we used the GloVe BIBREF5 pre-trained word embeddings. GloVe embeddings have been trained on a large tweet corpus (2B tweets, 27B tokens, 1.2M vocab, uncased). We experimented with multiple word embedding sizes for our task. We observed similar results with different sizes, and hence due to lack of space we report results using embedding size=200. We performed 10-Fold Cross Validation and calculated weighted macro precision, recall and F1-scores.
We use `adam' for CNN and LSTM, and `RMS-Prop' for FastText as our optimizer. We perform training in batches of size 128 for CNN & LSTM and 64 for FastText. More details on the experimental setup can be found from our publicly available source code.
We perform training in batches of size 128 for CNN & LSTM and 64 for FastText.
Answer: 64.
Question: Choose the word which is different from the rest: Cream, Oil, Butter, Curd, Cheese.
Answer: All except Oil are products obtained from milk.
Question: What is a ceiling joist?
Answer: The main framing members used to span various structures in modern building construction.
Question: What is the difference between stress and strain?
Answer: Stress is calculated as the pressure applied per unit area. So stress is essentially the force applied to an object divided by the cross sectional area of where the force is being applied to.

Strain is calculated as the change in  parameter value (length, height or angle) as it experiences stress. It is unitless. So strain is essentially the deformation that occurs due to the stress that is applied.

Young's Modulus is a mechanical property that is computed by dividing stress over strain. It is typically expressed in GPa (giga Pascals)
Question: Does their detection tool work better than human detection?
Evidence: We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \lambda=-5)$, where true positive rate was $40.4\%$, while the true negative rate of the real class was $62.7\%$. The precision were $16\%$ and $86\%$, respectively. The class-averaged F-score is $47.6\%$, which is close to random. Detailed classification reports are shown in Table~\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \emph{our NMT-Fake reviews pose a significant threat to review systems}, since \emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper.
Figure~\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \lambda=-5$) are detected with an excellent 97\% F-score.
The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \lambda=-5)$, where true positive rate was $40.4\%$, while the true negative rate of the real class was $62.7\%$. The precision were $16\%$ and $86\%$, respectively. The class-averaged F-score is $47.6\%$, which is close to random. Detailed classification reports are shown in Table~\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \emph{our NMT-Fake reviews pose a significant threat to review systems}, since \emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper.
The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \lambda=-5$) are detected with an excellent 97\% F-score.
Answer: Yes.
Question: Given the following paragraph about Latin Text, what does "Dona nobis pacem" mean in English?
Evidence: "Dona nobis pacem" (Ecclesiastical Latin: [ˈdona ˈnobis ˈpatʃem], "Give us peace") is a round for three parts to a short Latin text from the Agnus Dei. The melody has been passed orally. The round is part of many hymnals and songbooks. Beyond use at church, the round has been popular for secular quests for peace, such as the reunification of Germany.
Answer: "Give us peace"
Question: What is SoulCycle?
Answer: SoulCycle is 45-minute indoor cycling class. SoulCycle implements choreography on a stationery bike, making it rhythmic-based. When you go to a SoulCycle class, you are expected to rent or bring a pair of cycling shoes that clip into your bike and you then enter a dark candle-lit room with loud music.