DataHammer/scimrc huggingface dataset:
Question: What will they explore in the future?
Evidence: We have shown that ERP components can be predicted from neural networks pretrained as language models and fine-tuned to directly predict those components. To the best of our knowledge, prior work has not successfully used statistical models to predict all of these components. Furthermore, we have shown that multitask learning benefits the prediction of ERP components and can suggest how components relate to each other. At present, these joint-training benefit relationships are only suggestive, but if these relationships ultimately lead to insights about what drives each ERP component, then the components become more useful tools for studying human language comprehension. By using multitask learning as a method of characterization, we have found some expected relationships (LAN+P600 and ELAN+P600) and several more surprising relationships. We believe that this is exactly the kind of finding that makes multitask learning an interesting exploratory technique in this area. Additionally, we have shown that information can be shared between heterogeneous types of data (eye-tracking, self-paced reading, and ERP components) in the domain of human language processing prediction, and in particular between behavioral and neural data. Given the small datasets associated with human language processing, using heterogeneous data is a potentially major advantage of a multitask approach. In future work, we will further explore what information is encoded into the model representations when neural and behavioral data are used to train neural networks, and how these representations differ from the representations in a model trained on language alone.
In future work, we will further explore what information is encoded into the model representations when neural and behavioral data are used to train neural networks, and how these representations differ from the representations in a model trained on language alone.
Answer: What information is encoded into the model representations when neural and behavioral data are used to train neural networks, and how these representations differ from the representations in a model trained on language alone.
Question: From the passage provided, extract the name of the Director of the Star Wars movie
Evidence: Star Wars (retroactively titled Star Wars: Episode IV – A New Hope) is a 1977 American epic space opera film written and directed by George Lucas, produced by Lucasfilm and distributed by 20th Century-Fox. It is the first film in the Star Wars film series and fourth chronological chapter of the "Skywalker Saga". Set "a long time ago" in a fictional universe where the galaxy is ruled by the tyrannical Galactic Empire, the story focuses on a group of freedom fighters known as the Rebel Alliance, who aim to destroy the Empire's newest weapon, the Death Star. Luke Skywalker becomes caught in the conflict while learning the ways of a metaphysical power known as "the Force" from Jedi Master Obi-Wan Kenobi. The cast includes Mark Hamill, Harrison Ford, Carrie Fisher, Peter Cushing, Alec Guinness, David Prowse, James Earl Jones, Anthony Daniels, Kenny Baker, and Peter Mayhew.
Answer: Star wars was directed and written by George Lucas
Question: What data sets are used in this article?
Evidence: Although we did not have access to a low-resource dataset, there is a corpus of noisy multi-speaker speech that simulates many of the conditions we expect to find in our motivating applications: the CALLHOME Spanish–English speech translation dataset (LDC2014T23; Post el al., 2013). We ran UTD over all 104 telephone calls, which pair 11 hours of audio with Spanish transcripts and their crowdsourced English translations. The transcripts contain 168,195 Spanish word tokens (10,674 types), and the translations contain 159,777 English word tokens (6,723 types). Though our system does not require Spanish transcripts, we use them to evaluate UTD and to simulate a perfect UTD system, called the oracle.
For MT training, we use the pseudotext and translations of 50 calls, and we filter out stopwords in the translations with NLTK BIBREF15 . Since UTD is better at matching patterns from the same speaker (§ SECREF8 ), we created two types of 90/10% train/test split: at the call level and at the utterance level. For the latter, 90% of the utterances are randomly chosen for the training set (independent of which call they occur in), and the rest go in the test set. Hence at the utterance level, but not the call level, some speakers are included in both training and test data. Although the utterance-level split is optimistic, it allows us to investigate how multiple speakers affect system performance. In either case, the oracle has about 38k Spanish tokens to train on.
Although we did not have access to a low-resource dataset, there is a corpus of noisy multi-speaker speech that simulates many of the conditions we expect to find in our motivating applications: the CALLHOME Spanish–English speech translation dataset (LDC2014T23; Post el al., 2013).
Answer: The CALLHOME Spanish–English speech translation dataset.
Question: Classify each of the following as having 2 wheels, 4 wheels, or neither: Car, Tricycle, Bicycle, Motorcycle, Grocery Cart
Answer: Car: 4 Wheels
Tricycle: Neither
Bicycle: 2 Wheels
Motorcycle: 2 Wheels
Grocery Cart: 4 Wheels
Question: What are the two types of approaches for question classification？
Evidence: Over the years, a handful of QA systems have gained popularity around the world. One of the oldest QA system is BASEBALL (created on 1961) BIBREF4 which answers question related to baseball league in America for a particular season. LUNAR BIBREF5 system answers questions about soil samples taken from Apollo lunar exploration. Some of the most popular QA Systems are IBM Watson, Apple Siri and Wolfram Alpha. Examples of some QA systems based on different languages are: Zhang Yu Chinese question classification BIBREF6 based on Incremental Modified Bayes, Arabic QA system (AQAS) BIBREF7 by F. A. Mohammed, K. Nasser, & H. M. Harb and Syntactic open domain Arabic QA system for factoid questions BIBREF8 by Fareed et al. QA systems have been built on different analysis methods such as morphological analysis BIBREF9, syntactical analysis BIBREF10, semantic analysis BIBREF11 and expected answer Type analysis BIBREF12.
Researches on question classification, question taxonomies and QA system have been undertaken in recent years. There are two types of approaches for question classification according to Banerjee et al in BIBREF13 - by rules and by machine learning approach. Rule based approaches use some hard coded grammar rules to map the question to an appropriate answer type BIBREF14 BIBREF15. Machine Learning based approaches have been used by Zhang et al and Md. Aminul Islam et al in BIBREF16 and BIBREF0. Many classifiers have been used in machine learning for QC such as Support Vector Machine (SVM) BIBREF16 BIBREF17, Support Vector Machines and Maximum Entropy Model BIBREF18, Naive Bayes (NB), Kernel Naive Bayes (KNB), Decision Tree (DT) and Rule Induction (RI) BIBREF13. In BIBREF0, they claimed to achieve average precision of 0.95562 for coarse class and 0.87646 for finer class using Stochastic Gradient Descent (SGD).
A Bengali QC System was built by Somnath Banerjee and Sivaji Bandyopadhyay BIBREF13 BIBREF19 BIBREF20. They proposed a two-layer taxonomy classification with 9 coarse-grained classes and 69 fine-grained classes. There are other research works BIBREF0 BIBREF21 in Bengali Language. A survey was performed on text QA techniques BIBREF22 where there was an analysis conducted in Bengali Language. Syed Mehedi Hasan Nirob et al achieved 88.62% accuracy by using 380 top frequent words as the feature in their work BIBREF17.
There are two types of approaches for question classification according to Banerjee et al in [1] - by rules and by machine learning approach.
Answer: By rules and by machine learning approach.
Question: What are some ways I can avoid financial failure?
Answer: Financial failure is the result of exceeding ones ability to service debts, and ones liabilities exceed their assets.  In general, you should live beneath your means, and decrease debts to a serviceable level.  To ensure financial success, it is recommended to save and invest to the point where you can comfortably live on 25x your yearly expenses at the typical yearly draw down of 4% to the point of the typical life expectancy of 30 years past retirement.
Question: Why do we feel lighter in weight in a swimming pool?
Answer: We feel lighter in a swimming pool because the water exerts an upward force or buoyant force on our body. This is because the weight of our body in water is equal to the apparent weight.
Question: By how much did their model outperform the baseline?
Evidence: First, we can observe that the final model “Ours with Mask and Ordered Triplets” outperforms the Baseline and Ablation models on all metrics in previously seen environments. The difference in performance is particularly evident for the Exact Match and Goal Match metrics, with our model increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively. These results suggest that providing the behavioral navigation graph to the model and allowing it to process this information as a knowledge base in an end-to-end fashion is beneficial.
Lastly, it is worth noting that our proposed model (last row of Table TABREF28 ) outperforms all other models in previously seen environments. In particular, we obtain over INLINEFORM0 increase in EM and GM between our model and the next best two models.
First, we can observe that the final model “Ours with Mask and Ordered Triplets” outperforms the Baseline and Ablation models on all metrics in previously seen environments. The difference in performance is particularly evident for the Exact Match and Goal Match metrics, with our model increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively. These results suggest that providing the behavioral navigation graph to the model and allowing it to process this information as a knowledge base in an end-to-end fashion is beneficial.
Lastly, it is worth noting that our proposed model (last row of Table TABREF28 ) outperforms all other models in previously seen environments. In particular, we obtain over INLINEFORM0 increase in EM and GM between our model and the next best two models.
Answer: The answers are shown as follows:
* increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively
* over INLINEFORM0 increase in EM and GM between our model and the next best two models
Question: Give me a list of 1980s computer games.
Answer: Here is a list of some computer games from the 1980s: Galaga, Donkey Kong, Pac-Man, Frogger, Tetris and SimCity.
Question: What is an atmospheric river?
Answer: Atmospheric rivers are relatively long, narrow regions in the atmosphere – like rivers in the sky – that transport most of the water vapor outside of the tropics. These columns of vapor move with the weather, carrying an amount of water vapor roughly equivalent to the average flow of water at the mouth of the Mississippi River. When the atmospheric rivers make landfall, they often release this water vapor in the form of rain or snow.
Question: Where do sloths live?
Answer: Sloths are found throughout Central America and northern South America, including parts of Brazil and Peru. They live high in the trees of tropical rainforests, where they spend most of their time curled up or hanging upside down from branches.
Question: Do they test their approach on a dataset without incomplete data?
Evidence: The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here, we apply gtts , a Google Text-to-Speech python library, and macsay , a terminal command available in Mac OS as say. The second step consists of applying an STT module to the obtained audio files in order to obtain text containing STT errors. The STT module used here was witai , freely available and maintained by Wit.ai. The mentioned TTS and STT modules were chosen according to code availability and whether it's freely available or has high daily usage limitations.
The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora.
Answer: No.
Question: write a scene between two actors discussing movie Inception
Answer: Actress 1: "Oh my god, have you seen Inception?"

Actor 2: "Yes! That movie blew my mind. What did you think of it?"

Actress 1: "Honestly, I was so confused for most of it. But it was so interesting, I couldn't look away. I had to know what was going on."

Actor 2: "That's exactly how I felt. It's one of those movies that you have to pay attention to every detail, or you'll get lost."

Actress 1: "Absolutely. And the way they played with time and reality was so cool. I don't think I've ever seen a movie like that before."

Actor 2: "Yeah, the concept of entering someone's dreams to implant an idea was mind-boggling. And the way they executed it was so well done."

Actress 1: "It was. And the cast was amazing too. Leonardo DiCaprio was incredible as usual, and I loved seeing Ellen Page in a different type of role."

Actor 2: "Definitely. And the action scenes were so intense. The zero-gravity hallway fight was insane."

Actress 1: "Oh my god, yes. I was on the edge of my seat for that one. And the ending...I still can't decide what it means."

Actor 2: "I know, right? It's one of those movies that leaves you with so many questions. But that's part of what makes it so great."

Actress 1: "Exactly. I love when movies challenge me to think and leave me wanting more. Inception definitely did that."

Actor 2: "Agreed. It's definitely one of my all-time favorites. I could watch it a hundred times and still discover something new."

Actress 1: "Same here. It's just one of those movies that sticks with you long after it's over."
Question: Which sub-Saharan colony was the first to gain independence?
Evidence: Imperial rule by Europeans would continue until after the conclusion of World War II, when almost all remaining colonial territories gradually obtained formal independence. Independence movements in Africa gained momentum following World War II, which left the major European powers weakened. In 1951, Libya, a former Italian colony, gained independence. In 1956, Tunisia and Morocco won their independence from France. Ghana followed suit the next year (March 1957), becoming the first of the sub-Saharan colonies to be granted independence. Most of the rest of the continent became independent over the next decade.
Answer: Ghana was the first sub-Saharan colony to gain independence.
Question: Name some popular video games exclusive to Sony’s Playstation game consoles.
Answer: Some of the most popular games that are exclusive to Sony Playstation consoles include the Uncharted series, the Gran Turismo series, and the God of War series. Each series of games have sold tens of millions of copies.
Question: Tell me about the history of how the NBA started?
Evidence: The Basketball Association of America was founded in 1946 by owners of the major ice hockey arenas in the Northeastern and Midwestern United States and Canada. On November 1, 1946, in Toronto, Ontario, Canada, the Toronto Huskies hosted the New York Knickerbockers at Maple Leaf Gardens, in a game the NBA now refers to as the first game played in NBA history. The first basket was made by Ossie Schectman of the Knickerbockers. Although there had been earlier attempts at professional basketball leagues, including the American Basketball League (ABL) and the NBL, the BAA was the first league to attempt to play primarily in large arenas in major cities. During its early years, the quality of play in the BAA was not significantly better than in competing leagues or among leading independent clubs such as the Harlem Globetrotters. For instance, the 1948 ABL finalist Baltimore Bullets moved to the BAA and won that league's 1948 title, and the 1948 NBL champion Minneapolis Lakers won the 1949 BAA title. Prior to the 1948–49 season, however, NBL teams from Fort Wayne, Indianapolis, Minneapolis, and Rochester jumped to the BAA, which established the BAA as the league of choice for collegians looking to turn professional.

On August 3, 1949, the remaining NBL teams–Syracuse, Anderson, Tri-Cities, Sheboygan, Denver, and Waterloo–merged into the BAA. In deference to the merger and to avoid possible legal complications, the league name was changed to the present National Basketball Association, even though the merged league retained the BAA's governing body, including Maurice Podoloff as president. To this day, the NBA claims the BAA's history as its own. It now reckons the arrival of the NBL teams as an expansion, not a merger, and does not recognize NBL records and statistics.
Answer: The league was founded in New York City on June 6, 1946, as the Basketball Association of America (BAA). It changed its name to the National Basketball Association on August 3, 1949, after merging with the competing National Basketball League..
Question: Is Daft Punk still together?
Answer: No. Daft Punk officially broke up in 2021.
Question: Classify these alcoholic beverages as bourbon, wine, or beer: Budweiser, Blanton's, Devil's Backbone, Chardonnay, Pinot Noir, Yellowstone, Elijah Craig, Pinot Grigio, Yeungling, Sauvignon Blanc, Cabernet Sauvignon, Samuel Adams, Sierra Nevada, Baker's, Basil Hayden, Eagle Rare, Merlot, Jim Beam, Coors Light, Jack Daniels, Heineken, Miller Lite
Answer: Bourbons: Blanton's, Yellowstone, Elijah Craig, Baker's, Basil Hayden, Eagle Rare, Jim Beam, Jack Daniels. Wine: Chardonnay, Pinor Noir, Pinot Grigio, Sauvignon Blanc, Merlot. Beer: Budweiser, Devil's Backbone, Yeungling, Samuel Adams, Sierra Nevada, Coors Light, Heineken, Miller Lite
Question: What is Occam's razor?
Answer: Occam’s razor is a principle of theory construction or evaluation according to which, other things equal, explanations that posit fewer entities, or fewer kinds of entities, are to be preferred to explanations that posit more. It is sometimes misleadingly characterized as a general recommendation of simpler explanations over more complex ones.
Question: How long can you store cooked food in refrigerators?
Answer: A common misconception is the assumption that food "loses nutrients" when it's refrigerated. That is not as true as you think it is. It is always good to ask 2 follow up questions
What nutrients exactly? And by how much?
It turns out that water soluble vitamins are the most unstable and easily lost nutrients, but fun fact: most of the loss happens during cooking, not refrigeration. It is heat that destroys vitamins, not cold. In fact, in an airtight container, most cooked food will last 2-3 days at the minimum and up to a week in many cases. In the freezer, food will last up to 6 months (assuming no power cuts). All biological activity slows down with temperature.

There are a few exceptions - plain cooked/steamed rice can sometimes be infected by a bacteria that doesn't mind low temperatures, so it's best to consume it within 1-2 days.

Bonus point: Indian food, is uniquely fridge friendly because it tends to be spicy, salty and sour - these are three conditions microbes absolutely hate.
Question: Without quoting from the text, can you summarise from wikipedia who Denis-Benjamin Viger was?
Evidence: Denis-Benjamin Viger (French pronunciation: ​[dəni bɛ̃ʒamɛ̃ viʒe]; August 19, 1774 – February 13, 1861) was a 19th-century politician, lawyer, businessman in Lower Canada. He was a leader in the Patriote movement.

Viger was part of the militia in the early 19th century and then a captain in the War of 1812. He retired from the militia in 1824 with the rank of major.
Answer: Denis-Benjamin Viger was a Canadian 19th century politician, lawyer and businessman who lead the Patriote movement.
Question: 5 ways on prem is better than cloud
Answer: Will have absolute control 
Will have no external factors
It support safety requirements, and have the tools to ensure first-class safety
Full control over the data since it is stored locally
System can be customised according to your business
Question: Classify each as National Park in Utah or Arizona: Zion National Park, Bryce Canyon, Grand Canyon, Saguaro National Park
Answer: National Parks in Utah: Zion National Park, Bryce Canyon
National Parks in Arizona: Grand Canyon, Saguaro National Park
Question: What are toe beans?
Answer: Toe beans is a colloquial term originating on the Internet and is used to refer to the leathers of a cat (its toes and paw pad). It can also refer to a dog's paws but the term was popularized in the context of cats.
Question: Do they use skipgram version of word2vec?
Evidence: In this paper we focus on the skipgram approach with random negative examples proposed in BIBREF0 . This has been found to yield the best results among the proposed variants on a variety of semantic tests of the resulting vectors BIBREF7 , BIBREF0 . Given a corpus consisting of a sequence of sentences INLINEFORM0 each comprising a sequence of words INLINEFORM1 , the objective is to maximize the log likelihood: DISPLAYFORM0
In this paper we focus on the skipgram approach with random negative examples proposed in BIBREF0 . 
Answer: Yes.
Question: Given a reference text about phrynobatrachus tokba, what are common names for it?
Evidence: Phrynobatrachus tokba is a species of frog in the family Phrynobatrachidae. It is found in West Africa from Ghana westward to Côte d'Ivoire, Liberia, Guinea, Sierra Leone, and Guinea-Bissau. Common names Tokba river frog, Tokba puddle frog, and—when referring to the formerly recognized Phrynobatrachus alticola, forest river frog—have been proposed for it.
Answer: Common names for Phrynobatrachus tokba are Tokba river frog, Tokba puddle frog, and forest river frog.
Question: What are some well known movies with Leonardo DiCaprio?
Answer: Well known movies with Leonard DiCaprio are Inception, Titanic, The Wolf of Wall Street, and Shutter Island
Question: Given a polynomial x^2 + 2x + 1, what is x?
Answer: x is equal to -1
Question: Does the joint model proposed by authors outperform the visual-only model in all cases?
Evidence: We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content. We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document. Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv. These results underline the feasibility of assessing document quality via visual features, and the complementarity of visual and textual document representations for quality assessment.
Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv.
Answer: Yes, it does.