DataHammer/scimrc huggingface dataset:
Question: What are the machine learning-based classifiers that are compared in this paper?
Evidence: Question classification (QC) deals with question analysis and question labeling based on the expected answer type. The goal of QC is to assign classes accurately to the questions based on expected answer. In modern system, there are two types of questions BIBREF0. One is Factoid question which is about providing concise facts and another one is Complex question that has a presupposition which is complex. Question Answering (QA) System is an integral part of our daily life because of the high amount of usage of Internet for information acquisition. In recent years, most of the research works related to QA are based on English language such as IBM Watson, Wolfram Alpha. Bengali speakers often fall in difficulty while communicating in English BIBREF1.
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers. Bengali questions have flexible inquiring ways, so there are many difficulties associated with Bengali QC BIBREF0. As there is no rich corpus of questions in Bengali Language available, collecting questions is an additional challenge. Different difficulties in building a QA System are mentioned in the literature BIBREF2 BIBREF3. The first work on a machine learning based approach towards Bengali question classification is presented in BIBREF0 that employ the Stochastic Gradient Descent (SGD).
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.
Answer: Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)
Question: Can you tell me about Rugby Union?
Answer: Rugby Union is a 15 player a side game played around the world. The sport was created during the 1800s in England when, during a game of soccer, a player picked up the ball and starting running with it in his hands. Rugby is most popular in commonwealth nations however has been gaining popularity in other countries such as USA and Japan. The game is played in two 40 minute halves with the winner of the game ending up with the most points. Points can be earned by grounding the ball over the try line in the other teams territory or by kicking the ball through the posts on either end of the field. The game is notorious for its aggression and tackling which can result in injuries as the players don't wear helmets or body armour like in other sports.
Question: Identify the bird from the list: Hoot, Coot, Foot
Answer: Coot
Question: What is the best string tension for a tennis racket?
Answer: The best string tension for a tennis racket will depend on the racket, the type of strings, and the player. Generally speaking, a higher tension will offer more control, while a lower tension will offer more power. Strings such as polyester, which are common among intermediate and advanced players, are stiffer and usually play better at a lower tension, around 50 lbs or 22 kg. Strings like synthetic gut, which are common among beginners and juniors, play better at 55 lbs or more. This will vary based on the racket, which can also offer more of its own power or control.
Question: What high school did Tyrone Brazelton go to?
Evidence: Born in Chicago, Illinois, Brazelton initially played basketball for the Rich East High School, but transferred to the rival Rich Central High School during his senior year. He led the latter team to a championship in the Big Dipper tournament and was named as the most valuable player of the tournament.
Answer: Tyrone Brazelton attended Rich East High School and then transferred to Rich Central High School for his senior year.
Question: What band sings the song "My Own Worst Enemy"?
Evidence: a song by the American rock band Lit. It was commercially released in June 1999 as the lead single from Lit's second album, A Place in the Sun, which was also released that year. The song was only moderately successful at first, reaching number 17 on the Modern Rock Tracks chart on February 27, 1999. It later achieved mainstream success, peaking at number 51 on the Billboard Hot 100 chart and number one on the Modern Rock Tracks (also known as Alternative Songs) chart.
Answer: Lit
Question: ways to generate electricity
Answer: Use solar panel to harvest solar energy
use a dynamo and mechanical force such as animal.
Use wind tubine to harvest wind energy
Use salt water to create a battery
use steam to power turbine
Question: Cyrus Mistry was the  6th Chairman of Tata Sons between between which years
Answer: 2012 and 2016
Question: Where was Samuel Loew born?
Evidence: Samuel ben Nathan Ha-Levi Loew (Kelin or Kolin) (also "Lōw" or "Löw", Hebrew: שמואל בן נתן נטע הלוי קעלין; c. 1720–1806) was a Talmudist and Halakhist, best known for his work Machatzit HaShekel (Ashkenazi pronunciation, Machatzis HaShekel).

The son of Nathan Naṭe ha-Levi, he was born at Kolín, Bohemia. For nearly 60 years he headed over a yeshiva at Boskovice (Boskowitz), Moravia, where he died on May 20, 1806. He had the title Av Beis Din of Boskowitz.

Samuel had 3 sons and 3 daughters: Benjamin Ze'ev Ha-Levi (Wolf) Boskowitz (1746-1818), Jacob (c 1750-1833), Jehuda (c 1751-1800), Sara (Hinda) married Markus (Mordechai Lob) Wassertrilling of Boskovice, Esther married Bernhard Biach. His son Wolf Boskowitz delivered the sermon at his funeral. His descendant in the 5th generation, Dr. Max Anton Löw, a convert to Roman Catholicism, was the attorney of the anti-Semite Francis Deckert.[3
Answer: Samuel Loew was born at Kolin, Bohemia.
Question: Based on the text, please give a bulleted list of Marc Andreessen’s accomplishments
Evidence: Marc Lowell Andreessen (/ænˈdriːsən/ ann-DREE-sən; born July 9, 1971) is an American entrepreneur, venture capital investor, and software engineer. He is the co-author of Mosaic, the first widely used web browser with a graphical user interface; co-founder of Netscape; and co-founder and general partner of Silicon Valley venture capital firm Andreessen Horowitz. He co-founded and later sold the software company Opsware to Hewlett-Packard. Andreessen is also a co-founder of Ning, a company that provides a platform for social networking websites. He sits on the board of directors of Meta Platforms. Andreessen was one of six inductees in the World Wide Web Hall of Fame announced at the First International Conference on the World-Wide Web in 1994. As of February 2023, his net-worth is estimated at $1.7 billion by Forbes.
Answer: Co-author of the first widely used web browser with a graphical user interface (Mosaic)
Co-founder of Netscape
Co-founder of Andreessen Horowitz
Co-founder of Opsware
Co-founder of Ning
Board member of Meta Platforms
Inductee in the World Wide Web Hall of Fame
Question: In order to solve the difficulties, what methods does the author propose?
Evidence: Emotion detection has long been a topic of interest to scholars in natural language processing (NLP) domain. Researchers aim to recognize the emotion behind the text and distribute similar ones into the same group. Establishing an emotion classifier can not only understand each user's feeling but also be extended to various application, for example, the motivation behind a user's interests BIBREF0. Based on releasing of large text corpus on social media and the emotion categories proposed by BIBREF1, BIBREF2, numerous models have provided and achieved fabulous precision so far. For example, DeepMoji BIBREF3 which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER BIBREF4 learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.
As methods become mature, text-based emotion detecting applications can be extended from a single utterance to a dialogue contributed by a series of utterances. Table TABREF2 illustrates the difference between single utterance and dialogue emotion recognition. The same utterances in Table TABREF2, even the same person said the same sentence, the emotion it convey may be various, which may depend on different background of the conversation, tone of speaking or personality. Therefore, for emotion detection, the information from preceding utterances in a conversation is relatively critical.
In SocialNLP 2019 EmotionX, the challenge is to recognize emotions for all utterances in EmotionLines dataset, a dataset consists of dialogues. According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT) BIBREF5, FriendsBERT and ChatBERT. In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning.
The main goal of the present work is to predict the emotion of utterance within the dialogue. Following are four major difficulties we concern about:
The emotion of the utterances depends not only on the text but also on the interaction happened earlier.
The source of the two datasets are different. Friends is speech-based dialogues and EmotionPush is chat-based dialogues. It makes datasets possess different characteristics.
There are only $1,000$ dialogues in both training datasets which are not large enough for the stability of training a complex neural-based model.
The prediction targets (emotion labels) are highly unbalanced.
The proposed approach is summarized in Figure FIGREF3, which aims to overcome these challenges. The framework could be separated into three steps and described as follow:
In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning.****The proposed approach is summarized in Figure 1, which aims to overcome these challenges. The framework could be separated into three steps.
Answer: Causal utterance modeling, model pre-training, and fine-turning.
Question: How many people initially formed the band Raeth?
Evidence: Raeth was initially formed in 2006 by four students from Karachi, Pakistan — Wajhi Farooki (vocals), Mustafa Asad (bass), and Hasan Farabi (lead guitar). The band was launched in 2006 by Fire Records at a local beach club in DHA.
Answer: four
Question: On a backcountry backpacking trip where we expect to cross waterways, categorize each of the following as either 'useful', 'not useful', 'dangerous' or 'neither'.  A chinchilla, a compass, granola, dry bag, seventeen oars, thermite, ten tons of uranium ore.
Answer: Useful
compass
granola
dry bag
seventeen oars

Not Useful

Dangerous
chinchilla
thermite
ten tons of uranium ore

None
Question: What are multilingual models that were outperformed in performed experiment?
Evidence: Baselines We thoroughly compare our approaches to the following baselines:
Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target parallel data.
Multilingual: A single, shared NMT model for multiple translation directions BIBREF6.
Many-to-many: Trained for all possible directions among source, target, and pivot languages.
Many-to-one: Trained for only the directions to target language, i.e., source$\rightarrow $target and pivot$\rightarrow $target, which tends to work better than many-to-many systems BIBREF27.
Baselines We thoroughly compare our approaches to the following baselines:

Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target parallel data.

Multilingual: A single, shared NMT model for multiple translation directions BIBREF6.

Many-to-many: Trained for all possible directions among source, target, and pivot languages.

Many-to-one: Trained for only the directions to target language, i.e., source$\rightarrow $target and pivot$\rightarrow $target, which tends to work better than many-to-many systems BIBREF27.
Answer: The answers are shown as follows:
* Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target
* Multilingual: A single, shared NMT model for multiple translation directions
* Many-to-many: Trained for all possible directions among source, target, and pivot languages
* Many-to-one: Trained for only the directions to target language
Question: How large is the dataset?
Evidence: FLOAT SELECTED: Table 1: Statistical summary of multilingual corpora across English, Italian, Polish, Portuguese and Spanish. We present number of users (Users), documents (Docs), and average tokens per document (Tokens) in the corpus, plus the label distribution (HS Ratio, percent of documents labeled positive for hate speech).
FLOAT SELECTED: Table 1: Statistical summary of multilingual corpora across English, Italian, Polish, Portuguese and Spanish. We present number of users (Users), documents (Docs), and average tokens per document (Tokens) in the corpus, plus the label distribution (HS Ratio, percent of documents labeled positive for hate speech).
Answer: over 104k documents
Question: What does Midnight Sun mean?
Evidence: Midnight sun is a natural phenomenon that occurs in the summer months in places north of the Arctic Circle or south of the Antarctic Circle, when the Sun remains visible at the local midnight. When midnight sun is seen in the Arctic, the Sun appears to move from left to right, but in Antarctica the equivalent apparent motion is from right to left. This occurs at latitudes from 65°44' to 90° north or south, and does not stop exactly at the Arctic Circle or the Antarctic Circle, due to refraction.
Answer: Midnight sun is a natural phenomenon that occurs in the summer months in places north of the Arctic Circle or south of the Antarctic Circle, when the Sun remains visible at the local midnight.
Question: How is the order of the NFL draft determined?
Answer: The order of the NFL draft is determined by the record of each team during the previous season. The teams with the worst records choose before the teams with better records.
Question: In the series, A Song of Ice and Fire, who is the founder of House Arryn?
Answer: Artys I Arryn
Question: From the passage find the major production areas where Avocado is cultivated. Display the results in comma separated format.
Evidence: The avocado (Persea americana) is a medium-sized, evergreen tree in the laurel family (Lauraceae). It is native to the Americas and was first domesticated by Mesoamerican tribes more than 5,000 years ago. Then as now it was prized for its large and unusually oily fruit. The tree likely originated in the highlands bridging south-central Mexico and Guatemala. Its fruit, sometimes also referred to as an alligator or avocado pear, is botanically a large berry containing a single large seed. Avocado trees are partly self-pollinating, and are often propagated through grafting to maintain consistent fruit output. Avocados are presently cultivated in the tropical and Mediterranean climates of many countries. Mexico is the world's leading producer of avocados as of 2020, supplying nearly 30% of the global harvest in that year.

The fruit of domestic varieties have smooth, buttery, golden-green flesh when ripe. Depending on the cultivar, avocados have green, brown, purplish, or black skin, and may be pear-shaped, egg-shaped, or spherical. For commercial purposes the fruits are picked while unripe and ripened after harvesting. The nutrient density and extremely high fat content of avocado flesh are useful to a variety of cuisines and are often eaten to enrich vegetarian diets.

In major production regions like Chile, Mexico and California the water demands of avocado farms place strain on local sources. Avocado production is also implicated in other externalities, including deforestation and human rights concerns associated with the partial control of their production in Mexico by organized crime. Global warming is expected to result in significant changes to the suitable growing zones for avocados, and place additional pressures on the locales in which they are produced due to heat waves and drought.
Answer: Chile, Mexico, California
Question: Why does the James web Telescope rely so heavily on infrared?
Evidence: Very Early and distant objects visibility shifts to infrared.
Answer: The design emphasizes the near to mid-infrared for several reasons:

high-redshift (very early and distant) objects have their visible emissions shifted into the infrared, and therefore their light can be observed today only via infrared astronomy;
infrared light passes more easily through dust clouds than visible light;
colder objects such as debris disks and planets emit most strongly in the infrared;
these infrared bands are difficult to study from the ground or by existing space telescopes such as Hubble.
Question: Imagine you met an alien last night and write about this in your diary
Answer: Dear Diary,
		  It was just past midnight when I suddenly heard a loud thump in the backyard yesterday. I mustered some courage to ensure the doors and windows are locked. But I saw something very bright, which made it difficult to stare at it, and then suddenly the lights faded and I saw an alien. I did pinch myself twice to ensure this wasn’t a dream. But then I realised it was not a dream when it was trying to figure out ways to get water from a pipe fixed to a tap and some of it splashed over me. And without my realisation, I was screaming at the top of my voice. Soon my mom came and I was about to explain to her what happened but there was no trace of the alien. Everyone tried to convince me it was a dream, but then I saw my shirt was still a bit wet from the water that had splashed.

Regards,
XYZ
Question: Tell me which of these countries have won a Fifa World Cup Final: Argentina, Mexico, USA, France, China
Answer: From the options provided, only Argentina and France have won a Fifa World Cup Final, Mexico, USA and China have not.
Question: What is a "love hotel"?
Evidence: A love hotel is a type of short-stay hotel found around the world operated primarily for the purpose of allowing guests privacy for sexual activities. The name originates from "Hotel Love" in Osaka, which was built in 1968 and had a rotating sign. Although love hotels exist all over the world, the term "love hotel" is often used to refer specifically to those located within Japan.
Answer: A love hotel is a type of short-stay hotel found around the world operated primarily for the purpose of allowing guests privacy for sexual activities.
Question: Who wrote the book that the TV show Shantaram is based on?
Evidence: Some types of oysters are commonly consumed (cooked or raw), and in some locales are regarded as a delicacy. Some types of pearl oysters are harvested for the pearl produced within the mantle. Windowpane oysters are harvested for their translucent shells, which are used to make various kinds of decorative objects.
Answer: Gregory David Roberts wrote the novel Shantaram.
Question: What new car should I buy for my family?
Answer: SUVs are a popular choice for family cars as they often provide all-wheel drive, up to three rows of seating, and larger cargo capacity.
Question: Classify each of the following as a baseball or soccer term: pitcher, goalie, inning, goal, yellow card, red card, home run, catcher
Answer: Pitcher: Baseball
Goalie: Soccer
Inning: Baseball
Goal: Soccer
Yellow Card: Soccer
Red Card: Soccer
Home Run: Baseball
Catcher: Baseball
Question: Name some of the top soccer players
Answer: Pele
Messi
Ronaldo
Ronaldinho
Zinedine Zidane
Mbappe
Question: Give me the list of philosophers of science from before the 19th century
Answer: Before the 19th century, the philosophers of science were Roger Bacon, Sir Francis Bacon and Galileo Galilei.
Question: How did the United States win the Cold War?
Answer: A number of factors contributed to the United States defeating the Union of Soviet Socialist Republics (USSR) in the Cold War and becoming the world's most powerful state. A common view is that the U.S. won the Cold War because its capitalist economic system, characterized by free markets and the recognition of private property rights, was able to outperform the USSR's centrally-planned economy. The United States could therefore afford to spend more on its military than the USSR and won the nuclear arms race, which contributed to the internal collapse of the Soviet economy and the subsequent breakup of the Soviet Union into smaller, individual states with democratic elections. Additionally, though the USSR and the United States never officially engaged in direct military conflict with one another during the Cold War, various factions supported by the United States and its Western allies were able to successfully defeat Soviet-backed factions in proxy wars that took place all across the globe (with the notable exception of Vietnam). These defeats gradually limited the USSR's ability to project its power on the world stage, and helped the United States build and strengthen key alliances, including the North Atlantic Treaty Organization. Finally, the United States carried out effective intelligence, counter-intelligence, disinformation and propaganda campaigns that undermined the faith the USSR's population had in its government and buttressed support for the U.S. government's efforts domestically. By 1991, the Soviet Union had fallen and the U.S. had achieved global hegemony.
Question: The comparison of several metrics for evaluating OOD in section 5.1.3 is a bit confusing. What “metric” is used to evaluate the performance of the metrics so the author can say that one is better than the others?
Evidence: Table 8: OOD detection performance considering Maximum Probability Score (MPS), DesmpterShafer/Energy (DS) and Entropy (H) as uncertainty metrics.****For instance, in Table 9, consider DNN-SRN tested on SVHN: with the LRP stabilisation and ∈ = 1e − 08, γ = 0.95 the the belief bc can achieve an AUROC of 97.84% and an AUPR of 99.06%, which are both superior to the AUROC of 96.12% and AUPR of 98.10% reported in Table 8 with the DS metric for the same method.
Answer: We tried several existing metrics and in **cases** in which a metric was **strictly superior **(e.g. for mixup, the entropy is always better) we reported that one, in the other cases there are **no significant differences** on average with very minor differences. There was no solid justification to prefer one over the other in these cases as the differences were insignificant. We have provided performances of all these metrics in **Tables 8 and 9  in Appendix E**. In the same Appendix, we also perform an important analysis showing that **GMM-based metrics are highly unstable** in many cases due to numerical instability. Therefore, approaches using GMM-based metrics should also provide large scale experiments in order to show the efficacy.