DataHammer/scimrc huggingface dataset:
Question: From the passage note down the awards won by Babaluku. List the results in comma separated format.
Evidence: Babaluku, born Silas Babaluku Balabyekkubo, is a Ugandan rapper, musician, producer, community youth activist and social entrepreneur who raps in Luganda. He is a member of the Bataka Squad. He is the founder of "the Bavubuka Foundation which equips the youth with leadership skills". He is one of the pioneers of "Lugaflow" which is rap music in Luganda. He was featured in a 2008 documentary "Diamonds in the Rough: A Ugandan Hip-hop Revolution" that covered his journey from his crew’s early days of performing in Uganda to performing in at festivals in the United States of America.Early life and educationBababuluku was born in Kampala, Uganda in 1979. He is the first born of eight children to the late Pastor Deo Balabaekubo and Mrs Christine Balabyekubo. He went to Emen Memorial and Molly and Paul Primary schools in Makindye and eventually left for Canada when he was 12 years old. He is a graduate of sound engineering from British Columbia academy in Vancouver.MusicBabaluku started his music career in 1994. He is one of the pioneers of Lugaflow, which is rap in Luganda. He has a number of awards including a Pearl of Africa Award for Best Hip Hop Single and Buzz Teen Awards for Best Hip Hop Artist. The documentary "Diamonds in the Rough" in which he featured, won the Audience Choice Award at Dances With Films Festival and Best Feature Documentary at Peace on Earth Film Festival. In 2010 Babaluku was honored by the Ugandan Cultural Association for his Excellence and also chosen as Pioneer of the Year by the Words Beats & Life Remix Teach-In Awards.
Answer: Pearl of Africa Award, Buzz Teen Awards, Audience Choice Award at Dances With Films Festival,Best Feature Documentary at Peace on Earth Film Festival, Honored by the Ugandan Cultural Association for his Excellence, Chosen as Pioneer of the Year by the Words Beats & Life Remix Teach-In Awards
Question: What technique do the authors develop in this work?
Evidence: Pre-trained models BIBREF0, BIBREF1 have received much of attention recently thanks to their impressive results in many down stream NLP tasks. Additionally, multilingual pre-trained models enable many NLP applications for other languages via zero-short cross-lingual transfer. Zero-shot cross-lingual transfer has shown promising results for rapidly building applications for low resource languages. BIBREF2 show the potential of multilingual-BERT BIBREF0 in zero-shot transfer for a large number of languages from different language families on five NLP tasks, namely, natural language inference, document classification, named entity recognition, part-of-speech tagging, and dependency parsing.
Although multilingual models are an important ingredient for enhancing language technology in many languages, recent research on improving pre-trained models puts much emphasis on English BIBREF3, BIBREF4, BIBREF5. The current state of affairs makes it difficult to translate advancements in pre-training from English to non-English languages. To our best knowledge, there are only three available multilingual pre-trained models to date: (1) the multilingual-BERT (mBERT) that supports 104 languages, (2) cross-lingual language model BIBREF6 that supports 100 languages, and (3) Language Agnostic SEntence Representations BIBREF7 that supports 93 languages. Among the three models, LASER is based on neural machine translation approach and strictly requires parallel data to train.
Do multilingual models always need to be trained from scratch? Can we transfer linguistic knowledge learned by English pre-trained models to other languages? In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM. We apply our approach to autoencoding language models with masked language model objective and show the advantage of the proposed approach in zero-shot transfer. Our main contributions in this work are:
We propose a fast adaptation method for obtaining a bilingual BERT$_{\textsc {base}}$ of English and a target language within a day using one Tesla V100 16GB GPU.
We evaluate our bilingual LMs for six languages on two zero-shot cross-lingual transfer tasks, namely natural language inference BIBREF9 and universal dependency parsing. We show that our models offer competitive performance or even better that mBERT.
We illustrate that our bilingual LMs can serve as an excellent feature extractor in supervised dependency parsing task.
In this work, we develop a technique to rapidly transfer an existing pretrained model from English to other languages.
Answer: A technique to rapidly transfer an existing pretrained model from English to other languages.
Question: What percentage fewer errors did professional translations make?
Evidence: FLOAT SELECTED: Table 5: Classification of errors in machine translation MT1 and two professional human translation outputs HA and HB. Errors represent the number of sentences (out of N = 150) that contain at least one error of the respective type. We also report the number of sentences that contain at least one error of any category (Any), and the total number of error categories present in all sentences (Total). Statistical significance is assessed with Fisher’s exact test (two-tailed) for each pair of translation outputs.
To achieve a finer-grained understanding of what errors the evaluated translations exhibit, we perform a categorisation of 150 randomly sampled sentences based on the classification used by BIBREF3. We expand the classification with a Context category, which we use to mark errors that are only apparent in larger context (e. g., regarding poor register choice, or coreference errors), and which do not clearly fit into one of the other categories. BIBREF3 perform this classification only for the machine-translated outputs, and thus the natural question of whether the mistakes that humans and computers make are qualitatively different is left unanswered. Our analysis was performed by one of the co-authors who is a bi-lingual native Chinese/English speaker. Sentences were shown in the context of the document, to make it easier to determine whether the translations were correct based on the context. The analysis was performed on one machine translation (MT$_1$) and two human translation outputs (H$_A$, H$_B$), using the same 150 sentences, but blinding their origin by randomising the order in which the documents were presented. We show the results of this analysis in Table TABREF32.
FLOAT SELECTED: Table 5: Classification of errors in machine translation MT1 and two professional human translation outputs HA and HB. Errors represent the number of sentences (out of N = 150) that contain at least one error of the respective type. We also report the number of sentences that contain at least one error of any category (Any), and the total number of error categories present in all sentences (Total). Statistical significance is assessed with Fisher’s exact test (two-tailed) for each pair of translation outputs.
To achieve a finer-grained understanding of what errors the evaluated translations exhibit, we perform a categorisation of 150 randomly sampled sentences based on the classification used by BIBREF3
 The analysis was performed on one machine translation (MT$_1$) and two human translation outputs (H$_A$, H$_B$), using the same 150 sentences, but blinding their origin by randomising the order in which the documents were presented. We show the results of this analysis in Table TABREF32.
Answer: 36%
Question: Wadi Al Banat's name means ?
Evidence: Wadi Al Banat's name means "valley of the girls" in Arabic, and was thought to have been named that because it was a popular play area for young girls
Answer: valley of the girls
Question: What is wheat?
Answer: Wheat starts off as a grass, and it is grown to collect its seeds.  The grain seeds from wheat are often used in cereal, biscuits, crackers, bread, chips, pizza, pasta and pastries.  Wheat is also given in its raw form to farm animals to help them grow and stay nourished.
Question: What tools Aloy can exploit to survive in the world? give me the category and examples in the format {category} - {examples}
Evidence: Horizon Zero Dawn is an action role-playing game played from a third-person view. Players take control of Aloy, a hunter who ventures through a post-apocalyptic land ruled by robotic creatures. Aloy can kill enemies in a variety of ways – shooting them with arrows, setting traps such as tripwires using the Tripcaster, using explosives, and a spear. Machine components, including electricity and the metal they are composed of, are vital to Aloy's survival; she can loot their remains for crafting resources. Ammo, resource satchels, pouches, quivers, resistance, antidotes, health potions, and traps can all be crafted. Weapons have modification slots for dealing more damage. Aloy wears a Focus, a small head piece that scans machines to determine their susceptibilities, identify their location, their level, and the nature of loot they will drop. One machine, the Stalker, can enable cloaking technology to avert the gaze of Aloy's Focus scan. Machines attack with defensive and offensive measures, and in either case react to a perceived threat by charging at it with brute force or projectiles. Exhibiting the behaviour of wild animals, some machines are inclined to move in herds, and others, with the ability to fly, do so in flocks. Unless hacked with the Override Tool, or sufficiently hit with corruption arrows, machines do not exert aggressive force against each other. Aloy also engages in battle with human enemies, such as bandits and the Eclipse cult.
Answer: weapons: arrows, tripwires using the Tripcaster, explosives, spear
resources: Ammo, resource satchels, pouches, quivers, resistance, antidotes, health potions, traps
technology: Focus, Override Tool
Question: What is the measure of electric potential?
Evidence: Electricity is the set of physical phenomena associated with the presence and motion of matter that has a property of electric charge. Electricity is related to magnetism, both being part of the phenomenon of electromagnetism, as described by Maxwell's equations. Various common phenomena are related to electricity, including lightning, static electricity, electric heating, electric discharges and many others.
The presence of either a positive or negative electric charge produces an electric field. The movement of electric charges is an electric current and produces a magnetic field. In most applications, a force acts on a charge with a magnitude given by Coulomb's law. Electric potential is typically measured in volts.
Electricity is at the heart of many modern technologies, being used for:
a. Electric power where electric current is used to energise equipment;
b. Electronics which deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes and integrated circuits, and associated passive interconnection technologies.
Electrical phenomena have been studied since antiquity, though progress in theoretical understanding remained slow until the 17th and 18th centuries. The theory of electromagnetism was developed in the 19th century, and by the end of that century electricity was being put to industrial and residential use by electrical engineers. The rapid expansion in electrical technology at this time transformed industry and society, becoming a driving force for the Second Industrial Revolution. Electricity's extraordinary versatility means it can be put to an almost limitless set of applications which include transport, heating, lighting, communications, and computation. Electrical power is now the backbone of modern industrial society.
Answer: Electric potential is typically measured in volts.
Question: Extract the various manufacturers and suppliers of the Tomahawk missile and separate them by comma
Evidence: The Tomahawk (/ˈtɒməhɔːk/) Land Attack Missile (TLAM) is a long-range, all-weather, jet-powered, subsonic cruise missile that is primarily used by the United States Navy and Royal Navy in ship and submarine-based land-attack operations.

Under contract from the U.S. Navy, the Tomahawk was designed at the APL/JHU in a project led by James Walker near Laurel, Maryland, and was first manufactured by General Dynamics in the 1970s. It was intended to fill the role of a medium- to long-range, low-altitude missile that could be launched from a naval surface warfare platform, and featured a modular design accommodating a wide variety of warhead, guidance, and range capabilities. At least six variants and multiple upgraded versions of the TLAM have been added since the original design was introduced, including air-, sub-, and ground-launched variants with conventional and nuclear armaments. In 1992–1994, McDonnell Douglas Corporation was the sole supplier of Tomahawk Missiles and produced Block II and Block III Tomahawk missiles and remanufactured many Tomahawks to Block III specifications. In 1994, Hughes outbid McDonnell Douglas Aerospace to become the sole supplier of Tomahawk missiles. By 2019, the only variants in service were non-nuclear, sea-launched variants manufactured by Raytheon. In 2016, the U.S. Department of Defense purchased 149 Tomahawk Block IV missiles for $202.3 million.
Answer: General Dynamics, McDonnell Douglas Corporation, Hughes, Raytheon
Question: What is the size of this dataset?
Evidence: Named entity recognition (NER) is a fundamental step in text mining system development to facilitate the COVID-19 studies. There is critical need for NER methods that can quickly adapt to all the COVID-19 related new types without much human effort for training data annotation. We created this CORD-19-NER dataset with comprehensive named entity annotation on the CORD-19 corpus (2020-03-13). This dataset covers 75 fine-grained named entity types. CORD-19-NER is automatically generated by combining the annotation results from four sources. In the following sections, we introduce the details of CORD-19-NER dataset construction. We also show some NER annotation results in this dataset.
The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). We first merge all the meta-data (all_sources_metadata_2020-03-13.csv) with their corresponding full-text papers. Then we create a tokenized corpus (CORD-19-corpus.json) for further NER annotations.
We created this CORD-19-NER dataset with comprehensive named entity annotation on the CORD-19 corpus (2020-03-13). 
The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). 
Answer: The answers are shown as follows:
* 29,500 documents
Question: From the passage identify the years in which major events happened in Icahn’s business career. Display the results in comma separated format.
Evidence: In 1961, Icahn began his career as a stockbroker for Dreyfus Corporation. In 1963, he became an options manager for Tessel, Patrick & Co. and then he moved to Gruntal & Co.

In 1968, with $150,000 of his own money and a $400,000 investment from his uncle, M. Elliot Schnall, Icahn bought a seat on the New York Stock Exchange and formed Icahn & Co., which focused on risk arbitrage and options trading.

In 1978, in his first takeover attempt, he took a controlling stake in Tappan and forced the sale of the company to Electrolux, making a profit of $2.7 million, or doubling his investment. In 1979, he acquired Bayswater Realty & Capital Corporation. In 1983, he acquired ACF Industries and in 1985 he sold those shares to Phillips Petroleum, making a $50 million profit.[failed verification]

In 1985, by pooling his funds with investor funds and funds borrowed from banks, Icahn acquired 50% of Trans World Airlines, and he completely acquired the company in a leveraged buyout in 1988. Icahn systematically sold TWA's assets to repay money he owed, which was described as "asset stripping" and made him known as a "corporate raider". In 1991, he sold TWA's London routes to American Airlines for $445 million. Icahn made a $469 million profit, and left TWA with $540 million in debt. He formed lowestfare.com to sell TWA tickets and acquired portions of Global Leisure Travel from Ramy El-Batrawi.

In October 1986, Icahn launched an unsuccessful $8 billion hostile takeover for 89% of U.S. Steel; the bid was dropped in January 1987. In 1991, he sold his stake in the company for $1 billion, making a $200 million profit.
Answer: 1961, 1963,1968, 1978, 1983, 1985, 1986, 1988, 1987, 1991
Question: List reputable Universities in San Diego
Answer: 1. San Diego State University 
2. University California San Diego
3. University of San Diego
4. San Marcos State University 
5. Point Loma Nazarene University
Question: Why do you want to keep customers happy?
Answer: You can keep customers happy by putting an extreme focus on them.  Your business should exist to serve the needs of your customer.  You could also call this customer obsession.  By focussing on them and keeping them happy you will reduce customer attrition and churn, and you will have a continuous source of revenue for years to come because these happy customers turn into repeat customers over and over.
Question: When was the last time aliens visited the earth?
Answer: There is no evidence that aliens have ever visited the earth. There have been many claims including UFO sightings, strange atmospheric patterns, ancient buildings showing characteristics outside of human capabilities at that time. But all of these have been refuted and there is no credible evidence of alien visitation.
Question: Who amongst these are from India: Virat Kohli, Roger Federer, Rafael Nadal, 
Novak Djokovic, Serena Williams, Andre Agassi, Sachin Tendulkar, Ricky Ponting, Anil Kumble, Harmanpreet Kaur, Mithali Raj, Sarah Taylor.
Answer: Virat Kohli, Sachin Tendulkar, Harmanpreet Kaur, Mithali Raj are players from India.
Question: who won the last soccer world cup ?
Answer: Argentina won the last world cup. Argentina won 3 world cups in 1978, 1986, 2022.
Question: What is SAS language?
Answer: The SAS language is a computer programming language used for statistical analysis, created by Anthony James Barr at North Carolina State University.
Question: Who was Elizabeth II?
Evidence: Elizabeth II (Elizabeth Alexandra Mary; 21 April 1926 – 8 September 2022) was Queen of the United Kingdom and other Commonwealth realms from 6 February 1952 until her death in 2022. She was queen regnant of 32 sovereign states during her lifetime and was head of state of 15 realms at the time of her death. Her reign of 70 years and 214 days was the longest of any British monarch and the longest verified reign of any female monarch in history.
Answer: Elizabeth II was the queen of the United Kingdom and the longest-reigning monarch.
Question: Tell me wether these are cities, or states? IL, chicago, Texas, Fremont, Washington, Washington DC, san jose, bay area
Answer: Washington DC, Chicago, Freemont, San jose are cities, and IL, Texas, Washington are US states. Bay area is neither a state nor a city. It is referring to the area in northern CA which consists of multiple cities.
Question: What is the baseline?
Evidence: Emotions have been used in many natural language processing tasks and they showed their efficiency BIBREF35. We aim at investigating their efficiency to detect false information. In addition to EIN, we created a model (Emotion-based Model) that uses emotional features only and compare it to two baselines. Our aim is to investigate if the emotional features independently can detect false news. The two baselines of this model are Majority Class baseline (MC) and the Random selection baseline (RAN).
 In addition to EIN, we created a model (Emotion-based Model) that uses emotional features only and compare it to two baselines. Our aim is to investigate if the emotional features independently can detect false news. The two baselines of this model are Majority Class baseline (MC) and the Random selection baseline (RAN).
Answer: The answers are shown as follows:
* Majority Class baseline (MC) 
* Random selection baseline (RAN)
Question: What does the author find out from this paper?
Evidence: We created the Equity Evaluation Corpus (EEC), which consists of 8,640 sentences specifically chosen to tease out gender and race biases in natural language processing systems. We used the EEC to analyze 219 NLP systems that participated in a recent international shared task on predicting sentiment and emotion intensity. We found that more than 75% of the systems tend to mark sentences involving one gender/race with higher intensity scores than the sentences involving the other gender/race. We found such biases to be more widely prevalent for race than for gender. We also found that the bias can be different depending on the particular affect dimension involved.
We found the score differences across genders and across races to be somewhat small on average ( INLINEFORM0 , which is INLINEFORM1 of the 0 to 1 score range). However, for some systems the score differences reached as high as 0.34 (34%). What impact a consistent bias, even with an average magnitude INLINEFORM2 , might have in downstream applications merits further investigation.
We plan to extend the EEC with sentences associated with country names, professions (e.g., doctors, police officers, janitors, teachers, etc.), fields of study (e.g., arts vs. sciences), as well as races (e.g., Asian, mixed, etc.) and genders (e.g., agender, androgyne, trans, queer, etc.) not included in the current study. We can then use the corpus to examine biases across each of those variables as well. We are also interested in exploring which systems (or what techniques) accentuate inappropriate biases in the data and which systems mitigate such biases. Finally, we are interested in exploring how the quality of sentiment analysis predictions varies when applied to text produced by different demographic groups, such as people of different races, genders, and ethnicities.
The Equity Evaluation Corpus and the proposed methodology to examine bias are not meant to be comprehensive. However, using several approaches and datasets such as the one proposed here can bring about a more thorough examination of inappropriate biases in modern machine learning systems.
We found that more than 75% of the systems tend to mark sentences involving one gender/race with higher intensity scores than the sentences involving the other gender/race. We found such biases to be more widely prevalent for race than for gender. We also found that the bias can be different depending on the particular affect dimension involved.
Answer: They found that more than 75% of the systems tend to mark sentences involving one gender/race with higher intensity scores than the sentences involving the other gender/race. They found such biases to be more widely prevalent for race than for gender. They also found that the bias can be different depending on the particular affect dimension involved.
Question: Classify which colors are shades or red or purple: Violet, burgundy, lavender, brick, lilac, plum, rust, orchid, maroon, ruby, cardinal, fire-engine
Answer: Red: Burgundy, brick, rust, maroon, ruby, cardinal, fire-engine
Purple: Violet, lavender, lilac, plum, orchid