DataHammer/scimrc huggingface dataset:
Question: Does the paper mention other works proposing methods to detect anglicisms in Spanish?
Evidence: In terms of automatic detection of anglicisms, previous approaches in different languages have mostly depended on resource lookup (lexicon or corpus frequencies), character n-grams and pattern matching. alex-2008-comparing combined lexicon lookup and a search engine module that used the web as a corpus to detect English inclusions in a corpus of German texts and compared her results with a maxent Markov model. furiassi2007retrieval explored corpora lookup and character n-grams to extract false anglicisms from a corpus of Italian newspapers. andersen2012semi used dictionary lookup, regular expressions and lexicon-derived frequencies of character n-grams to detect anglicism candidates in the Norwegian Newspaper Corpus (NNC) BIBREF21, while losnegaard2012data explored a Machine Learning approach to anglicism detection in Norwegian by using TiMBL (Tilburg Memory-Based Learner, an implementation of a k-nearest neighbor classifier) with character trigrams as features. garley-hockenmaier-2012-beefmoves trained a maxent classifier with character n-gram and morphological features to identify anglicisms in German online communities. In Spanish, serigos2017using extracted anglicisms from a corpus of Argentinian newspapers by combining dictionary lookup (aided by TreeTagger and the NLTK lemmatizer) with automatic filtering of capitalized words and manual inspection. In serigos2017applying, a character n-gram module was added to estimate the probabilities of a word being English or Spanish. moreno2018configuracion used different pattern-matching filters and lexicon lookup to extract anglicism cadidates from a corpus of tweets in US Spanish.
In Spanish, serigos2017using extracted anglicisms from a corpus of Argentinian newspapers by combining dictionary lookup (aided by TreeTagger and the NLTK lemmatizer) with automatic filtering of capitalized words and manual inspection. In serigos2017applying, a character n-gram module was added to estimate the probabilities of a word being English or Spanish. moreno2018configuracion used different pattern-matching filters and lexicon lookup to extract anglicism cadidates from a corpus of tweets in US Spanish.
Answer: Yes.
Question: Do the authors report on English datasets only?
Evidence: We consider a dataset of curated gang and non-gang members' Twitter profiles collected from our previous work BIBREF9 . It was developed by querying the Followerwonk Web service API with location-neutral seed words known to be used by gang members across the U.S. in their Twitter profiles. The dataset was further expanded by examining the friends, follower, and retweet networks of the gang member profiles found by searching for seed words. Specific details about our data curation procedure are discussed in BIBREF9 . Ultimately, this dataset consists of 400 gang member profiles and 2,865 non-gang member profiles. For each user profile, we collected up to most recent 3,200 tweets from their Twitter timelines, profile description text, profile and cover images, and the comments and video descriptions for every YouTube video shared by them. Table 1 provides statistics about the number of words found in each type of feature in the dataset. It includes a total of 821,412 tweets from gang members and 7,238,758 tweets from non-gang members.
It was developed by querying the Followerwonk Web service API with location-neutral seed words known to be used by gang members across the U.S. in their Twitter profiles.
Answer: Yes.
Question: Suggest 5 horror films to me
Answer: Psycho
Babadook
The Exorcist
Saw
Rosemary's Baby
Question: Give me a bulleted list of the seven most recent Harvard University presidents
Answer: * Lawrence S. Bacow (2018 - present)
* Drew Gilpin Faust (2007 - 2018)
* Lawrence H. Summers (2001 - 2007)
* Neil L. Rudenstine (1991 - 2001)
* Derek Bok (2006 - 2007) (interim, also served 1971-1991)
* Nathan Marsh Pusey (1953-1971)
* James Bryant Conant (1933-1953)
Question: Classify list as breeds of either a 'dog' or a 'cat': Border Collie, Siamese, Maine Coon, German Shepard, Persian, and Corgi?
Answer: Border Collie is a breed of dog. Siamese is a breed of cat. Maine Coon is breed of cat. German Shepard is a breed of dog. Persian is a breed of cat. Corgi is breed of dog.
Question: Which of the following items would I need on a surf trip? Surfboard, leash, tent, knife, wetsuit, batteries, granola bars, bag of grapes, lead weights, a dump truck, the complete collection of Beach Boys albums on vinyl.
Answer: The useful items are a surfboard, leash, wetsuit and granola bars
Question: What do the authors propose for modeling sentences?
Evidence: In this paper, we proposed a method of stacking multiple LSTM layers for modeling sentences, dubbed CAS-LSTM. It uses not only hidden states but also cell states from the previous layer, for the purpose of controlling the vertical information flow in a more elaborate way. We evaluated the proposed method on various benchmark tasks: natural language inference, paraphrase identification, and sentiment classification. Our models achieve the new state-of-the-art accuracy on SNLI and Quora Question Pairs datasets and obtain comparable results on MultiNLI and SST datasets. The proposed architecture can replace any stacked LSTM under one weak restriction—the size of states should be identical across all layers.
For future work we plan to apply the CAS-LSTM architecture beyond sentence modeling tasks. Various problems e.g. sequence labeling, sequence generation, and language modeling might benefit from sophisticated modulation on context integration. Aggregating diverse contexts from sequential data, e.g. those from forward and backward reading of text, could also be an intriguing research direction.
In this paper, we proposed a method of stacking multiple LSTM layers for modeling sentences, dubbed CAS-LSTM. It uses not only hidden states but also cell states from the previous layer, for the purpose of controlling the vertical information flow in a more elaborate way.
Answer: A method of stacking multiple LSTM layers, dubbed CAS-LSTM. It uses not only hidden states but also cell states from the previous layer, for the purpose of controlling the vertical information flow in a more elaborate way.
Question: How many uniue words are in the dataset?
Evidence: The large corpus acquired from multiple resources is rich in vocabulary. We present the complete statistics of collected corpus (see Table TABREF52) with number of sentences, words and unique tokens.
FLOAT SELECTED: Table 2: Complete statistics of collected corpus from multiple resources.
The large corpus acquired from multiple resources is rich in vocabulary. We present the complete statistics of collected corpus (see Table TABREF52) with number of sentences, words and unique tokens.
FLOAT SELECTED: Table 2: Complete statistics of collected corpus from multiple resources.
Answer: 908456 unique words are available in collected corpus.
Question: Does the paper report the accuracy of the model?
Evidence: The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. For the former, we reach our best result by using as aligned information the French, the original aligned language for this dataset. Languages closely related to French (Spanish and Portuguese) ranked better, while our worst result used German. English also performs notably well in our experiments. We believe this is due to the statistics features of the resulting text. We observe in Table that the English portion of the dataset contains the smallest vocabulary among all languages. Since we train our systems in very low-resource settings, vocabulary-related features can impact greatly the system's capacity to language-model, and consequently the final quality of the produced alignments. Even in high-resource settings, it was already attested that some languages are more difficult to model than others BIBREF9.
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. 
Answer: No.
Question: Give me a list of fun activities to do in the summer
Answer: In the summer you could do a a variety of activities. Depending on everybody's preference, you can enjoy a sunny day at the beach, hiking in the mountains, ride the bicycle, attend a live concert or simply enjoy a picnic. If you are more of an active person, you can try activities like parasailing, ride a hot air balloon, waterskiing or canyoning.
Question: Who are the best guitarists of all time?
Answer: Although there have been many great guitarists over the years, Slash, Eric Clapton, Eddie Van Halen, Jimi Hendrix, Carlos Santana, Slash, Prince and Jimmy Page are the most commonly thought of as the best ever.
Question: Is the natural language crucial in the planning of an agent?
Evidence: Negotiations, either between individuals or entities, are ubiquitous in everyday human interactions ranging from sales to legal proceedings. Being a good negotiator is a complex skill, requiring the ability to understand the partner's motives, ability to reason and to communicate effectively, making it a challenging task for an automated system. While research in building automatically negotiating agents has primarily focused on agent-agent negotiations BIBREF0, BIBREF1, there is a recent interest in agent-human negotiations BIBREF2 as well. Such agents may act as mediators or can be helpful for pedagogical purposes BIBREF3.
Efforts in agent-human negotiations involving free-form natural language as a means of communication are rather sparse. Researchers BIBREF4 recently studied natural language negotiations in buyer-seller bargaining setup, which is comparatively less restricted than previously studied game environments BIBREF5, BIBREF6. Lack of a well-defined structure in such negotiations allows humans or agents to express themselves more freely, which better emulates a realistic scenario. Interestingly, this also provides an exciting research opportunity: how can an agent leverage the behavioral cues in natural language to direct its negotiation strategies? Understanding the impact of natural language on negotiation outcomes through a data-driven neural framework is the primary objective of this work.
We focus on buyer-seller negotiations BIBREF4 where two individuals negotiate the price of a given product. Leveraging the recent advancements BIBREF7, BIBREF8 in pre-trained language encoders, we attempt to predict negotiation outcomes early on in the conversation, in a completely data-driven manner (Figure FIGREF3). Early prediction of outcomes is essential for effective planning of an automatically negotiating agent. Although there have been attempts to gain insights into negotiations BIBREF9, BIBREF10, to the best of our knowledge, we are the first to study early natural language cues through a data-driven neural system (Section SECREF3). Our evaluations show that natural language allows the models to make better predictions by looking at only a fraction of the negotiation. Rather than just realizing the strategy in natural language, our empirical results suggest that language can be crucial in the planning as well. We provide a sample negotiation from the test set BIBREF4 along with our model predictions in Table TABREF1.
Rather than just realizing the strategy in natural language, our empirical results suggest that language can be crucial in the planning as well.
Answer: Yes, it is.
Question: What is the gun culture in the United States?
Evidence: In the United States, gun culture encompasses the behaviors, attitudes, and beliefs about firearms and their usage by civilians. Gun ownership in the United States is the highest in the world, and constitutionally protected by the Second Amendment to the United States Constitution. Firearms are widely used in the United States for self-defense, hunting, and recreational uses, such as target shooting.
Answer: The United States has become a victim of its past as it relates to its gun culture. The 2nd Amendment was ratified in 1791, which at the time seemed appropriate given that the United States had just waged a war against England for its freedom. However, more than 200 years later, the 2nd Amendment no longer solely ties itself to its original intent: to protect itself and its people. The prevalence of guns in the United States has led to the United States having the highest gun ownership rates in the world. This in turn has resulted in many inexplicable acts of gun violence that have cost innocent lives and traumatized many.
Question: Does Singapore have more than five official languages?
Evidence: Singapore (/ˈsɪŋ(ɡ)əpɔːr/ (listen)), officially the Republic of Singapore, is an island country and city-state in maritime Southeast Asia. It lies about one degree of latitude (137 kilometres or 85 miles) north of the equator, off the southern tip of the Malay Peninsula, bordering the Strait of Malacca to the west, the Singapore Strait to the south, the South China Sea to the east, and the Straits of Johor to the north. The country's territory is composed of one main island, 63 satellite islands and islets, and one outlying islet; the combined area of these has increased by 25% since the country's independence as a result of extensive land reclamation projects. It has the third highest population density in the world. With a multicultural population and recognising the need to respect cultural identities of the major ethnic groups within the nation, Singapore has four official languages: English, Malay, Mandarin, and Tamil. English is the lingua franca and numerous public services are available only in English. Multi-racialism is enshrined in the constitution and continues to shape national policies in education, housing, and politics.
Answer: No, Singapore has only four official languages.
Question: If we dug straight through to where we stood on Earth, would we reach the other side?
Answer: The core of the earth is a molten core of heavy metals, its probable you would not survive the journey.
Question: Sort these into breakfast or dinner foods: Waffles, pancakes, turkey, chicken noodle soup, salad, bacon and eggs, egg drop soup
Answer: The breakfast foods are waffles, pancakes and bacon and eggs. The dinner foods are turkey, chicken noodle soup, salad, and egg drop soup
Question: What is Southern Charm the TV Show?
Evidence: Southern Charm is an American reality television series that premiered on Bravo on March 3, 2014. The series chronicles the personal and professional lives of several socialites who reside in Charleston, South Carolina.

The show focuses on the Southern culture, along with the political history of the area, and has featured local historical places including Lewisfield Plantation and Mikell House. The show gives viewers an inside look at modern day aristocracy in Charleston, South Carolina.
Answer: Southern Charm is a Bravo TV Show based in Charleston SC. They go to dinners, throw parties, fight with each other like any other reality TV show. Some of the known character on the show include Craig Conover, Shep Rose, Austen Kroll, Leva Bonaparte, Madison LeCroy, and Taylor-Ann Greene.
Question: Which GAN do they use?
Evidence: We assume that for each corpora INLINEFORM0 , we are given word embeddings for each word INLINEFORM1 , where INLINEFORM2 is the dimension of each word embedding. We are also given a classification task on documents that is represented by a parametric model INLINEFORM3 taking document embeddings as feature vectors. We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . Note that INLINEFORM7 are given but INLINEFORM8 is trained. Here we consider INLINEFORM9 as the generator, and the goal of the discriminator is to distinguish documents represented by the original embeddings INLINEFORM10 and the same documents represented by the new embeddings INLINEFORM11 .
We assume that for each corpora INLINEFORM0 , we are given word embeddings for each word INLINEFORM1 , where INLINEFORM2 is the dimension of each word embedding. We are also given a classification task on documents that is represented by a parametric model INLINEFORM3 taking document embeddings as feature vectors. We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . Note that INLINEFORM7 are given but INLINEFORM8 is trained. Here we consider INLINEFORM9 as the generator, and the goal of the discriminator is to distinguish documents represented by the original embeddings INLINEFORM10 and the same documents represented by the new embeddings INLINEFORM11 .
Answer: The answers are shown as follows:
* We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 .
Question: Identify which instrument is string or woodwind: Diplica, Kontra
Answer: Kontra is string, Diplica is woodwind.
Question: What are the 4 Non-commercial rule-based prompts?
Evidence: We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
4 Non-commercial rule-based: E.L.I.Z.A. (Wallace and Dunlop), Parry (Colby, 2016), A.L.I.C.E. (Wallace, 2014), Alley (Learn English Network, 2014).
Answer: E.L.I.Z.A., Parry, A.L.I.C.E., Alley.
Question: According to this reference text about UVA, what years have UVA women won NCAA titles in rowing?
Evidence: In the 21st century alone, UVA teams have won seventeen NCAA championships. The men's teams have won recent NCAA titles in basketball (2019); lacrosse (2003, 2006, 2011, 2019, and 2021); baseball (2015); soccer (2009 and 2014); and tennis (2013, 2015, 2016, 2017, and 2022). UVA women have won recent NCAA titles in rowing (2010 and 2012) and swimming & diving (2021 and 2022). The Cavaliers rank first in the ACC (a power conference) with 22 men's NCAA Championships, and rank second in the conference with nine women's NCAA Championships.
Answer: UVA women won NCAA titles in rowing in 2010 and 2012.
Question: Who are the contenders for the World Chess Championship in 2023?
Answer: Ian Nepomniachtchi from Russia up against Ding Liren from China
Question: Identify which animal species is alive or extinct: Machaeroides, Red Panda
Answer: Red Panda is alive, Machaeroides is extinct.
Question: Give me a list of different ways to brew coffee
Answer: Drip brew, french press, espresso, pour over, percolator
Question: Landlord can I get a dog.
Answer: Dear Landlord. I am keen to get a rescue dog. I would like to request your support before I start my process. I will ensure you that I shall chose a dog that is small and will not disrupt the other tenants. Science is proving that pets are very good for mental health. I know my neighbours and I am sure they will not mind.  I look forward to you support and I will keep you posted of my progress in finding a suitable pet that would be welcomed in the building.  Many thanks. My best regards.
Question: Given the reference text below, what is Air Jordan?
Evidence: Air Jordan is a line of basketball shoes and athletic apparel produced by American corporation Nike, Inc. The first Air Jordan shoe was produced for Hall of Fame former basketball player Michael Jordan during his time with the Chicago Bulls in late 1984 and released to the public on April 1, 1985. The shoes were designed for Nike by Peter Moore, Tinker Hatfield, and Bruce Kilgore.
Answer: Air Jordan is a line of basketball shoes produced by Nike and Michael Jordan, first released in public on 1st April 1985.
Question: Give a list of possible reasons why couples would divorce
Answer: People grow apart; difference of opinions on where to live; financial stress; wanting different things in life; infidelity; different approaches to parenting; falling out of love; differing stances on having children; difficult relationships with in-laws; different views on parenting and parenting styles
Question: What year was the Ice Bowl played in?
Answer: Often called "The Ice Bowl", the 1967 NFL Championship was a game between the Dallas Cowboys and the Green Bay Packers. The game was played in frigid conditions, at a temperature of −15 °F (−26 °C) before wind chill.
Question: Suppose you started at 0 degrees N, 0 degrees E on the earth. You started traveling due north, continuing straight for 2439 miles, turned 60 degrees counter-clockwise, traveled another 4878 miles, turned 60 degrees counter-clockwise traveled another 4878 miles, turned 60 degrees once again, and traveled another 4878 miles. At this point where would you be? Please explain the mathematics of how you came to your conclusion.
Answer: You would be approximately 35 degrees S, 0 degrees E, or 2439 miles south of your starting position, assuming the earth is a sphere with a circumference of 24,901 miles. After traveling 2439 miles north, you would be at 35 degrees N, 0 degrees E. After turning 60 degrees counter clockwise and traveling 4878 miles, you would be at 35 degrees N, 90 degrees W. Turning 60 degrees counter-clockwise and traveling another 4878 miles, you would arrive at 35 degrees S, 90 degrees W. Finally turning 60 degrees counter-clockwise and traveling 2439 miles you would arrive at 35 degrees S, 0 degrees W. If you returned to your starting position, you will have traced out a spherical square.