DataHammer/scimrc huggingface dataset:
Question: Give me some ideas of places to visit on my next backpacking trip to South America.
Answer: - Take a cruise to visit the islands of the Galapagos
- Straddle the northern and southern hemispheres at La Mitad del Mundo (The Middle of the Earth), close to Quito, Ecuador
- Hike the Inca Trail to Machu Picchu from Cusco in Peru. You need to get a permit at least 6 months in advance.
- Hike the Salkantay Trek to Machu Picchu from Cusco in Peru. It is a great alternative to the Inca Trail if you do not get a permit.
- Visit villages around Lake Titicaca in Peru
- Take a multi-day tour to the Salar de Uyuni, the world's largest salt flat, on a four-wheel drive from Uyuni in Bolivia
- Go stargazing in the Atacama Desert in Chile
Question: How many texts were selected?
Evidence: The Russian and Ukrainian languages are mainly spoken in Russian Federation and the Ukraine and belong to the East-Slavic group of the Indo-European language family. They share many common morphosyntactic features: both are SVO languages with free word order and rich morphology, both use the Cyrillic alphabet and share many common cognates.
Both Russia and the Ukraine have common academic tradition that makes it easier to collect corpora, which are comparable in terms of both genre and strictly defined academic fields. We work with such a corpus of Russian and Ukrainian academic texts, initially collected for the purposes of cross-lingual plagiarism detection. This data is available online through a number of library services, but unfortunately cannot be republished due to copyright limitations.
The Ukrainian subcorpus contains about 60 thousand extended summaries (Russian and Ukrainian russian`автореферат', `avtoreferat') of theses submitted between 1998 and 2011. The Russian subcorpus is smaller in the number of documents (about 16 thousand, approximately the same time period), but the documents are full texts of theses, thus the total volume of the Russian subcorpus is notably larger: 830 million tokens versus 250 million tokens in the Ukrainian one. Generally, the texts belong to one genre that can be defined as post-Soviet expository academic prose, submitted for academic degree award process.
The documents were converted to plain text files from MS Word format in the case of the Ukrainian subcorpus and mainly from OCRed PDF files in the case of the Russian subcorpus. Because of this, the Russian documents often suffer from OCR artifacts, such as words split with line breaks, incorrectly recognized characters and so on. However, it does not influence the resulting model much, as we show below.
Both Ukrainian and Russian documents come with meta data allowing to separate them into academic fields, with economics, medicine and law being most frequent topics for the Ukrainian data and economics, history and pedagogy dominating the Russian data.
For evaluation, 3 topics were used, distant enough from each other and abundantly presented in both subcorpora: economics, law and history. We randomly selected 100 texts in each language for each topic. As an average length of Russian texts is significantly higher (them being full theses), we cropped them, leaving only the first 5 thousand words, to mimic the size of the Ukrainian summaries. These 600 documents in 3 classes are used as a test set (see Section "Experiment Design and Evaluation" for the description of the conducted experiments).
The corpora (including test set) were PoS-tagged. Each word was replaced with its lemma followed by a PoS-tag (`russianдиссертация_S', `russianдиссертацiя_N'). Functional parts of speech (conjunctions, pronouns, prepositions, etc.) and numerals were removed from the texts.
We randomly selected 100 texts in each language for each topic.
Answer: 100 texts in each language for each topic.
Question: Who are Kim Kardashian's siblings?
Evidence: Kimberly Noel Kardashian was born on October 21, 1980, in Los Angeles, California, to Robert and Kris Kardashian (née Houghton). She has an older sister, Kourtney, a younger sister, Khloé, and a younger brother, Rob. Their mother is of Dutch, English, Irish, and Scottish ancestry, while their father was a third-generation Armenian-American. In 1991, their parents divorced and their mother married Bruce Jenner, the 1976 Summer Olympics decathlon winner. As a result of her mother's remarriage, Kim Kardashian acquired step-brothers Burton "Burt", Brandon, and Brody; a step-sister, Casey; and later two half-sisters, Kendall and Kylie Jenner.
Answer: Kim Kardashian has six siblings. Kourtney, Khloe, and Rob Kardashian are her three fully biological siblings. Burton, Brandon, Brody, and Casey Jenner are her four step-siblings. Kendall and Kylie Jenner are her two half-sisters.
Question: Describe the sensation of taking a cold shower and explain the rationale for why someone might take one.
Answer: When you take a shower with very cold water, you throw your body into a temporary state of shock. You feel your skin raise. Your toes may curl a bit. Your eyes may widen. If it is early morning and you're still waking up, you may find that you suddenly feel very alert. 

By leaving a room with a warm and comfortable temperature and entering a space with water produced at a chilly and uncomfortable temperature you actually trick your body. The sudden shift produces alertness that can be beneficial for people who are trying to wake up without caffeine and other stimulants or are otherwise trying to train their bodies to tolerate harsh conditions.
Question: Is ABAKO no longer an active political party?
Evidence: The Alliance of Bakongo (French: Alliance des Bakongo, or ABAKO) was a Congolese political party, founded by Edmond Nzeza Nlandu, but headed by Joseph Kasa-Vubu, which emerged in the late 1950s as vocal opponent of Belgian colonial rule in what today is the Democratic Republic of the Congo. Additionally, the organization served as the major ethno-religious organization for the Kongo people (also known as Bakongo) and became closely intertwined with the Kimbanguist Church which was extremely popular in the lower Congo.
Answer: Correct, The Alliance of Bakongo, or ABAKO, is no longer a political party.
Question: Haven't you always believed that Earth is flat?
Answer: The statement is incorrect because I never believed that Earth is flat. The Earth is an irregularly shaped Ellipsoid.
Question: What is handscroll used for
Evidence: The handscroll is a long, narrow, horizontal scroll format in East Asia used for calligraphy or paintings. A handscroll usually measures up to several meters in length and around 25–40 cm in height. Handscrolls are generally viewed starting from the right end. This kind of scroll is intended to be read or viewed flat on a table, in sections. The format thus allows for the depiction of a continuous narrative or journey. The traditional alternative format in East Asian paintings is the vertical hanging scroll, which is rarely as long.
Answer: The handscroll is a long, narrow, horizontal scroll format in East Asia used for calligraphy or paintings.
Question: What corpus was the source of the OpenIE extractions?
Evidence: We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. For each test set, we use the corresponding training questions $Q_\mathit {tr}$ to retrieve domain-relevant sentences from S. Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).
We consider two knowledge sources. The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. This corpus is used by the IR solver and also used to create the tuple KB T and on-the-fly tuples $T^{\prime }_{qa}$ . Additionally, TableILP uses $\sim $ 70 Curated tables (C) designed for 4th grade NY Regents exams.
We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. 
Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).
The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. 
Answer: The answers are shown as follows:
* domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining
Question: By how much do they, on average, outperform the baseline multilingual model on 16 low-resource tasks?
Evidence: To evaluate our sampling strategy, we compare it with the pretrained model and fine-tuned model on 16 different corpora. The results show that our approach outperforms those baselines on all corpora: it achieves 1.6% lower phone error rate on average. Additionally, we demonstrate that our corpus-level embeddings are able to capture the characteristics of each corpus, especially the language and domain information. The main contributions of this paper are as follows:
To evaluate our sampling strategy, we compare it with the pretrained model and fine-tuned model on 16 different corpora. The results show that our approach outperforms those baselines on all corpora: it achieves 1.6% lower phone error rate on average.
Answer: The answers are shown as follows:
* 1.6% lower phone error rate on average
Question: Which datasets are used in experiments?
Evidence: Due to the reduction of computational time complexity we expect our method to yield performance gains especially for longer sequences and tasks where the source can be compactly represented in a fixed-size memory matrix. To investigate the trade-off between speed and performance, we compare our technique to standard models with and without attention on a Sequence Copy Task of varying length like in BIBREF14 . We generated 4 training datasets of 100,000 examples and a validation dataset of 1,000 examples. The vocabulary size was 20. For each dataset, the sequences had lengths randomly chosen between 0 to $L$ , for $L\in \lbrace 10, 50, 100, 200\rbrace $ unique to each dataset.
Next, we explore if the memory-based attention mechanism is able to fit complex real-world datasets. For this purpose we use 4 large machine translation datasets of WMT'17 on the following language pairs: English-Czech (en-cs, 52M examples), English-German (en-de, 5.9M examples), English-Finish (en-fi, 2.6M examples), and English-Turkish (en-tr, 207,373 examples). We used the newly available pre-processed datasets for the WMT'17 task. Note that our scores may not be directly comparable to other work that performs their own data pre-processing. We learn shared vocabularies of 16,000 subword units using the BPE algorithm BIBREF19 . We use newstest2015 as a validation set, and report BLEU on newstest2016.
To investigate the trade-off between speed and performance, we compare our technique to standard models with and without attention on a Sequence Copy Task of varying length like in BIBREF14 .
For this purpose we use 4 large machine translation datasets of WMT'17 on the following language pairs: English-Czech (en-cs, 52M examples), English-German (en-de, 5.9M examples), English-Finish (en-fi, 2.6M examples), and English-Turkish (en-tr, 207,373 examples).
Answer: Sequence Copy Task and WMT'17
Question: What Is a Request for Proposal (RFP)?
Answer: A request for proposal (RFP) is a structured acquisition process that announces a project, describes it, and solicits bids from qualified contractors to complete it. Most organizations prefer to launch their projects using RFPs, and many governments always use them.  In some cases there is are a minimum number of respondents required and minimum timeframe to make an award.
Question: Can you give me a short summary of the follow paragraph about the history of electronic music?
Evidence: During the 1960s, digital computer music was pioneered, innovation in live electronics took place, and Japanese electronic musical instruments began to influence the music industry. In the early 1970s, Moog synthesizers and drum machines helped popularize synthesized electronic music. The 1970s also saw electronic music begin to have a significant influence on popular music, with the adoption of polyphonic synthesizers, electronic drums, drum machines, and turntables, through the emergence of genres such as disco, krautrock, new wave, synth-pop, hip hop, and EDM. In the early 1980s mass-produced digital synthesizers, such as the Yamaha DX7, became popular, and MIDI (Musical Instrument Digital Interface) was developed. In the same decade, with a greater reliance on synthesizers and the adoption of programmable drum machines, electronic popular music came to the fore. During the 1990s, with the proliferation of increasingly affordable music technology, electronic music production became an established part of popular culture. In Berlin starting in 1989, the Love Parade became the largest street party with over 1 million visitors, inspiring other such popular celebrations of electronic music.
Answer: With the advent of new electronic instruments in the 60s and 70s, namely synthesizers and drum machines, electronic music quickly gained mainstream popularity through genres like disco and hip hop. As the technology progressed through the 80s and 90s, more affordable instruments led electronic music to be even more widely embraced, cementing its status in pop culture and leading to the rise of large electronic music events around the globe.
Question: What three datasets are used to measure performance?
Evidence: In this study, we evaluate our model on three real KG including two typical large-scale knowledge graph: Freebase BIBREF0, DBpedia BIBREF1 and a self-construction game knowledge graph. First, we adapt a dataset extracted from Freebase, i.e., FB24K, which used by BIBREF26. Then, we collect extra entities and relations that from DBpedia which that they should have at least 100 mentions BIBREF7 and they could link to the entities in the FB24K by the sameAs triples. Finally, we build a datasets named as DBP24K. In addition, we build a game datasets from our game knowledge graph, named as Game30K. The statistics of datasets are listed in Table TABREF24.
First, we adapt a dataset extracted from Freebase, i.e., FB24K, which used by BIBREF26. Then, we collect extra entities and relations that from DBpedia which that they should have at least 100 mentions BIBREF7 and they could link to the entities in the FB24K by the sameAs triples. Finally, we build a datasets named as DBP24K. In addition, we build a game datasets from our game knowledge graph, named as Game30K.
Answer: The answers are shown as follows:
* FB24K
* DBP24K
* Game30K
Question: What is the main difference between decoder only transformer and encoder only transformer?
Answer: The main difference is that the encoder-only transformer looks at the input sequence from both directions, both front and the back, this means they can better understand the relationships between each token in the sequence. On the other hand, the decoder-only transformer only look at one direction of the input, which means they are better suited for autoregressive language tasks such as text generation.
Question: Based on OpenStreetMap, what did the authors design and collect?
Evidence: The task of interpreting and following natural language (NL) navigation instructions involves interleaving different signals, at the very least the linguistic utterance and the representation of the world. For example, in turn right on the first intersection, the instruction needs to be interpreted, and a specific object in the world (the intersection) needs to be located in order to execute the instruction. In NL navigation studies, the representation of the world may be provided via visual sensors BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 or as a symbolic world representation. This work focuses on navigation based on a symbolic world representation (referred to as a map).
Previous datasets for NL navigation based on a symbolic world representation, HCRC BIBREF5, BIBREF6, BIBREF7 and SAIL BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15 present relatively simple worlds, with a small fixed set of entities known to the navigator in advance. Such representations bypass the great complexity of real urban navigation, which consists of long paths and an abundance of previously unseen entities of different types.
In this work we introduce Realistic Urban Navigation (RUN), where we aim to interpret navigation instructions relative to a rich symbolic representation of the world, given by a real dense urban map. To address RUN, we designed and collected a new dataset based on OpenStreetMap, in which we align NL instructions to their corresponding routes. Using Amazon Mechanical Turk, we collected 2515 instructions over 3 regions of Manhattan, all specified (and verified) by (respective) sets of humans workers. This task raises several challenges. First of all, we assume a large world, providing long routes, vulnerable to error propagation; secondly, we assume a rich environment, with entities of various different types, most of which are unseen during training and are not known in advance; finally, we evaluate on the full route intended, rather than on last-position only.
We then propose a strong neural baseline for RUN where we augment a standard encoder-decoder architecture with an entity abstraction layer, attention over words and worlds, and a constantly updating world-state. Our experimental results and ablation study show that this architecture is indeed better-equipped to treat grounding in realistic urban settings than standard sequence-to-sequence architectures. Given this RUN benchmark, empirical results, and evaluation procedure, we hope to encourage further investigation into the topic of interpreting NL instructions in realistic and previously unseen urban domains.
[1]The task defined by BIBREF6 is of moving between entities only.
To address RUN, we designed and collected a new dataset based on OpenStreetMap, in which we align NL instructions to their corresponding routes.
Answer: A new dataset, in which they align NL instructions to their corresponding routes.
Question: What is the dataset used in the paper?
Evidence: Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17. On the Mboshi side, we consider alphabetic representation with no tonal information. On the French side,we simply consider the default segmentation into words.
Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17. On the Mboshi side, we consider alphabetic representation with no tonal information. On the French side,we simply consider the default segmentation into words.
Answer: The answers are shown as follows:
* French-Mboshi 5K corpus
Question: For the corpus that is annotated with shallow syntax, what do the authors propose?
Evidence: We briefly review the shallow syntactic structures used in this work, and then present a model architecture to obtain embeddings from shallow Syntactic Context (mSynC).
Given a corpus that is annotated with shallow syntax, we propose to condition on both the preceding words and their annotations.
Answer: They propose to condition on both the preceding words and their annotations.
Question: Who starred in Godfather Part II
Evidence: The Godfather Part II is a 1974 American epic crime film produced and directed by Francis Ford Coppola. The film is partially based on the 1969 novel The Godfather by Mario Puzo, who co-wrote the screenplay with Coppola, and it is both a sequel and a prequel to the 1972 film The Godfather, presenting parallel dramas: one picks up the 1958 story of Michael Corleone (Al Pacino), the new Don of the Corleone family, protecting the family business in the aftermath of an attempt on his life; the prequel covers the journey of his father, Vito Corleone (Robert De Niro), from his Sicilian childhood to the founding of his family enterprise in New York City. The ensemble cast also features Robert Duvall, Diane Keaton, Talia Shire, Morgana King, John Cazale, Mariana Hill, and Lee Strasberg.
Answer: Following the success of the first film, Paramount Pictures began developing a follow-up, with many of the cast and crew returning. Coppola, who was given more creative control, had wanted to make both a sequel and a prequel to The Godfather that would tell the story of the rise of Vito and the fall of Michael. Principal photography began in October 1973 and wrapped up in June 1974. The Godfather Part II premiered in New York City on December 12, 1974, and was released in the United States on December 20, 1974, receiving divided reviews from critics; its reputation, however, improved rapidly, and it soon became the subject of critical re-appraisal. It grossed $48 million in the United States and Canada and up to $93 million worldwide on a $13 million budget. The film was nominated for eleven Academy Awards, and became the first sequel to win Best Picture. Its six Oscar wins also included Best Director for Coppola, Best Supporting Actor for De Niro and Best Adapted Screenplay for Coppola and Puzo. Pacino won Best Actor at the BAFTAs and was nominated at the Oscars.

Like its predecessor, Part II remains a highly influential film, especially in the gangster genre. It is considered to be one of the greatest films of all time, as well as the rare example of a sequel that may be superior to its predecessor. In 1997, the American Film Institute ranked it as the 32nd-greatest film in American film history and it retained this position 10 years later. It was selected for preservation in the U.S. National Film Registry of the Library of Congress in 1993, being deemed "culturally, historically, or aesthetically significant". The Godfather Part III, the final installment in the trilogy, was released in 1990.
Question: What was the world’s first high level programming language 1957
Answer: IBM FORTRAN