DataHammer/scimrc huggingface dataset:
Question: What do the correlation demonstrate? 
Evidence: To investigate the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods, we first study whether there are significant, strong and meaningful correlations between the terms present in each corpus and the many neighbourhood attributes through the Pearson correlation coefficient $\rho $ . For each term in each corpus, we calculate the correlation between the term and all the selected demographic attributes. To do so, for each term, we define a vector with the dimension of the number of neighbourhoods. The value of each cell in this vector represents the normalised tf-idf value of the term for the corresponding neighbourhood. For each demographic attribute, we also define a vector with the dimension of the number of neighbourhoods. Each cell represents the value for the demographic attribute of the corresponding neighbourhood. We then calculate the Pearson correlation coefficient ( $\rho $ ) between these two vectors to measure the strength of the association between each term and each attribute.
To investigate the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods, we first study whether there are significant, strong and meaningful correlations between the terms present in each corpus and the many neighbourhood attributes through the Pearson correlation coefficient $\rho $ .
Answer: The answers are shown as follows:
* the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods
Question: Identify from the following list all of the ingredients that are necessary to bake chocolate chip cookies: butter, spinach, eggs, flour, apples, sugar, vanilla extract, chocolate chips, charcoal, a positive COVID 19 test cassette, fingernail polish, salt
Answer: Butter, eggs, flour, sugar, vanilla extract, chocolate chips, salt
Question: India's Last 10 Presidents
Answer: Droupadi Murmu
Ram Nath Kovind
Pranab Mukherjee
Pratibha Devisingh Patil
A. P. J. Abdul Kalam
K. R. Narayanan
Shankar Dayal Sharma
R. Venkataraman
Giani Zail Singh
Neelam Sanjiva Reddy
Question: In the series A Song of Ice and Fire, who is the founder of House Massey?
Answer: Maldon Massey
Question: What is ECharts Java?
Evidence: ECharts Java is a lightweight but comprehensive library for Java developers to easily use JavaScript visualization library Apache ECharts. The simple chart mode facilitates users to write visualization fast and easily, empowered by the clean APIs provided by ECharts Java. The advanced mode helps create an Option object and its Json representation in chainable Java codes, which includes almost all the features defined in Apache ECharts. Now ECharts Java supports Apache ECharts version 5.x.

Features

- Simple, clean and organized APIs, supporting method chaining
- Full coverage of Apache ECharts functionalities
- Easily integrate with Web Frameworks
- Flexible export format, including HTML and images
- Complete and detailed documentation and examples
Answer: ECharts Java is a lightweight, comprehensive library for Java developers to easily integrate JavaScript visualization library Apache ECharts into their web frameworks. The simple and clean APIs facilitate users to develop visualization components fast and easily. For complex and advanced usage, it allows developers to create an Option object and its Json representation. It is compatible with Apache ECharts version 5.x and provides almost full coverage of Apache ECharts functionalities. 

Here are some highlights of ECharts Java:

- Simple, clean, organized APIs with method chaining
- Full coverage of Apache ECharts functionalities
- Easily integrate with Web Frameworks
- Flexible export format, including HTML and images
- Complete and detailed documentation and examples
Question: List the ten episode titles from the fifth season of "Game of Thrones" in broadcast order.
Answer: The Wars to Come, The House of Black and White, High Sparrow, Sons of the Harpy, Kill the Boy, Unbent, Unbowed, Unbroken, The Gift, Hardhome, The Dance of Dragons, Mother's Mercy
Question: What are the chess pieces that a player starts out with?
Evidence: Chess pieces are divided into two different colored sets. While the sets might not be literally white and black (e.g. the light set may be a yellowish or off-white color, the dark set may be brown or red), they are always referred to as "white" and "black". The players of the sets are referred to as White and Black, respectively. Each set consists of sixteen pieces: one king, one queen, two rooks, two bishops, two knights, and eight pawns. Chess sets come in a wide variety of styles; for competition, the Staunton pattern is preferred.

The game is played on a square board of eight rows (called ranks) and eight columns (called files). By convention, the 64 squares alternate in color and are referred to as light and dark squares; common colors for chessboards are white and brown, or white and dark green.
Answer: 8 pawns, 2 knights, 2 bishops, 2 rooks, 1 queen, and 1 king
Question: Give me a bulleted list of the past 10 Masters Tournament Champions.
Answer: Scottie Scheffler
Hideki Matsuyama
Dustin Johnson
Tiger Woods
Patrick Reed
Sergio García
Danny Willett
Jordan Spieth
Bubba Watson
Adam Scott
Question: In Sec 4.2, what are the delta rule-based functions?
Evidence: The benefit of handling the large objects in a 2 step process is that we can compute their waypoints in a more flexible manner. In our framework, we find backing up a few steps to be a very effective strategy, which leaves some margin between the target position of the agent and the target object the agent needs to interact with This strategy is particularly critical for articulated objects. We use simple heuristics, denoted δ1 and δ2 as introduced in the paper. Specifically, the agent will back up 3 steps if the target object is a fridge, 2 steps if it is a safe, a cabinet or a drawer, and 1 step for everything else. The implementation of the δ*(·, ·) is simply an integer 1, 2 or 3 for either x or y coordinate given the 4 different rotation angles r ∈ {0。 , 90。, 180。, 270。}.
Answer: These are the backup functions described in Appendix A.2. We will add full implementation details.
Question: Classify each of the following transportation options as "Mass Transportation" or "Individual Transportation": car, bike, subway, train, tram, motorcycle, scooter
Answer: Car: Individual Transportation
Bike: Individual Transportation
Subway: Mass Transportation
Train: Mass Transportation
Tram: Mass Transportation
Motorcycle: Individual Transportation
Scooter: Individual Transportation
Question: Rank countries with highest population
Answer: Total world population is around 8 billion. China has 1.4 billion and India has another 1.4 billion population. United States has 330 million. Indonesia has 270 million and Pakistan has 235 million. 45% of the world population is present in these 5 countries.
Question: Who is Lewis Hamilton of Formula 1?
Evidence: Sir Lewis Carl Davidson Hamilton (born 7 January 1985) is a British racing driver currently competing in Formula One, driving for the Mercedes-AMG Petronas Formula One Team. In Formula One, Hamilton has won a joint-record seven World Drivers' Championship titles (tied with Michael Schumacher), and holds the records for the most wins (103), pole positions (103), and podium finishes (192), among others
Answer: Born and raised in Stevenage, Hertfordshire, Hamilton joined the McLaren young driver programme in 1998 at the age of 13, becoming the youngest racing driver ever to be contracted by a Formula One team. This led to a Formula One drive with McLaren for six years from 2007 to 2012, making Hamilton the first ever black driver to race in the series. In his inaugural season, Hamilton set numerous records as he finished runner-up to Kimi Räikkönen by one point. The following season, he won his maiden title in dramatic fashion—making a crucial overtake at the last corner on the last lap of the last race of the season—to become the then-youngest Formula One World Champion in history. After six years with McLaren, Hamilton signed with Mercedes in 2013.

Changes to the regulations for 2014 mandating the use of turbo-hybrid engines saw the start of a highly successful period for Hamilton, during which he won six further drivers' titles. Consecutive titles came in 2014 and 2015 during an intense rivalry with teammate Nico Rosberg. Following Rosberg's retirement in 2016, Ferrari's Sebastian Vettel became Hamilton's closest rival in two championship battles, in which Hamilton twice overturned mid-season point deficits to claim consecutive titles again in 2017 and 2018. His third and fourth consecutive titles followed in 2019 and 2020 to equal Schumacher's record of seven drivers' titles. Hamilton achieved his 100th pole position and race win during the 2021 season.

Hamilton has been credited with furthering Formula One's global following by appealing to a broader audience outside the sport, in part due to his high-profile lifestyle, environmental and social activism, and exploits in music and fashion. He has also become a prominent advocate in support of activism to combat racism and push for increased diversity in motorsport. Hamilton was the highest-paid Formula One driver from 2013 to 2021, and was ranked as one of the world's highest-paid athletes by Forbes of twenty-tens decade and 2021. He was also listed in the 2020 issue of Time as one of the 100 most influential people globally, and was knighted in the 2021 New Year Honours. Hamilton was granted honorary Brazilian citizenship in 2022.
Question: Given a reference text about True Corporation Public Company Limited, tell me what country it's in and how it was formed.
Evidence: True Corporation Public Company Limited (TRUE) (Formerly: True Corporation Public Company Limited and Total Access Communication Public Company Limited) is a communications conglomerate in Thailand. It is a joint venture between Charoen Pokphand Group and Telenor, formed by the merger between the original True Corporation and DTAC in the form of equal partnership to create a new telecommunications company that can fully meet the needs of the digital age. True controls Thailand's largest cable TV provider, TrueVisions, Thailand's largest internet service provider True Online,[citation needed] Thailand's largest mobile operators, TrueMove H and DTAC TriNet, which is second and third only to AIS. and entertainment media including television, internet, online games, and mobile phones under the True Digital brand. As of August 2014, True, along with True Telecommunications Growth Infrastructure Fund, had a combined market capitalization of US$10 billion.[citation needed] TrueMove is also a partner of Vodafone Group. Charoen Pokphand Group and Telenor hold equal ownership of 30% of True's shares as of March 2023. It operates fixed-line (as a concessionaire of NT (formerly known as TOT)), wireless, cable TV, IPTV and broadband services.
Answer: True Corporation Public Company Limited is located in Thailand and was formed by the original True Corporation merging with DTAC.
Question: What baseline do the authors propose for RUN?
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.
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.
Answer: A strong neural baseline.
Question: Why is an umbrella useful?
Answer: The top of an umbrella is made with waterproof material attached to a long handle so that it extends over the head and protects its owner from rain. The material folds down so that it can be easily carried when not needed. Often, the handle also collapses so that the umbrella becomes small enough to fit into a bag.
Question: What K-pop group did Kang Se-jung sing for?
Evidence: Kang Se-jung (born January 15, 1982) is a South Korean actress. She made her entertainment debut as a singer for the K-pop girl group Papaya in 2000; the group released two albums before disbanding in 2001. Kang began acting full-time in 2004, and has appeared in television series such as Assorted Gems (2009) and Heartless City (2013). Upon turning to acting, she used the stage name Go Na-eun before reverting to use of her birth name in 2017.
Answer: Papaya
Question: Will the positions where the NART model outputs incoherent tokens have very high hidden state similarity compared with the ART model？
Evidence: Neural machine translation has attracted much attention in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3. Given a sentence $x=(x_1, \dots ,x_{T_x})$ from the source language, the straight-forward way for translation is to generate the target words $y=(y_1, \dots , y_{T_y})$ one by one from left to right. This is also known as the AutoRegressive Translation (ART) models, in which the joint probability is decomposed into a chain of conditional probabilities:
While the ART models have achieved great success in terms of translation quality, the time consumption during inference is still far away from satisfactory. During training, the predictions at different positions can be estimated in parallel since the ground truth pair $(x,y)$ is exposed to the model. However, during inference, the model has to generate tokens sequentially as $y_{<t}$ must be inferred on the fly. Such autoregressive behavior becomes the bottleneck of the computational time BIBREF4.
In order to speed up the inference process, a line of works begin to develop non-autoregressive translation models. These models break the autoregressive dependency by decomposing the joint probability with
The lost of autoregressive dependency largely hurt the consistency of the output sentences, increase the difficulty in the learning process and thus lead to a low quality translation. Previous works mainly focus on adding different components into the NART model to improve the expressiveness of the network structure to overcome the loss of autoregressive dependency BIBREF5, BIBREF6, BIBREF7. However, the computational overhead of new components will hurt the inference speed, contradicting with the goal of the NART models: to parallelize and speed up neural machine translation models.
To tackle this, we proposed a novel hint-based method for NART model training. We first investigate the causes of the poor performance of the NART model. Comparing with the ART model, we find that: (1) the positions where the NART model outputs incoherent tokens will have very high hidden states similarity; (2) the attention distributions of the NART model are more ambiguous than those of ART model. Therefore, we design two kinds of hints from the hidden states and attention distributions of the ART model to help the training of the NART model. The experimental results show that our model achieves significant improvement over the NART baseline models and is even comparable to a strong ART baseline in BIBREF4.
Comparing with the ART model, we find that: (1) the positions where the NART model outputs incoherent tokens will have very high hidden states similarity; (2) the attention distributions of the NART model are more ambiguous than those of ART model.
Answer: Yes.
Question: Give me 10 information extraction type questions about why Michael Jordan is the best basketball player of all time that will be used for training a large language model
Evidence: Jordan's individual accolades and accomplishments include six NBA Finals Most Valuable Player (MVP) awards, ten NBA scoring titles (both all-time records), five NBA MVP awards, ten All-NBA First Team designations, nine All-Defensive First Team honors, fourteen NBA All-Star Game selections, three NBA All-Star Game MVP awards, three NBA steals titles, and the 1988 NBA Defensive Player of the Year Award. He holds the NBA records for career regular season scoring average (30.12 points per game) and career playoff scoring average (33.4 points per game). In 1999, he was named the 20th century's greatest North American athlete by ESPN, and was second to Babe Ruth on the Associated Press' list of athletes of the century. Jordan was twice inducted into the Naismith Memorial Basketball Hall of Fame, once in 2009 for his individual career, and again in 2010 as part of the 1992 United States men's Olympic basketball team ("The Dream Team"). He became a member of the United States Olympic Hall of Fame in 2009, a member of the North Carolina Sports Hall of Fame in 2010, and an individual member of the FIBA Hall of Fame in 2015 and a "Dream Team" member in 2017. In 2021, Jordan was named to the NBA 75th Anniversary Team.
Answer: Michael Jordan was a NCAA champion in college at North Carolina. In the NBA he played in six NBA championships and his team won each championship series. He was voted MVP in for each championship.  When he retired, between the two three-peats championships, the Bulls went from great to just above average.  Individually he carried at 30 point per game scoring average and was a killer on defense.  Teams schemed to stop him thinking if you stop MJ, you stop the Bulls.  They were correct and it worked somewhat in his early years. However he broke through. They still schemed and planned against him but failed.  While in his peak MJs dominance kept other great players, sharing the era with him, from championships.  Greats such as Charles Barkley, Dominique Wilkins, Karl Malone, Hakeem Olajuwan and Patrick Ewing, could not go through him although some led their teams to championships when MJ was retired.
Question: Why is the N400 so named？
Evidence: While a full discussion of each ERP component and the features of language thought to trigger each are beyond the scope of this document (for reviews see e.g. BIBREF0 , BIBREF2 , BIBREF4 , BIBREF5 , and BIBREF6 ), we introduce some basic features of ERP components to help in the discussion later. ERP components are electrical potential responses measured with respect to a baseline that are triggered by an event (in our case the presentation of a new word to a participant in an experiment). The name of each ERP component reflects whether the potential is positive or negative relative to the baseline. The N400 is so-named because it is Negative relative to a baseline (the baseline is typically recorded just before a word is presented at an electrode that is not affected by the ERP response) and because it peaks in magnitude at about 400ms after a word is presented to a participant in an experiment. The P600 is Positive relative to a baseline and peaks around 600ms after a word is presented to a participant (though its overall duration is much longer and less specific in time than the N400). The post-N400 positivity is so-named because it is part of a biphasic response; it is a positivity that occurs after the negativity associated with the N400. The early post-N400 positivity (EPNP) is also part of a biphasic response, but the positivity has an eariler onset than the standard PNP. Finally, the LAN and ELAN are the left-anterior negativity and early left-anterior negativity respectively. These are named for their timing, spatial distribution on the scalp, and direction of difference from the baseline. It is important to note that ERP components can potentially cancel and mask each other, and that it is difficult to precisely localize the neural activity that causes the changes in electrical potential at the electrodes where those changes are measured.
The N400 is so-named because it is Negative relative to a baseline (the baseline is typically recorded just before a word is presented at an electrode that is not affected by the ERP response) and because it peaks in magnitude at about 400ms after a word is presented to a participant in an experiment.
Answer: Because it is Negative relative to a baseline (the baseline is typically recorded just before a word is presented at an electrode that is not affected by the ERP response) and because it peaks in magnitude at about 400ms after a word is presented to a participant in an experiment.