DataHammer/scimrc huggingface dataset:
Question: What were the primary motivations for the Dual Alliance?
Evidence: The Dual Alliance (German: Zweibund, Hungarian: Kettős Szövetség) was a defensive alliance between Germany and Austria-Hungary, which was created by treaty on October 7, 1879, as part of Germany's Otto von Bismarck's system of alliances to prevent or limit war. The two powers promised each other support in case of attack by Russia. Also, each state promised benevolent neutrality to the other if one of them was attacked by another European power (generally taken to be France, even more so after the Franco-Russian Alliance of 1894). Bismarck saw the alliance as a way to prevent the isolation of the German Empire, which had just been founded a few years before, and to preserve peace, as Russia would not wage war against both empires.

Formation
When Austria-Hungary and Germany formed an alliance in 1879, it was one of the more surprising alliances of its time. Though both shared the German language and a similar culture, Austria-Hungary and Germany were often driven apart, most notably during the recent Austro-Prussian War. Additionally, the Habsburg rulers believed that the promotion of nationalism, which was favoured by Germany, would destroy their multinational empire. However, their common distrust of Russia brought both empires together for a common cause.
Answer: The primary motivations for the alliance between Germany and Austria-Hungary were peacemaking and protection. The alliance ensured that both Germany and Austria-Hungary would be safe from attack by Russia. The alliance also promoted peace between the two countries and other European powers.
Question: Given a reference text about Pwojè Pyebwa, who funded the project?
Evidence: Pwojè Pyebwa (Tree Project) is a tree-planting project in Haiti. It was designed, implemented, and initially run by cultural anthropologists. Different from reforestation projects, Pwojè Pyebwa promoted agroforestry—the strategic mixing of crops, trees, and animals. The agroforestry project in Haiti project was funded by the United States Agency for International Development USAID, but implemented through the Pan American Development Organization (PADF) and CARE. It was originally called the Agroforestry Outreach Project (AOP). The original project spanned a decade (1981–1991), but policies from Pwojè Pyebwa continued to direct PADF tree-planting through a second decade (1992 to 2000).

The project was executed throughout the country. Based on Haitian farmers’ voluntary and widespread participation, the project has been deemed a success by many. Pwojè Pyebwa distributed free trees to farmers, and advised on the incorporation of trees into existing cropping arrangements based on anthropological research into the land tenure, agricultural systems, and the historical market-orientation of Haitian peasant farmers. The project eventually won the Praxis prize in applied anthropology. It is estimated that the project involved one-third of the entire rural Haitian population. Approximately 65 million trees were planted.
Answer: Pwojè Pyebwa was funded by the United States Agency for International Development (USAID).
Question: Given this paragraph about video game consoles, when was the Nintendo DS released?
Evidence: In 2004, Nintendo released the Nintendo DS, which featured such innovations as dual screens – one of which being a touchscreen – and wireless connectivity for multiplayer play. Throughout its lifetime, more than 154 million units were sold, making it the most successful handheld console and the second best-selling console in history. In 2005, Nintendo released the Game Boy Micro, the last system in the Game Boy line. Sales did not meet Nintendo's expectations, with 2.5 million units being sold by 2007. In mid-2005, the Nintendo World Store was inaugurated in New York City.
Answer: 2004
Question: How do dolphins make noise?
Answer: Dolphins use their blow hole on top of their head to make a variety of noises.
Question: To which previous papers does this work compare its results?
Evidence: holistic: The baseline model which maps the holistic image feature and LSTM-encoded question feature to a common space and perform element-wise multiplication between them.
TraAtt: The traditional attention model, implementation of WTL model BIBREF9 using the same $3\times 3$ regions in SalAtt model.
RegAtt: The region attention model which employs our novel attention method, same as the SalAtt model but without region pre-selection.
ConAtt: The convolutional region pre-selection attention model which replaces the BiLSTM in SalAtt model with a weight-sharing linear mapping, implemented by a convolutional layer.
Besides, we also compare our SalAtt model with the popular baseline models i.e. iBOWIMG BIBREF4 , VQA BIBREF1 , and the state-of-the-art attention-based models i.e. WTL BIBREF9 , NMN BIBREF21 , SAN BIBREF14 , AMA BIBREF33 , FDA BIBREF34 , D-NMN BIBREF35 , DMN+ BIBREF8 on two tasks of COCO-VQA.
holistic: The baseline model which maps the holistic image feature and LSTM-encoded question feature to a common space and perform element-wise multiplication between them.

TraAtt: The traditional attention model, implementation of WTL model BIBREF9 using the same $3\times 3$ regions in SalAtt model.

RegAtt: The region attention model which employs our novel attention method, same as the SalAtt model but without region pre-selection.

ConAtt: The convolutional region pre-selection attention model which replaces the BiLSTM in SalAtt model with a weight-sharing linear mapping, implemented by a convolutional layer.

Besides, we also compare our SalAtt model with the popular baseline models i.e. iBOWIMG BIBREF4 , VQA BIBREF1 , and the state-of-the-art attention-based models i.e. WTL BIBREF9 , NMN BIBREF21 , SAN BIBREF14 , AMA BIBREF33 , FDA BIBREF34 , D-NMN BIBREF35 , DMN+ BIBREF8 on two tasks of COCO-VQA.
Answer: The answers are shown as follows:
* holistic
* TraAtt
* RegAtt
* ConAtt
* ConAtt
* iBOWIMG 
* VQA
* VQA
* WTL 
* NMN 
* SAN 
* AMA 
* FDA 
* D-NMN
* DMN+
Question: How are templates discovered from training data?
Evidence: Our framework includes three key modules: Retrieve, Fast Rerank, and BiSET. For each source article, Retrieve aims to return a few candidate templates from the training corpus. Then, the Fast Rerank module quickly identifies a best template from the candidates. Finally, BiSET mutually selects important information from the source article and the template to generate an enhanced article representation for summarization.
This module starts with a standard information retrieval library to retrieve a small set of candidates for fine-grained filtering as cao2018retrieve. To do that, all non-alphabetic characters (e.g., dates) are removed to eliminate their influence on article matching. The retrieval process starts by querying the training corpus with a source article to find a few (5 to 30) related articles, the summaries of which will be treated as candidate templates.
The above retrieval process is essentially based on superficial word matching and cannot measure the deep semantic relationship between two articles. Therefore, the Fast Rerank module is developed to identify a best template from the candidates based on their deep semantic relevance with the source article. We regard the candidate with highest relevance as the template. As illustrated in Figure FIGREF6 , this module consists of a Convolution Encoder Block, a Similarity Matrix and a Pooling Layer.
Our framework includes three key modules: Retrieve, Fast Rerank, and BiSET. For each source article, Retrieve aims to return a few candidate templates from the training corpus. Then, the Fast Rerank module quickly identifies a best template from the candidates. Finally, BiSET mutually selects important information from the source article and the template to generate an enhanced article representation for summarization.
This module starts with a standard information retrieval library to retrieve a small set of candidates for fine-grained filtering as cao2018retrieve. To do that, all non-alphabetic characters (e.g., dates) are removed to eliminate their influence on article matching. The retrieval process starts by querying the training corpus with a source article to find a few (5 to 30) related articles, the summaries of which will be treated as candidate templates.
The above retrieval process is essentially based on superficial word matching and cannot measure the deep semantic relationship between two articles. Therefore, the Fast Rerank module is developed to identify a best template from the candidates based on their deep semantic relevance with the source article. We regard the candidate with highest relevance as the template. As illustrated in Figure FIGREF6 , this module consists of a Convolution Encoder Block, a Similarity Matrix and a Pooling Layer.
Answer: The answers are shown as follows:
* For each source article, Retrieve aims to return a few candidate templates from the training corpus. Then, the Fast Rerank module quickly identifies a best template from the candidates.
Question: What is muscular hypertrophy?
Answer: Muscular hypertrophy is the process of increasing the size of muscles.
Question: What is a proper noun?
Answer: A proper noun is a noun that identifies an entity and is used to reference that entity. For example, India is a proper noun.
Question: What university did Ted Ringwood join and what discipline did he follow ?
Evidence: Ted Ringwood was born in Kew, only child of Alfred Edward Ringwood. He attended Hawthorn West State School where he played cricket and Australian Rules football. In 1943 he was successful in gaining a scholarship to Geelong Grammar School where he boarded. On matriculation, he enrolled in Geology a science degree at the University of Melbourne where he held a Commonwealth Government Scholarship, and was awarded a resident scholarship at Trinity College. He represented the college and the university in football. He obtained First Class Honours degree in Geology and began a MSc degree in field-mapping and petrology of the Devonian Snowy River volcanics of northeastern Victoria, graduating with Honours in 1953. Ringwood then undertook a PhD, beginning an experimental study about the origin of metalliferous ore deposits, but later changed his research topic so as to apply geochemistry to an understanding of the structure of the Earth, in particular the mineralogical constitution of the Earth's mantle
Answer: Ted Ringwood joined the University of Melbourne and he took a Geology science degree
Question: Extract the majors of Suzanne Collins from the text.  Separate them with a comma.
Evidence: Collins graduated from the Alabama School of Fine Arts in Birmingham in 1980 as a Theater Arts major. She completed her bachelor of arts degree from Indiana University Bloomington in 1985 with a double major in theater and telecommunications. In 1989, Collins earned her Master of Fine Arts in dramatic writing from the New York University Tisch School of the Arts.
Answer: Theater Arts, Theater, Telecommunications, Fine Arts in dramatic writing
Question: classify these as kitchen or bathroom items: toothpaste, pan, pots, spoons, shampoo, dish soap, bath soap, bath bombs, plates, oven mitts, cups, toilet paper
Answer: Kitchen items: pan, pots, spoons, dish soap, plates, oven mitts, cups
Bathroom items: toothpaste, shampoo, bath soap, bath bombs, toilet paper
Question: Why is better out-of-distribution generalization for smoother minimum expected？what is the motivation/context for investigating Conjecture 1?
Evidence: Unsupervised Domain Adaptation: It refers to a class of methods that aim to adapt models to work in a target domain distinct from what it was trained on. One of the most prominent lines of work is based on DAT. This involves using an additional discriminator to distinguish between samples of source and target domain. The goal of the model is to learn features that can not be distinguished between source and target. The follow-up works have improved this basic idea by introducing a class information based discriminator (CDAN), introducing a transferable normalization function  etc. In this work, we focus on analyzing and improving such methods. Another line of work involves DA by using self-training on target domain which will not be the focus of this work.
As neural networks operate in the regime of over parameterized models, low error on training data does not always lead to better generalization . Often it has been stated) that smoother minima does generalize better on unseen data. But until recently, this was practically expensive as smoothing required additional costly computations. Recently, a method called Sharpness Aware Minimization (SAM) has been proposed to find a smoother minima with an additional gradient computation step. SAM also improves the ImageNet model performance on ImageNet-C and ImageNet-R (which are out of distribution). It has also been observed that smoothness w.r.t. input (image) is beneficial for domain adaptation, which motivates us to explore smoothness w.r.t weights (W) in case of DAT. However, the earlier work has focused on achieving a smoother minima w.r.t. W for ERM. Currently, no study has been done if the loss function is composed of both ERM and adversarial objectives (as present in DAT).
SAM also improves the ImageNet model performance (Chen et al., 2021) on ImageNet-C and ImageNet-R (which are out of distribution). It has also been observed that smoothness w.r.t. input (image) is beneficial for domain adaptation (Shu et al., 2018; Cai et al., 2021), which motivates us to explore smoothness w.r.t weights (W) in case of DAT. However, the earlier work has focused on achieving a smoother minima w.r.t. W for ERM.
Answer: Converging to smooth minima is shown to have generalize models robust to corruptions (which are out of distribution) in comparison to SGD [1]. [1] shows significant improvement in performance on both ImageNet-C and ImageNet-R (both out of distribution datasets). Also, smoothness in image space has been shown to be beneficial for domain adaptation [2]. We aim to explore the smoothness in weight space and its effect on domain adaptation in this current work.
Question: Describe a plan for a road trip from Las Vegas to West Grand Canyon, including stops for visiting Hoover Dam and lunch. Your plan should include a budget, a timeline, and a description of the sights, for a group of 7 friends.
Answer: That's a great road trip! A group of 7 people can fit perfectly on a 7 seat car that you can rent in one of the many providers in Las Vegas. Start your day early in the morning to get the car and pick up your friends. From there your first destination is going to be the Hoover Dam, around 40 minutes by car from Las Vegas. Take your time to take pictures from the bridge and then go the dam, you can visit inside, the self-guided ticket is $10. You will be there for a couple of hours.

After these you need to go south east and take a turn to Dolan Springs, this will be an hour driving, so by that time you are around lunch time, which will cost you approximately $15.
From Dolan Sprints it's a straight 50 minutes ride to the West Grand Canyon. In the road you will find midway the Arizona's Joshua Tree Forest. Take the time to appreciate it, as well as the desert along the way.

When you arrive at the West Grand Canyon, you can buy the $70 general admission ticket + Skywalk. This gives you unlimited access for the day to Eagle Point, Guano Point, Hualapai Ranch and the Skywalk experience. You can walk the sky on the 10-foot wide, horseshoe-shaped glass bridge that extends 70 feet over the rim of the Grand Canyon.

When leaving the West Grand Canyon the trip back to Las Vegas takes 2h. You will need to refill the tank, before returning the vehicle to the car renting company. The renting for the day and the gas will cost around $35 per person.

Depending on your pace, this is a morning and afternoon road trip. With an estimated cost of $130 per person.
Question: What is the tallest mountain in Colorado?
Answer: Mount Elbert is the tallest mountain in Colorado standing at 14,439 feet
Question: How do you motivate and focus a team to exceed expectations during a terrible economic downturn?
Answer: Set a goal, vision, create plans to execute, don’t over complicate it. Win as a team and lose as team.
Question: Given a reference text about the Bakersfield Majestics, where did the team play its games?
Evidence: The Bakersfield Majestics, formerly the Bakersfield Magic, are an American basketball team based in Bakersfield, California, and members in the minor professional The Basketball League (TBL).

Starting The Franchise
From 2006 to 2016, Bakersfield, California, was home to the Bakersfield Jam of the NBA Development League. The team played at Rabobank Arena until 2009 and later at the Jam Events Center. On May 9, 2014, the Jam entered a hybrid affiliation with the Phoenix Suns, giving the Suns exclusive affiliation with the team while allowing the Jam to operate under their own management. On April 12, 2016, the Suns announced that the organization had purchased the Jam and were relocating the franchise to the town of Prescott Valley, Arizona, for the 2016–17 season to become the Northern Arizona Suns. In response, the previous owners of the Jam franchise, Stan Ellis and David Higdon, announced that they had been working with the D-League in securing a new franchise and affiliation before the 2016–17 season, although it did not come to fruition.

In June 2021, it was first reported by KGET-TV that Bakersfield would host the Bakersfield Magic in The Basketball League for the 2022 season. On August 5, 2021, it was announced that Spencer Norman, previously announced as the team market owner for the Fresno Fire, would be the team market owner. Karaya Gage was initially announced as the team's first head coach, making her the first African-American woman to serve as Head Coach of a men's professional basketball team. However, after coaching the team during a pre-season exhibition against members of the Bakersfield Police Department on January 29, 2022, Gage quietly left the organization. The team then announced that Peter Pang would serve as the new Head Coach, becoming the first Chinese-American to coach a professional basketball team in the United States. Lawrence "Doc" Holliday was the team's initial General Manager. However, the team had announced on March 22, 2022, that Holliday had died.

On April 1, 2022, The Bakersfield Magic announced that L. Dee Slade would take over as the new General Manager of the team.
Answer: The Bakersfield Majestics played its games at Rabobank Arena until 2009 and then the Jam Events Center in Bakersfield, California.
Question: What are the unique characteristics of the Jindo dog breed?
Answer: The Jindo breed of dog is indigenous to the island of Jindo off the Southern coast of South Korea. They are typically between 30 and 50 pounds fully grown and vary from a white to a golden brown color. Listed as of the living natural treasures of South Korea, they are known for being intelligent, loyal, and having a keen ability to find their way home.
Question: What is Linux?
Answer: Linux is an open-source UNIX-like operating system initially developed by Linus Torvalds and now with thousands of contributors around the world. Linux is actually the operating system kernel and the other components necessary to form an operating system are coming from other open source projects and in particular the GNU project. For this reason, the operating system is sometimes referred to as GNU/Linux.
Question: Give me a list of most popular songs of Foo Fighters
Answer: 1. Everlong
2. The Pretender
3. Best of You
4. Times like These
5. My Hero
6. Monkey Wrench
7. All My Life
8. Learn to Fly
9. Walk
10. The Sky Is a Neighbourhood
11. These days
12. Something from Nothing
13. Run
14. Stranger Things Have Happened
15. Rope
Question: How large is the first dataset?
Evidence: The two datasets used for the challenge are Friends and EmotionPush, part of the EmotionLines corpus BIBREF4. The datasets contain English-language dialogues of varying lengths. For the competition, we provided 1,000 labeled dialogues from each dataset for training, and 240 unlabeled dialogues from each dataset for evaluation. The Friends dialogues are scripts taken from the American TV sitcom (1994-2004). The EmotionPush dialogues are from Facebook Messenger chats by real users which have been anonymized to ensure user privacy. For both datasets, dialogue lengths range from 5 to 24 lines each. A breakdown of the lengths of the dialogues is shown in Table .
For the competition, we provided 1,000 labeled dialogues from each dataset for training, and 240 unlabeled dialogues from each dataset for evaluation. 
Answer: 1 000 labeled dialogues for training and 240 unlabeled dialogues for evaluation
Question: What is an Aeolosaurini?
Evidence: Aeolosaurini is an extinct clade of titanosaurian dinosaurs known from the Cretaceous period of Argentina and Brazil. Rodrigo M. Santucci and Antonio C. de Arruda-Campos (2011) in their cladistic analysis found Aeolosaurus, Gondwanatitan, Maxakalisaurus, Panamericansaurus and Rinconsaurus to be aeolosaurids.

Aeolosaurini is characterized by several synapomorphies of the caudal vertebrae, such as angled centra, elongate prezygapophyses, and neural arches shifted anteriorly relative to the centra. In life, their tails may have been strongly curved downward as a result of these traits, which may have increased the force exerted by the caudofemoralis longus muscle in retracting the hindlimb. Some aeolosaurins, such as Shingopana and Overosaurus, were relatively small compared to other titanosaurs, whereas others, such as Aeolosaurus maximus, were large.
Answer: Aeolosaurini is an extinct clade of titanosaurian dinosaurs known from the Cretaceous period of Argentina and Brazil. Aeolosaurini is characterized by several synapomorphies of the caudal vertebrae, such as angled centra, elongate prezygapophyses, and neural arches shifted anteriorly relative to the centra.
Question: What are the primary causes of inflation?
Answer: There is often a decrease in the quality of money (currency value) when the money supply increases too rapidly in an economy.
In general, economists believe that increased money supply (monetary inflation) causes price increases over the long run, but disagree on causes for short-term inflation.
Question: Provide a short review for a Japanese restaurant called Honto in Brisbane. It was a little hard to find as it is in a backstreet, but we found it without too much trouble. Be sure to mention it was very atmospheric and that the food was great.
Answer: Dining at Honto was an especially memorable experience for us. The space was atmospheric and the Japanese food served was exceptional. It is surely one of Brisbane's best kept secrets.
Question: Extract who was Karl Lagerfeld
Evidence: Karl Otto Lagerfeld (German pronunciation: [kaʁl ˈʔɔtoː ˈlaːɡɐˌfɛlt] (listen); 10 September 1933 – 19 February 2019) was a German fashion designer, creative director, artist and photographer.

He was known as the creative director of the French fashion house Chanel, a position held from 1983 until his death, and was also creative director of the Italian fur and leather goods fashion house Fendi, and of his own eponymous fashion label. He collaborated on a variety of fashion and art-related projects.
Answer: Karl Lagerfeld was one of the world's premier fashion designers who rose to international fame in the 1980s, as the creative director of the French fashion house Chanel.  He was also the creative director of Fendi and had his own fashion label.  Early in his career, he was the assistant to Pierre Balmain and became the creative director of Jean Patou in 1958.  He collaborated with many high end fashion labels such as Krizia, Charles Jourdan, Chloe and Valentino.