DataHammer/scimrc huggingface dataset:
Question: Are there any limitations in the existing approaches?
Evidence: Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%, 1.31%, respectively.
Recently, neural networks based on multitask learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks.
Answer: The shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks.
Question: What is the tallest building in Chicago?
Answer: The tallest building in Chicago is the Sears Tower
Question: What dataset do they use?
Evidence: Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). While our main goal is to verify our hypotheses on a large data condition, thus the need to include proprietary data, for the sake of reproducibility in both languages we also provide results with systems only trained on TED Talks (small data condition). When training on large scale data we use Transformer with layer size of 1024, hidden size of 4096 on feed forward layers, 16 heads in the multi-head attention, and 6 layers in both encoder and decoder. When training only on TED talks, we set layer size of 512, hidden size of 2048 for the feed forward layers, multi-head attention with 8 heads and again 6 layers in both encoder and decoder.
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De).
Answer: The answers are shown as follows:
* English$\rightarrow $Italian/German portions of the MuST-C corpus
* As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)
Question: I follow you all the time and copy your every move, but you can’t touch me or catch me. What am I?
Answer: I am your shadow.
Question: In which country is Jean Marie Ralph Féthière a politician?
Evidence: Jean Marie Ralph Féthière is a Haitian politician. He is a Senator from the north and a member of the ruling PHTK party. On September 23, 2019, while trying to leave Parliament amid a crowd as the government was voting to confirm a new prime minister, Féthière drew a handgun and fired toward the crowd. Chery Dieu-Nalio, a photographer for the Associated Press, suffered injuries to his face from bullet fragments, while a security guard named Leon Leblanc was also injured.
Answer: Haiti
Question: Who is J.K.Rowling?
Evidence: Joanne Rowling CH OBE FRSL (/ˈroʊlɪŋ/ "rolling"; born 31 July 1965), also known by her pen name J. K. Rowling, is a British author and philanthropist. She wrote Harry Potter, a seven-volume children's fantasy series published from 1997 to 2007. The series has sold over 600 million copies, been translated into 84 languages, and spawned a global media franchise including films and video games. The Casual Vacancy (2012) was her first novel for adults. She writes Cormoran Strike, an ongoing crime fiction series, under the alias Robert Galbraith.
Answer: Joanne Rowling a.k.a J.K.Rowling is the famous author of Harry Potter books.
Question: What is the best kind of shade tree for a backyard in growing region 6?
Answer: There are many great trees homeowners in growing region 6 may want to consider for their backyard depending on their priorities. 

First, the two main types of trees are deciduous, trees that lose their leaves each winter, and evergreen, which retain their foliage year round. Many of the most popular shade trees are deciduous trees, however there are some great evergreen options as well.

Starting with deciduous trees, there are flowering trees and traditional shade trees. Of the traditional shade trees, you may want to consider maples and oaks, of which there are many varieties. Red maples grow a bit slower than silver maples, but have hardier bark, beautiful red leaves in the fall, and a more pleasing traditional branching habit that many prefer vs, Silver maples. Sugar maples growth slower still, but are beautiful, prized trees that turn glowing orange in the fall and produce the sap used in maple syrup come spring. These trees can be 40-70 feet tall at maturity. Oaks are incredibly beautiful trees that are typically a little slower growing than maples, but have strong bark, enabling them to survive harsher conditions and have a long lifespan. They can handle being used as street trees as they do fine with moderate pollution levels and salt from de-icing. You really can't go wrong with maples or oaks as a summer shade tree, with their tall canopies providing wonderful shade once their leaves fill in during the spring.

Some popular flowering options that provide ample shade include cherry and magnolia trees, both of which have beautiful showy spring flowers and reach a height of 20-60 feet at maturity depending on which of the many variants you choose (be careful with this as some are best suited to warmer climates and don't tolerate the cold as well). 

For evergreens, the options are a bit more limited as many of these trees have low canopies that extend all the way to the ground. Many species of spruce and fir tree can have their canopies raised as they mature, creating space underneath for shade. Some pine trees have ample canopy under them, however their thin needle density can limit the amount of shade they provide.

Whichever you ultimately choose, Shade trees add a ton of value to a home and are a great investment. Consider working with an arborist to make the decision based on your lighting and soil conditions. Enlisting a professional's help with planting will ensure your new tree is set up to grow healthy and strong.
Question: What is the state-of-the-art neural coreference resolution model?
Evidence: We use the English coreference resolution dataset from the CoNLL-2012 shared task BIBREF15 , the benchmark dataset for the training and evaluation of coreference resolution. The training dataset contains 2408 documents with 1.3 million words. We use two state-of-art neural coreference resolution models described by BIBREF2 and BIBREF1 . We report the average F1 value of standard MUC, B INLINEFORM0 and CEAF INLINEFORM1 metrics for the original test set.
We use two state-of-art neural coreference resolution models described by BIBREF2 and BIBREF1 .
Answer: The answers are shown as follows:
* BIBREF2 
* BIBREF1
Question: who won the oscar 2023 for best original song music
Answer: M.M.Keeravani , Chnadrabose won the oscar for Naatu Naatu song  from film RRR. RRR is one the biggest and top grosser in Indian films and got various applauds internationally. RRR was directed by S.S.Rajamouli one of the greatest Director who directed films like Baahubali which is the one of the biggest periodic drama. Ramcharan and NTR are the lead actors in RRR.
Question: What is a computer network?
Answer: A computer network are groups of computers than can communicate with each other through a LAN or Local Area Network.  In the early days of networking they all had to be hard wired and with the development of newer technology networks could communicate and be setup over wireless.  Computer networks also share data and devices with each other.  For example a shared printer can be used by multiple devices on the same computer network.
Question: Tell me whether these are indoor or outdoor games. 
Cricket, Table Tennis, Soccer, Carom
Answer: Cricket- Outdoor Game, Table Tennis - Indoor Game, Soccer- Outdoor Game, Carom - Indoor Games
Question: Classify each of the following workouts as upper body or lower body: bench press, pull-up, squat, rows, leg extension, calf raise
Answer: Bench Press: Upper Body
Pull-up: Upper Body
Squat: Lower Body
Rows: Upper Body
Leg Extension: Lower Body
Calf Raise: Lower Body
Question: was bert used?
Evidence: BERT BIBREF6 is a state-of-the-art model, which has been pre-trained on a large corpus and is suitable to be fine-tuned for various downstream NLP tasks. The main innovation between this model and existing language models is in how the model is trained. For BERT, the text conditioning happens on both the left and right context of every word and is therefore bidirectional. In previous models BIBREF7, a unidirectional language model was usually used in the pre-training. With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).
For this study, the NSP is used as a proxy for the relevance of response. Furthermore, in order to improve performance, we fine-tune on a customized dataset which achieved an accuracy of 82.4%. For the main analysis, we used the single-turn dataset, which gave us a correlation of 0.28 between the mean of the AMT evaluation and the BERT NSP. Next, we put each score into a category. For example, if the average score is 2.3, this would be placed in category 2. We then displayed the percentage of positive and negative predictions in a histogram for each of the categories. As seen in Figure FIGREF5, a clear pattern is seen between the higher scores and the positive prediction, and the lower scores and the negative predictions. details of how they are combined to create a final classification layer.
With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).

For this study, the NSP is used as a proxy for the relevance of response.
Answer: Yes.
Question: What is the most effective way to clean your floors?
Answer: Using the O-Cedar EasyWring Microfiber Spin Mop and Bucket Cleaning System, fill the bucket with hot water and 1 TSP of powdered tide. It will leave your floors super clean and smelling good. For deeper cleans, you can add a bit of distilled white vinegar.
Question: How to estimate the semantic similarity?
Evidence: Semantic similarity can be estimated between words, between phrases, between sentences, and between documents in a corpus. Estimation could either be based upon corpus text alone by applying approaches like vector space modeling BIBREF16, latent semantic analysis BIBREF17, topic modeling BIBREF18, or neural embeddings (e.g., Word2Vec BIBREF19 or Glove BIBREF20) and their extensions to phrase, sentence, and document levels. Otherwise it can be estimated based upon ontological relationships (e.g., WordNet based BIBREF21) among concept terms appearing in the corpus.
Semantic similarity can be estimated between words, between phrases, between sentences, and between documents in a corpus.
Answer: Semantic similarity can be estimated between words, between phrases, between sentences, and between documents in a corpus.
Question: What is the best bike setup for Zwift's Three Little Sisters tour?
Answer: I recommend a lightweight bike with good climbing capabilities for this route, such as the Specialized Tarmac, Canyon Ultimate, or Trek Emonda. These bikes offer a good balance of speed and climbing ability, which can be helpful on the steep ascents of the Three Little Sisters tour. The route will first tackle the Hilly KOM, Titan's Grove KOM, and finally, finish on the Volcano KOM. In terms of wheels, a set of lightweight climbing wheels such as the DT Swiss disc or Zipp 858's could be a good option, as they offer reduced weight and improved aerodynamics.
Question: For what is known as Transphonologization?
Evidence: In historical linguistics, transphonologization (also known as rephonologization or cheshirization, see below) is a type of sound change whereby a phonemic contrast that used to involve a certain feature X evolves in such a way that the contrast is preserved, yet becomes associated with a different feature Y.
Answer: In historical linguistics, transphonologization (also known as rephonologization or cheshirization, see below) is a type of sound change whereby a phonemic contrast that used to involve a certain feature X evolves in such a way that the contrast is preserved, yet becomes associated with a different feature Y.

For example, a language contrasting two words */sat/ vs. */san/ may evolve historically so that final consonants are dropped, yet the modern language preserves the contrast through the nature of the vowel, as in a pair /sa/ vs. /sã/. Such a situation would be described by saying that a former contrast between oral and nasal consonants has been transphonologized into a contrast between oral and nasal vowels.

The term transphonologization was coined by André-Georges Haudricourt. The concept was defined and amply illustrated by Hagège & Haudricourt; it has been mentioned by several followers of panchronic phonology, and beyond.
Question: Tell me whether each of these is a drug or food: marijuana, fried chicken, alcohol, caffeine, sushi, mango
Answer: Marijuana: Drug
Fried Chicken: Food
Alcohol: Drug
Caffeine: Drug
Sushi: Food
Mango: Food
Question: Why was Tingri important for Nepalese people based on the following text?
Evidence: Gangga (or Tingri according to name of region) (Tibetan: སྒང་དགའ་, Wylie: sgang dga'; Chinese: 岗嘎镇; pinyin: Gǎnggā Zhèn) is a town in Tingri County, in the south of the Tibet Autonomous Region. It has a population of around 523, and is often used as a base by mountain climbers preparing to ascend Mount Everest or Cho Oyu. It is known[citation needed] for its views of Mount Everest, Mount Lhotse, Mount Makalu, and Cho Oyu, which comprise four of the six highest mountains in the world.

Tingri used to be an important trading post where Sherpas from Nepal exchanged rice, grain and iron for Tibetan wool, livestock and salt. It gives its name to the more than 4,500-meter (14,800 ft)-high broad upland basin that is known as the Tingri Plain. One must cross the pass known as the Lak Pa La (el. 5,220 meters (17,130 ft)) to the north to reach the Tsangpo Valley system. Shallow, fast-flowing rivers of melted snow water make its grassy meadowland ideal for grazing by Tibetan animals. The plain used to abound with gazelles, blue sheep, antelopes and khyang or wild asses but most of the animals are gone now. The Chinese army has a small base nearby.

Tingri Lankor (Ding ri glang 'khor) — Padampa's Residence was founded in 1097 CE by the South Indian Buddhist adept, Padampa Sangye (died 1117) who made five visits to Tibet and was an important person in the re-establishment of the Buddhadharma in Tibet. His consort was the Tibetan dakini Machik Labdron. The gompa or temple was built in the Padampa Sange's meditation cave and became the seat of the Dampapa School of Tibetan Buddhism. It is in the process of restoration.
Answer: Tingri was used as a trading post by Sherpas from Nepal as a means for exchanging rice, grain and iron in return for Tibetan wool, livestock and salt.
Question: Extract the names of the stage shows Dixie Violet Egerickx starred in and the role she played in them. List them using bullets in the format {Stage} - {Role}
Evidence: Dixie Violet Egerickx (born 31 October 2005) is an English actress. Egerickx has appeared three times on the London stage, as Iphigenia in Robert Icke's adaptation of Oresteia, Rosalind in Alexi Kay Campbell's Sunset at the Villa Thalia at the National Theatre and as Jenny Caroline 'Qui Qui' Marx in Richard Bean and Clive Coleman's Young Marx at The Bridge Theatre, directed by Sir Nicholas Hytner. She appeared in the 2017 National Geographic series Genius and the 2018 television series Patrick Melrose making her feature film debut in the 2018 film The Little Stranger. Egerickx was named in Screen International's Screen Stars of Tomorrow list in 2019 at the age of 13. She starred as Edie in the 2020 drama film Summerland, and portrayed Mary Lennox in the 2020 film The Secret Garden. In 2019 she filmed HBO's Unaired Game of Thrones Prequel Pilot and most recently has completed filming the role of Jo Ransome in the Apple TV miniseries of The Essex Serpent, directed by Clio Barnard.
Answer: Oresteia - Iphigenia
Sunset at the Villa Thalia - Rosalind
Young Marx - Jenny Caroline 'Qui Qui' Marx
Question: Tell me which of these is a Country, a City, a River, or a Mountain: New York, Nile, Amazon, Kilimanjaro, USA, Spain, Italy, Rome, Prague, Zurich, France, San Francisco, K2, Buenos Aires, Brazil, Mount Everest, London, Mekong
Answer: New York - City
Nile - River
Amazon - River
Kilimanjaro - Mountain
USA - Country
Spain - Country
Italy - Country
Rome - City
Prague - City
Zurich - City
France - Country
San Francisco - City
K2 - Mountain
Buenos Aires - City
Brazil - Country
Mount Everest - Mountain
London - City
Mekong - River
Question: What kind of milk is Vietnamese coffee often served with?
Answer: Vietnamese coffee is often brewed over sweetened condensed milk.
Question: Where was his Catholic school?
Evidence: Waring was born John Roderick Rodriguez in Lewisham, London, into a respectable family of Portuguese ancestry, and attended a Catholic school in Southwark.
Answer: Southwark
Question: Write a bulleted list of the plants that grow around Stanwell-Fletcher Lake.
Evidence: Stanwell-Fletcher Lake is the largest lake on Somerset Island, the tenth-largest island of the Canadian Arctic Archipelago. The lake, along with most of Somerset Island, is located within the Qikiqtaaluk Region of the Canadian territory of Nunavut.

The harsh tundral climate and minimal soil cover of the highlands surrounding the lake allows only a few types of plants to grow there, mostly lichens, mosses, and arctic willow. Vegetation is more prominent in the thermokarst lowlands to the north of the lake, which are dominated by sedges during the summer. The low-lying areas to the southeast of the lake, and the nearby Creswell Bay, host a Canadian Important Bird Area. The area supports large bird populations during the summer, especially breeding populations of various shorebirds. 13 shorebird species have been observed in the area, while 11 are known to breed there. These species includes the white-rumped sandpiper, red phalarope, black-bellied plover, sanderling, American golden plover, ruddy turnstone, Baird's sandpiper, buff-breasted sandpiper, pectoral sandpiper, semipalmated sandpiper, and red knot. Other birds with populations in the area include the snow goose, king eider, long-tailed duck, northern fulmar, black-legged kittiwake, and peregrine falcon. Stanwell-Fletcher Lake is also a large and important habitat for anadromous populations of arctic char, which spend summer in coastal areas including Creswell Bay and surrounding waters, but migrate via the Union River to in the lake before it freezes over. Fourhorn sculpin are also present within the lake.
Answer: * lichens
* mosses
* arctic willow
Question: Given this paragraph about the Last of Us TV Series, who was not cast in the show?
Evidence: Casting took place virtually through Zoom due to the COVID-19 pandemic. Casting director Victoria Thomas wanted to honor the game without being limited by it. Mazin and Thomas sought high-profile guest stars; Thomas said many of the actors "don't usually do one-episode guest spots". On February 10, 2021, Pascal and Ramsey were cast as Joel and Ellie. Earlier that day, it was reported Mahershala Ali was offered the role of Joel after Matthew McConaughey turned it down; The Hollywood Reporter noted Ali "did circle a role" in the show, but a deal was never formed. Any actresses considered for Ellie for the canceled film adaptation—such as Maisie Williams and Kaitlyn Dever—had aged out of consideration by the time the series was in production, resulting in a reset of candidates. The producers primarily sought actors who could embody Joel and Ellie individually as well as imitate their relationship.: 14:42  Though both were previously featured on HBO's Game of Thrones, Pascal and Ramsey had not met before the filming of The Last of Us began but found they had instant chemistry, which developed over the course of production.
Answer: Matthew McConaughey turned down the role of Joel which was also reportedly offered to and turned down by Mahershala Ali afterwards. Maisie Williams and Kaitlyn Dever were considered for the role of Ellie but aged out of consideration by the time of production.
Question: What is beta oxidation?
Answer: In biochemistry and metabolism, beta oxidation (also β-oxidation) is the catabolic process by which fatty acid molecules are broken down in the cytosol in prokaryotes and in the mitochondria in eukaryotes to generate acetyl-CoA, which enters the citric acid cycle, and NADH and FADH2, which are co-enzymes used in the electron transport chain. It is named as such because the beta carbon of the fatty acid undergoes oxidation to a carbonyl group. Beta-oxidation is primarily facilitated by the mitochondrial trifunctional protein, an enzyme complex associated with the inner mitochondrial membrane, although very long chain fatty acids are oxidized in peroxisomes.
Question: How do the authors measure how temporally dynamic a community is?
Evidence: Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable.
. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . 
Answer: The answers are shown as follows:
* the average volatility of all utterances
Question: Who created the game of basketball?
Answer: Basketball was invented by Dr. James Naismith, a canadian physical education teacher in winter of 1891. The game was invented to keep his students active during the Winter months.