DataHammer/scimrc huggingface dataset:
Question: What was the last year the Ford Pinto was manufactured
Evidence: The Ford Pinto is a subcompact car that was manufactured and marketed by Ford Motor Company in North America from 1971 until 1980 model years. The Pinto was the first subcompact vehicle produced by Ford in North America.
Answer: Ford stopped manufacturing the Pinto in 1980
Question: Please list all the emperors in the following passage
Evidence: The political unrest began with the murder of Emperor Commodus on New Year's Eve 192. Once Commodus was assassinated, Pertinax was named emperor, but immediately aroused opposition in the Praetorian Guard when he attempted to initiate reforms. They then plotted his assassination, and Pertinax was killed while trying to reason with the mutineers. He had only been emperor for three months. Didius Julianus, who purchased the title from the Praetorian Guard, succeeded Pertinax, but was ousted by Septimius Severus and executed on June 1. Severus was declared Caesar by the Senate, but Pescennius Niger was hostile when he declared himself emperor. This started the civil war between Niger and Severus; both gathered troops and fought throughout the territory of the empire. Due to this war, Severus allowed Clodius Albinus, whom he suspected of being a threat, to be co-Caesar so that Severus did not have to preoccupy himself with imperial governance. This move allowed him to concentrate on waging the war against Niger. Most historians count Severus and Albinus as two emperors, though they ruled simultaneously. The Severan dynasty was created out of the chaos of AD 193.
Answer: Commodus, Pertinax, Didius Julianus, Septimius Severus, Clodius Albinus
Question: What is the difference between an adverb and adjective?
Answer: An adjective describes an item, whereas an adverb modifies a verb
Question: How long was the Titanic?
Answer: The RMS Titanic was 882 feet long. The Titanic was considered unsinkable; however a collision with an iceberg led to its tragic demise and it sunk on April 14, 1912.
Question: Tell me which one does not fit with the group:  Cigar, Cigarette, Tobacco, Pipe, Hookah
Answer: All except Tobacco are means of smoking.
Question: Walter Koenig played which part in the Star Trek series
Answer: Ensign Chekov
Question: Classify these as faster or slower than driving a car: running, flying a plane, riding a bike, walking, driving a boat.
Answer: Running, walking, and riding a bike will typically be slower than driving a car. Flying a plane will typically be faster than driving a car. Driving a boat may be slower or faster than driving a car, depending on where you’re going and whether a body of water might provide a more direct route.
Question: How do they handle polysemous words in their entity library?
Evidence: Figure FIGREF54 shows an exemplary process of converting the first paragraph of a Polish Wikipedia entry, describing former Polish president Lech Wałęsa, into a list of WordNet synsets. First, we omit all unessential parts of the paragraph (1). This includes text in brackets or quotes, but also introductory expressions like jeden z (one of) or typ (type of). Then, an entity name is detached from the text by matching one of definition patterns (2). In the example we can see the most common one, a dash (–). Next, all occurrences of separators (full stops, commas and semicolons) are used to divide the text into separate chunks (3). The following step employs shallow parsing annotation – only nominal groups that appear at the beginning of the chunks are passed on (4). The first chunk that does not fulfil this requirement and all its successors get excluded from further analysis (4.1). Finally, we split the coordination groups and check, whether their lemmas correspond to any lexemes in WordNet (5). If not, the process repeats with the group replaced by its semantic head. In case of polysemous words, only the first word sense (usually the most common) is taken into account.
In case of polysemous words, only the first word sense (usually the most common) is taken into account.
Answer: The answers are shown as follows:
* only the first word sense (usually the most common) is taken into account
Question: From the given text, can you please extract all possible technical details of the aeroplane in the JSON format {key:value} where key is the attribute and value is the data for the attribute.
Evidence: The aircraft involved in the accident was a 4-year-old Airbus A330-203, with manufacturer serial number 660, registered as F-GZCP. Its first flight was on 25 February 2005, and it was delivered 2 months later to the airline on 18 April 2005. At the time of the crash, it was Air France's newest A330. The aircraft was powered by two General Electric CF6-80E1A3 engines with a maximum thrust of 68,530 or 60,400 lbf (304.8 or 268.7 kN) (take-off/max continuous), giving it a cruise speed range of Mach 0.82–0.86 (470–493 knots or 870–913 kilometres per hour or 541–567 miles per hour), at 35,000 feet (11,000 m) of altitude and a range of 12,500 km (6,700 nmi; 7,800 mi). The aircraft underwent a major overhaul on 16 April 2009, and at the time of the accident had accumulated about 18,870 flying hours.
Answer: Here are the technical details of the aeroplane that I could parse and format it in JSON format {key:value}.

{
"age in years":"4",
"aircraft manufacturer":"Airbus",
"aircraft model":"A330-203",
"manufacturer serial number":"660",
"aircraft registration number":"F-GZCP",
"first flight date":"25 February, 2005",
"airline delivery date":"18 April, 2005",
"airline name":"Air France",
"aircraft engine type":"General Electric CF6-80E1A3",
"aircraft engine maximum thrust":"60400",
"aircraft engine maximum thrust unit":"lbf",
"aircraft cruise speed range":"Mach0.82-0.86",
"aircraft cruise speed attitude":"35000",
"aircraft cruise speed attitude unit":"feet",
"aircraft range":"12500",
"aircraft range unit":"km",
"aircraft major overhaul date":"16 April, 2009",
"aircraft accumulated flying hours":"18870"
}
Question: What type of democracy is Nigeria?
Answer: Nigeria is a federal republic. A democratic constitution with a federal system based on the United States of America. There is both a national government and governments for each of Nigeria's 36 states. Nigeria's President is the head of state, government and Nigeria's multi-party system.
Question: What are mammals?
Answer: Mammals are animals that have vertebrae, give milk to their babies, have hair or fur, and are warm-blooded. There are seven types of mammals: rodentia, chiroptera, soricomorpha, primates, carnivora, artiodactyla, diprotodontia, and lagomorpha.
Question: Does increasing the amount of monolingual unlabeled data improve SRI both in German and English?
Evidence: We propose a Bayesian model of semantic role induction (SRI) that uses crosslingual latent variables to capture role alignments in parallel corpora. The crosslingual latent variables capture correlations between roles in different languages, and regularize the parameter estimates of the monolingual models. Because this is a joint Bayesian model of multilingual SRI, we can apply the same model to a variety of training scenarios just by changing the inference procedure appropriately. We evaluate monolingual SRI with a large unlabeled dataset, bilingual SRI with a parallel corpus, bilingual SRI with annotations available for the source language, and monolingual SRI with a small labeled dataset. Increasing the amount of monolingual unlabeled data significantly improves SRI in German but not in English. Adding word alignments in parallel sentences results in small, non significant improvements, even if there is some labeled data available in the source language. This difficulty in showing the usefulness of parallel corpora for SRI may be due to the current assumptions about role alignments, which mean that only a small percentage of roles are aligned. Further analyses reveals that annotating small amounts of data can easily outperform the performance gains obtained by adding large unlabeled dataset as well as adding parallel corpora.
Future work includes training on different language pairs, on more than two languages, and with more inclusive models of role alignment.
Increasing the amount of monolingual unlabeled data significantly improves SRI in German but not in English.
Answer: No.
Question: What are the concatenation parts of the vector in the first layer of the model?
Evidence: The first layer of DFN transforms each word in the passage, question and answer candidates independently into a fixed-dimension vector. This vector is the concatenation of two parts. The first part is the pre-trained GloVe embedding BIBREF21 of each word. For each out-of-vocabulary word, we map it to an all-zero vector. The second part is the character encodings. This is carried out by mapping each character to a trainable embedding, and then feeding all characters into an LSTM BIBREF22 . The last state of this LSTM is used as the character encodings. The output of the Lexicon Encoding layer is a set of vectors for $Q,P$ and each answer candidate in $\mathcal {A}$ , respectively: $Q^\text{embed}=\lbrace q^\text{embed}_i\rbrace _{i=1}^{l_q}, P^\text{embed}=\lbrace p^\text{embed}_i\rbrace _{i=1}^{l_p}$ , and $A^\text{embed}_j=\lbrace a^\text{embed}_{i,j}\rbrace _{i=1}^{l_a^j}, j=1,2,...,r$ .
The first layer of DFN transforms each word in the passage, question and answer candidates independently into a fixed-dimension vector. This vector is the concatenation of two parts. The first part is the pre-trained GloVe embedding (Pennington et al., 2014) of each word. For each out-ofvocabulary word, we map it to an all-zero vector. The second part is the character encodings.
Answer: The pre-trained GloVe embedding and the character encodings.
Question: How was the inter-annotator agreement status？
Evidence: We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above. Initially, we asked four annotators to test our guidelines on two texts. Subsequently, identified issues were discussed and resolved. Following this pilot annotation phase, we asked two different annotators to annotate two case reports according to our guidelines. The same annotators annotated an overall collection of 53 case reports.
Inter-annotator agreement is calculated based on two case reports. We reach a Cohen's kappa BIBREF6 of 0.68. Disagreements mainly appear for findings that are rather unspecific such as She no longer eats out with friends which can be seen as a finding referring to “avoidance behaviour”.
Inter-annotator agreement is calculated based on two case reports. We reach a Cohen's kappa (Cohen, 1960) of 0.68.
Answer: They reached a Cohen's kappa (Cohen, 1960) of 0.68.
Question: Write a short letter to a dear friend who just lost their Mother, only six months after losing their father.  Both of their parents were someone you and your family were also very close with.
Answer: There are no words to express our deepest sympathy at the passing of your Mother and our dear friend.  She was the strongest woman with the biggest heart and always treated us as if we were her own.  Her passing leaves a gapping hole in our hearts but know that we find peace knowing she is free from pain and reunited with your father, who we know was waiting to greet her with open arms.  Our childhood was richer because of both of your parents and more memorable because of their love.  We are so sorry that you lost your parents so close together and will forever remember them by their deep faith, their remarkable smiles and their love for everyone that was lucky enough to know them.
Question: Tracey and Hepburn first film in 1942 was what
Answer: Woman of the Year
Question: What is used for the decoder?
Evidence: To solve the issues described above, we present Entity2Topic (E2T), a module that can be easily attached to any sequence-to-sequence based abstractive summarization model. E2T encodes the linked entities extracted from the text and transforms them into a single topic vector. This vector is ultimately concatenated to the decoder hidden state vectors. The module contains two submodules specifically for the issues presented by the entity linking systems: the entity encoding submodule with selective disambiguation and the pooling submodule with firm attention.
Overall, our full architecture can be illustrated as in Figure 2 , which consists of an entity linking system (ELS), a sequence-to-sequence with attention mechanism model, and the E2T module. We note that our proposed module can be easily attached to more sophisticated abstractive summarization models BIBREF13 , BIBREF14 that are based on the traditional encoder-decoder framework and consequently can produce better results. The code of the base model and the E2T are available online.
For the decoder, we use a two-layer unidirectional GRU with attention.
Answer: A two-layer unidirectional GRU with attention.
Question: Select the odd word from this list and explain why you chose it
Galaxy S23
Milky Way
Pinwheel Galaxy
Andromeda Galaxy
Sombrero Galaxy
Answer: Galaxy S23 is the odd one out. All names on the list are examples of galaxies that can be found across the universe, whereas the Galaxy S23 is a Samsung brand of smartphone.
Question: What makes the game Overwatch appealing?
Answer: People enjoy the game of Overwatch for several reasons. First, it is a first-person shooter (FPS) which allows you to select from a wide range of available characters or heroes. Each of these heroes provides their own set of unique abilities meaning you can always pick a new hero and play the game in a different way. Second, it is a team-based game, so every match you play will be different. There are many different strategies you can implement and they depend on the heroes that you and your team along with whatever composition the opponents have selected. Third and finally, the creators of Overwatch, Blizzard, released a new version of the game: Overwatch 2. This has introduced new heroes, maps, and a new competition mode which has made the game fun for existing and new players.
Question: Does Twitter ever break their own rules?
Answer: Yes, all the time.  Two day ago, @verified, Twitter's own verified account follows every verified users on Twitter. Today, it follows nobody. So in short, Twitter has broken their own mass unfollow restriction.
Question: In Figure 2: x1 net SHeteroFL seems to converge faster than slimmer nets till around round 50. What happens afterwards?
Evidence: For motivation, we set up a standard non-i.i.d. FL benchmark using DomainNet dataset (refer to experimental details in Section 5) and study how the budget constraint impedes effective training of customizable models. Case 1: FL without budget constraint. First, we individually train networks of different widths by FedAvg. We see that the slimmer networks converge slower and are less generalizable, even though they are trained on all clients (solid lines in Fig.).
The results justified the motivation of training wider networks than slimmer ones.
Case 2: FL with budget constraint. Following the above budget constraint, i.e., R k = (1/2) ⌈4k/K⌉ , we deploy HeteroFL to train budget-compatible prototype models locally. Since HeteroFL was not designed for model customization, data are not fully used: each model prototype is only trained by 1/4 of clients, when clients of the R k = 1 budget can actually afford all slimmer models. Therefore, we extend HeteroFL to a slimmable version (SHeteroFL) by training all affordable prototypes locally. As shown in Fig., wider models (e.g., ×1 net) converge to validation accuracy lower than not only the FedAvg counterparts, but also the slimmer ×0.25 net, showing that the widest model may not be a good candidate model. Therefore, switching models to wider configurations lowers efficiency but does not improve accuracy, which is not a valid customization.
From the perspective of data allocation, it is not surprising that SHeteroFL exhibits a non-monotonous relation between the model width and accuracy. For ×1 nets, only 1/4 clients and data are accessible for training. In comparison, 3/4 data are accessible by ×0.25 nets and the more data empowers ×0.25 nets better generalization ability than ×1 nets.
As shown in Fig. 2, wider models (e.g., ×1 net) converge to validation accuracy lower than not only the FedAvg counterparts,but also the slimmer ×0.25 net, showing that the widest model may not be a good candidate model. Therefore, switching models to wider configurations lowers efficiency but does not improve accuracy,
which is not a valid customization. From the perspective of data allocation, it is not surprising that SHeteroFL exhibits a non-monotonous relation between the model width and accuracy. For ×1 nets, only 1/4 clients and data are accessible for training. In comparison, 3/4 data are accessible by ×0.25 nets and the more data empowers
×0.25 nets better generalization ability than ×1 nets.
Answer: We discussed the case in the 3rd paragraph of Sec 4.1, following which we provide the intuition (in our initial submission). The stated intuition is consistent with the reviewer's opinion: "Intuitively, as a consequence of the budget constraint, fewer training clients are able to participate in training wider networks, where the reduced sample size or model size greatly impacted the generalization."
Question: What is the tallest mountain in the continental US?
Answer: Mt. Whitney, which is 14500 ft,  is the tallest mountain in the continental US. Although if we were to include the state of Alaska, then the tallest mountain is actually Mt Denali, which is 20,320 ft. Mt. Whitney is actually only the 17th tallest mountain in all of the US, where the tallest 1-16 are all mountains in Alaska.
Question: Is the language spoken in Kerala a palindrome?
Evidence: Malayalam (/ˌmæləˈjɑːləm/; Malayalam: മലയാളം, Malayāḷam, [mɐlɐjaːɭɐm] (listen)) is a Dravidian language spoken in the Indian state of Kerala and the union territories of Lakshadweep and Puducherry (Mahé district) by the Malayali people. It is one of 22 scheduled languages of India. Malayalam was designated a "Classical Language of India" in 2013. Malayalam has official language status in Kerala and Puducherry (Mahé), and is also the primary spoken language of Lakshadweep and is spoken by 34 million people in India. Malayalam is also spoken by linguistic minorities in the neighbouring states; with a significant number of speakers in the Kodagu and Dakshina Kannada districts of Karnataka, and Kanyakumari, district of Tamil Nadu. It is also spoken by the Malayali Diaspora worldwide, especially in the Persian Gulf countries, due to the large populations of Malayali expatriates there. They are a significant population in each city in India including Mumbai, Bengaluru, Delhi, Kolkata, Pune etc.
Answer: Yes, malayalam is the language spoken in Kerala and it is a palindrome
Question: Which dataset do they use?
Evidence: For the low resource translation task, we used the BTEC corpus as the training data, which consists of 30k sentence pairs with 0.27M Chinese words and 0.33M English words. As development and test sets, we used the CSTAR03 and IWSLT04 held out sets, respectively. We trained a 4-gram language model on the target side of training corpus for running Moses. For training all NMT systems, we employed the same settings as those in the large scale task, except that vocabulary size is 6000, batch size is 16, and the hyper-parameter INLINEFORM0 for SA-NMT.
We used the data from the NIST2008 Open Machine Translation Campaign. The training data consisted of 1.8M sentence pairs, the development set was nist02 (878 sentences), and the test sets are were nist05 (1082 sentences), nist06 (1664 sentences) and nist08 (1357 sentences).
For the low resource translation task, we used the BTEC corpus as the training data, which consists of 30k sentence pairs with 0.27M Chinese words and 0.33M English words. As development and test sets, we used the CSTAR03 and IWSLT04 held out sets, respectively. 
We used the data from the NIST2008 Open Machine Translation Campaign. The training data consisted of 1.8M sentence pairs, the development set was nist02 (878 sentences), and the test sets are were nist05 (1082 sentences), nist06 (1664 sentences) and nist08 (1357 sentences).
Answer: The answers are shown as follows:
* BTEC corpus
* the CSTAR03 and IWSLT04 held out sets
* the NIST2008 Open Machine Translation Campaign
Question: Identify which instrument is string or percussion: Pahu, Morin khuur
Answer: Morin khuur is string, Pahu is percussion.
Question: What are some good activities to keep kids busy while traveling?
Answer: Having lots of activities at hand is key to an enjoyable trip with your kids. Some activity ideas include: Device games or movies (be sure to bring headphones!), coloring, reading books (load these on the device too), playing I Spy, playing the license plate game (keep track of the different states), practice shoe tying, sing songs (better for car trips), or play cards. When you need a break, bring out the snacks!
Question: How is each instance of the dataset annotated?
Evidence: We built a dataset for the task of hate speech detection in memes with 5,020 images that were weakly labeled into hate or non-hate memes, depending on their source. Hate memes were retrieved from Google Images with a downloading tool. We used the following queries to collect a total of 1,695 hate memes: racist meme (643 memes), jew meme (551 memes), and muslim meme (501 Memes). Non-hate memes were obtained from the Reddit Memes Dataset . We assumed that all memes in the dataset do not contain any hate message, as we considered that average Reddit memes do not belong to this class. A total of 3,325 non-hate memes were collected. We split the dataset into train (4266 memes) and validation (754 memes) subsets. The splits were random and the distribution of classes in the two subsets is the same. We didn't split the dataset into three subsets because of the small amount of data we had and decided to rely on the validation set metrics.
We built a dataset for the task of hate speech detection in memes with 5,020 images that were weakly labeled into hate or non-hate memes, depending on their source.
Answer: The answers are shown as follows:
* weakly labeled into hate or non-hate memes, depending on their source
Question: What kind of plant is the Harrisonia
Answer: Harrisonia is a small genus of flowering plants in the subfamily Cneoroideae of the Rutaceae (citrus family). Older taxonomic treatments have placed this genus in the Simaroubaceae.
Question: Is it better to visit Yosemite or Venice?
Answer: Yosemite and Venice are both wonderful to visit. Yosemite is best in spring, before the park becomes crowded with summer hordes, and while the waterfalls are in full volume. Venice is best to visit in the Fall, while it's still warm but after the summer hordes have left. The choice between the two depends in part on the season, but also on whether or not you prefer to be in a park in a beautiful natural setting in the case of Yosemite, or in a fascinating and beautiful old world style city in a unique environment with canals, lagoons, boats and museums in the case of Venice. In both places, it's best to be walking to see the sights. In Yosemite you can hike or bike. In Venice you can walk or take a boat ride on the canals. If possible, one should visit both. If one can only visit one of the two, the choice ultimately boils down to one between natural beauty (Yosemite) and old world culture-based beauty (Venice).