DataHammer/scimrc huggingface dataset:
Question: How does PARENT compute the precision of the generated text?
Evidence: The task of generating natural language descriptions of structured data (such as tables) BIBREF2 , BIBREF3 , BIBREF4 has seen a growth in interest with the rise of sequence to sequence models that provide an easy way of encoding tables and generating text from them BIBREF0 , BIBREF1 , BIBREF5 , BIBREF6 .
For text generation tasks, the only gold standard metric is to show the output to humans for judging its quality, but this is too expensive to apply repeatedly anytime small modifications are made to a system. Hence, automatic metrics that compare the generated text to one or more reference texts are routinely used to compare models BIBREF7 . For table-to-text generation, automatic evaluation has largely relied on BLEU BIBREF8 and ROUGE BIBREF9 . The underlying assumption behind these metrics is that the reference text is gold-standard, i.e., it is the ideal target text that a system should generate. In practice, however, when datasets are collected automatically and heuristically, the reference texts are often not ideal. Figure FIGREF2 shows an example from the WikiBio dataset BIBREF0 . Here the reference contains extra information which no system can be expected to produce given only the associated table. We call such reference texts divergent from the table.
We show that existing automatic metrics, including BLEU, correlate poorly with human judgments when the evaluation sets contain divergent references (§ SECREF36 ). For many table-to-text generation tasks, the tables themselves are in a pseudo-natural language format (e.g., WikiBio, WebNLG BIBREF6 , and E2E-NLG BIBREF10 ). In such cases we propose to compare the generated text to the underlying table as well to improve evaluation. We develop a new metric, PARENT (Precision And Recall of Entailed N-grams from the Table) (§ SECREF3 ). When computing precision, PARENT effectively uses a union of the reference and the table, to reward correct information missing from the reference. When computing recall, it uses an intersection of the reference and the table, to ignore extra incorrect information in the reference. The union and intersection are computed with the help of an entailment model to decide if a text n-gram is entailed by the table. We show that this method is more effective than using the table as an additional reference. Our main contributions are:
When computing precision, PARENT effectively uses a union of the reference and the table, to reward correct information missing from the reference.
Answer: When computing precision, PARENT effectively uses a union of the reference and the table, to reward correct information missing from the reference.
Question: Extract the cinema industry and the percentage box office share occupied by that industry in the format {Industry} - {Percentage} and show as a bullet list. If no percentage specified just list the industry name.
Evidence: Indian cinema is composed of multilingual and multi-ethnic film art. In 2019, Hindi cinema represented 44% of box office revenue, followed by Telugu and Tamil film industries, each representing 13%, Malayalam and Kannada film industries, each representing 5%. Other prominent languages in the Indian film industry include Bengali, Marathi, Odia, Punjabi, Gujarati and Bhojpuri. As of 2022, the combined revenue of South Indian film industries has surpassed that of the Mumbai-based Hindi film industry (Bollywood). As of 2022, Telugu cinema leads Indian cinema's box-office revenue.[details 2]
Answer: Hindi cinema - 44%
Telugu - 13%
Tamil - 13%
Malayalam - 5%
Kannada - 5%
Bengali - 
Marathi -
Odia - 
Punjabi - 
Gujarati - 
Bhojpuri -
Question: Is organic food healthier?
Answer: Whether a food is organic or not does not change the nutritional value of those foods. The idea that organic food is healthier than non organic food can be supported by the premise that potential chemicals or pesticides found in or non organic food are harmful and therefore unhealthy.
Question: Who is George R.R. Martin?
Evidence: George Raymond Richard Martin (born George Raymond Martin; September 20, 1948), also known as GRRM, is an American novelist, screenwriter, television producer and short story writer. He is the author of the series of epic fantasy novels A Song of Ice and Fire, which were adapted into the Emmy Award-winning HBO series Game of Thrones (2011–2019) and its prequel series House of the Dragon (2022–present). He also helped create the Wild Cards anthology series, and contributed worldbuilding for the 2022 video game Elden Ring.
Answer: George Raymond Richard Martin (born George Raymond Martin; September 20, 1948), also known as GRRM, is an American novelist, screenwriter, television producer and short story writer. He is the author of the series of epic fantasy novels A Song of Ice and Fire, which were adapted into the Emmy Award-winning HBO series Game of Thrones (2011–2019) and its prequel series House of the Dragon (2022–present). He also helped create the Wild Cards anthology series, and contributed worldbuilding for the 2022 video game Elden Ring.

In 2005, Lev Grossman of Time called Martin "the American Tolkien", and in 2011, he was included on the annual Time 100 list of the most influential people in the world. He is a longtime citizen of Santa Fe, New Mexico, where he helped fund Meow Wolf and owns the Jean Cocteau Cinema. The city commemorates March 29 as George R. R. Martin Day.
Question: Tell me a joke involving a bird native to Antarctica.
Answer: Two penguins were sitting on an iceberg. One penguin says to the other penguin, "it looks like you're wearing a tuxedo." The other penguin says, "who says I'm not?"
Question: How many Olympic venues are there for canoeing
Evidence: For the Summer Olympics there are 26 venues that have been or will be used in canoeing. Initially set for canoe sprint (flatwater racing until November 2008), canoe slalom (slalom racing until November 2008) was first added at the 1972 Summer Olympics in Munich. Dropped after those games, canoe slalom was reinstitute at the 1992 Summer Olympics in Barcelona.
Answer: For the Summer Olympics there are 26 venues that have been or will be used in canoeing. Initially set for canoe sprint (flatwater racing until November 2008), canoe slalom (slalom racing until November 2008) was first added at the 1972 Summer Olympics in Munich. Dropped after those games, canoe slalom was reinstituted at the 1992 Summer Olympics in Barcelona.

Games	Venue	Other sports hosted at venue for those games	Capacity	Ref.
1936 Berlin	Berlin-Grünau Regatta Course	Rowing	19,000	
1948 London	Henley Royal Regatta	Rowing	Not listed.	
1952 Helsinki	Taivallahti	None	Not listed.	
1956 Melbourne	Lake Wendouree	Rowing	14,300	
1960 Rome	Lake Albano	Rowing	10,000	
1964 Tokyo	Lake Sagami	None	1,500	
1968 Mexico City	Virgilio Uribe Rowing and Canoeing Course	Rowing	17,600	
1972 Munich	Eiskanal (slalom)	None	25,000	
Regattastrecke Oberschleißheim (sprint)	Rowing	41,000	
1976 Montreal	Notre Dame Island	Rowing	27,000	
1980 Moscow	Krylatskoye Sports Complex Canoeing and Rowing Basin	Rowing	21,600	
1984 Los Angeles	Lake Casitas	Rowing	4,680	
1988 Seoul	Han River Regatta Course/Canoeing Site	Rowing	25,000	
1992 Barcelona	Canal Olímpic de Catalunya (sprint)	None	500	
Parc Olímpic del Segre (slalom)	None	2,500	
1996 Atlanta	Lake Lanier (sprint)	Rowing	17,300	
Ocoee Whitewater Center (slalom)	None	14,400	
2000 Sydney	Penrith Whitewater Stadium (slalom)	None	12,500	
Sydney International Regatta Centre (sprint)	Rowing	20,000	
2004 Athens	Olympic Canoe/Kayak Slalom Centre (slalom)	None	3,150	
Schinias Olympic Rowing and Canoeing Centre (sprint)	Rowing	14,000	
2008 Beijing	Shunyi Olympic Rowing-Canoeing Park	Rowing, Swimming (marathon)	37,000	
2012 London	Dorney Lake (sprint)	Rowing	30,000	
Lee Valley White Water Centre (slalom)	None	12,000	
2016 Rio de Janeiro	Lagoa Rodrigo de Freitas (sprint)	Rowing	14,000	
Olympic Whitewater Stadium (slalom)	None	8,000	
2020 Tokyo	Kasai Rinkai Park (slalom)	None	7,500	
Sea Forest Waterway (sprint)	Rowing	12,800
Question: Given a reference text about the golden-green woodpecker, tell be how big they are.
Evidence: The golden-green woodpecker (Piculus chrysochloros) is a species of bird in subfamily Picinae of the woodpecker family Picidae. It is found in Panama and every mainland South American country except Chile and Uruguay.

The golden-green woodpecker is 18 to 27 cm (7.1 to 11 in) long and weighs 55 to 91 g (1.9 to 3.2 oz). Males and females have the same plumage except on their heads. Males of the nominate subspecies P. c. chrysochloros are red from forehead to nape, have olive green from the lores around the eye and onto the side of the neck, and successively a pale yellow stripe, a short red malar band, and an olive green stripe below that area. Their chin and upper throat are yellow. The nominate female has no red on its head; its forehead to nape are olive and it has an olive-brown malar area. Nominate adults have olive green upperparts. Their flight feathers are darker olive-brown and their tail dark olive-brown. Their underparts are pale yellow-buff with olive-brown bars. Their longtish beak is dark gray to blackish with a paler base, their iris white to bluish white, and the legs grayish green. Juveniles are generally duller than adults and have less well defined barring on their underparts.

Subspecies P. c. xanthochlorus is smaller and brighter yellow than the nominate, and the female has a yellow crown. P. c. capistratus is very large and a darker olive green above than the nominate, and has less red in the malar area, a barred throat, and greenish white instead of yellow buff on their underparts. P. c. paraensis is intermediate in size. Its cheek stripe, throat, and underparts' base color are cinnamon-buff. Males have a green, not red, malar, and females have a greenish yellow crown. P. c. laemostictus is large and rather dark, with a plain whitish throat. P. c. polyzonus is also large, with a pale yellow cheek stripe and throat and a yellow base color on the underparts
Answer: The golden-green woodpecker weighs between 55 and 91 grams and is between 18 and 27 centimeters long.
Question: What is the tallest tree in the world
Answer: Coastal redwood (Sequoia sempervirens)
Question: How much is the accuracy of the best temporal model performing?
Evidence: Motivated by the growing interest in matching user account across different social media and networking sites, in this paper we presented models for Digital Stylometry, which is a method for matching users through stylometry inspired techniques. We used temporal and linguistic patterns of users to do the matching.
We experimented with linguistic, temporal, and combined temporal-linguistic models using standard and novel techniques. The methods based on our novel confusion model outperformed the more standard ones in all cases. We showed that both temporal and linguistic information are useful for matching users, with the best temporal model performing with an accuracy of $.10$ and the best linguistic model performing with an accuracy of $0.27$ . Even though the linguistic models vastly outperformed the temporal models, when combined the temporal-linguistic models outperformed both with an accuracy of $0.31$ . The improvement in the performance of the combined models suggests that although temporal information is dwarfed by linguistic information, in terms of its contribution to digital stylometry, it nonetheless provides non-overlapping information with the linguistic data.
Our models were evaluated on $5,612$ users with a total of $11,224$ accounts on Twitter and Facebook combined. In contrast to other works in this area, we did not use any profile information in our matching models. The only information that was used in our models were the time and the linguistic content of posts by the users. This is in accordance with traditional stylometry techniques (since people could lie or misstate this information). Also, we wanted to show that there are implicit clues about the identity of users in the content (language) and context (time) of the users' interactions with social networks that can be used to link their accounts across different services.
In addition to the technical contributions (such as our confusion model), we hope that this paper is able to shed light on the relative ease with which seemingly innocuous information can be used to track users across social networks, even when signing up on different services using completely different account and profile information. In the future, we hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into our models.
We showed that both temporal and linguistic information are useful for matching users, with the best temporal model performing with an accuracy of 0.10 and the best linguistic model performing with an accuracy of 0.27.
Answer: 0.10.
Question: Identify which animal species is alive or extinct: Black-footed Ferret, Dodo
Answer: Dodo is extinct, Black-footed Ferret is alive.
Question: Which component of a PV solar system implements MPPT?
Evidence: Maximum power point tracking (MPPT) or sometimes just power point tracking (PPT), is a technique used with variable power sources to maximize energy extraction as conditions vary. The technique is most commonly used with photovoltaic (PV) solar systems, but can also be used with wind turbines, optical power transmission and thermophotovoltaics.

PV solar systems have varying relationships to inverter systems, external grids, battery banks, and other electrical loads. The central problem addressed by MPPT is that the efficiency of power transfer from the solar cell depends on the amount of available sunlight, shading, solar panel temperature and the load's electrical characteristics. As these conditions vary, the load characteristic (impedance) that gives the highest power transfer changes. The system is optimized when the load characteristic changes to keep power transfer at highest efficiency. This optimal load characteristic is called the maximum power point (MPP). MPPT is the process of adjusting the load characteristic as the conditions change. Circuits can be designed to present optimal loads to the photovoltaic cells and then convert the voltage, current, or frequency to suit other devices or systems.

Solar cells' non-linear relationship between temperature and total resistance can be analyzed based on the Current-voltage (I-V) curve and the power-voltage (P-V) curves. MPPT samples cell output and applies the proper resistance (load) to obtain maximum power. MPPT devices are typically integrated into an electric power converter system that provides voltage or current conversion, filtering, and regulation for driving various loads, including power grids, batteries, or motors. Solar inverters convert DC power to AC power and may incorporate MPPT.

The power at the MPP (Pmpp) is the product of the MPP voltage (Vmpp) and MPP current (Impp).

In general, the P-V curve of a partially shaded solar array can have multiple peaks, and some algorithms can get stuck in a local maximum rather than the global maximum of the curve.
Answer: An MPPT device is usually integrated into the electric power converter system. The electric power converter system is responsible for voltage and current conversation like the DC to AC solar inverter.
Question: Do they report results only on English datasets?
Evidence: Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.
The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.
Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.
The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.
Answer: Yes.
Question: What should be the best personality in a man
Answer: 1. Gentleness
2. Honest
3. Good character to be an example of your generation
Question: Based on the given passage tell me who masked cultists are?
Evidence: Aloy (Ashly Burch) is cast out from the Nora tribe at birth and raised by a fellow outcast named Rost (JB Blanc). As a child (Ava Potter), Aloy obtains a Focus, an augmented reality device that gives her special perceptive abilities. Aloy becomes curious about her origins and is told by Rost that if she wins the Proving, a competition to earn the right to become a member of the Nora, the tribe's Matriarchs might concede this information. Aloy spends some years training in combat and survival under Rost's instruction. After coming of age, Aloy participates in the Proving; she wins the competition, but masked cultists attack the Nora. Rost sacrifices himself to save Aloy from their leader, Helis (Crispin Freeman). When Aloy awakens, the Matriarchs explain that Aloy was found as an infant before a sealed door within the Nora's sacred mountain and that these suspicious origins were the reason for her being an outcast. The Matriarchs name Aloy a "Seeker," allowing her to leave their lands in pursuit of the cultists. Aloy eventually learns that the cultists are part of a splinter Carja faction called the Eclipse and that Aloy was targeted due to her resemblance to an Old World scientist named Elisabet Sobeck (also voiced by Burch). Aloy locates the ruined corporate campus of Faro Automated Solutions and discovers that the Old World was destroyed nearly a thousand years ago after Faro lost control of its automated peacekeeper military robots. The robots, which could self-replicate and consume biomass, overran the planet and engulfed the biosphere, stripping Earth of life. Zero Dawn, a project spearheaded by Sobeck, was initiated to create an automated terraforming system to deactivate the robots and restore life to Earth.
Answer: The masked cultists attacked the Nora when Aloy won the proving competition. Their leader was named Helis. The cultists were part of a splinter Carja faction called the Eclipse and wanted to kill Aloy due to her resemblance to an Old World scientist named Elisabet Sobeck.
Question: Please categorize these items as something a human can safely drink or something that a human should avoid drinking: engine oil, tea, coolant, beer, molten lava, sparkling water, pasteurized cow's milk, and laundry detergent.
Answer: The items that are safe to drink are tea, beer, sparkling water, and milk. It is not safe to drink engine oil, coolant, molten lava, and laundry detergent.
Question: what are the baselines?
Evidence: Table TABREF17 shows our results on CMRC-2017 dataset, which shows that our SAW Reader (mul) outperforms all other single models on the test set, with 7.57% improvements compared with Attention Sum Reader (AS Reader) baseline. Although WHU's model achieves the best besides our model on the valid set with only 0.75% below ours, their result on the test set is lower than ours by 2.27%, indicating our model has a satisfactory generalization ability.
FLOAT SELECTED: Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC2017 Leaderboard 7. The best results are in bold face.
FLOAT SELECTED: Table 3: Case study on CMRC-2017.
Table TABREF17 shows our results on CMRC-2017 dataset, which shows that our SAW Reader (mul) outperforms all other single models on the test set, with 7.57% improvements compared with Attention Sum Reader (AS Reader) baseline
FLOAT SELECTED: Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC2017 Leaderboard 7. The best results are in bold face.
FLOAT SELECTED: Table 3: Case study on CMRC-2017.
Answer: AS Reader, GA Reader, CAS Reader
Question: Give me a list of some best Action movies
Answer: Some of the action movies that never gets old are

1. The Dark Knight: Its not just another Batman but some people consider it the best Batman and Joker movie
2. The Lord of the Rings: It is a movie of three parts sequence, so a great candidate for binge watching over a weekend. However, "The Return of the King" might be considered the best one of all.
3. "The Matrix" series: This Keanu Reeves classic is a must watch that only grows better with age like a good wine.
4. Terminator 2: Judgement Day: Well o well. This movie is a great blend of sci-fi and action and one of its kind in early 90s movie.
5. Gladiator: This box office hit is a drama all around life of a general turned Gladiator, Maximus. Goosebumps guaranteed.
Question: Which corpus of synsets are used?
Evidence: The following different sense inventories have been used during the evaluation:
Watlink, a word sense network constructed automatically. It uses the synsets induced in an unsupervised way by the Watset[CWnolog, MCL] method BIBREF2 and the semantic relations from such dictionaries as Wiktionary referred as Joint INLINEFORM0 Exp INLINEFORM1 SWN in Ustalov:17:dialogue. This is the only automatically built inventory we use in the evaluation.
The following different sense inventories have been used during the evaluation:
Watlink, a word sense network constructed automatically. It uses the synsets induced in an unsupervised way by the Watset[CWnolog, MCL] method BIBREF2 and the semantic relations from such dictionaries as Wiktionary referred as Joint INLINEFORM0 Exp INLINEFORM1 SWN in Ustalov:17:dialogue.
Answer: The answers are shown as follows:
* Wiktionary