DataHammer/scimrc huggingface dataset:
Question: How long should a child's carseat remain rear facing?
Answer: Extended rear facing until the child reaches the maximum height or weight limits of the carseat is the safest option. When a child is sitting forward facing in a frontal collision a large amount of force is exerted onto the head, neck and shoulders leading to serious injury like paralysis or death. If the child is sitting rear facing during a collision, the force is spread over a greater area of the body with less pressure on the neck, head, and inner organs. Children should sit rear facing as long as possible, in case of collision it reduces the risk of serious injury or death by more than 90%. You should consult your car seat's manual to determine the maximum height and weight restrictions.
Question: When was the Philippine House Committee elevated into a standing committee
Answer: The Philippine House Committee on Sustainable Development Goals, or House Sustainable Development Goals Committee is a standing committee of the Philippine House of Representatives.

Originally designated as a special committee, it was elevated into a standing committee on March 10, 2020.
Question: who are the greatest Raider players
Answer: Jim Otto, Ken Stabler, Cliff Branch, Tim Brown, Marcus Allen
Question: What is the difference between affect and effect?
Answer: Affect is a verb, and it means to impact or change. Effect is a noun, an effect is the result of a change. An example of each is as follows; 
"Exercising is positively affecting my health”
“Exercise can have positive effects on your health”
Question: What are some words that rhyme with orange
Answer: Sporange
Storage
Courage
Warrant
Abhorrent 
Forage
Porridge
Question: What framework do they introduce to optimize NMT by?
Evidence: Neural machine translation (NMT) has recently become popular in the field of machine translation. However, NMT suffers from the problem of repeating or missing words in the translation. To address this problem, Tu et al. (2017) proposed an encoder-decoder-reconstructor framework for NMT using back-translation. In this method, they selected the best forward translation model in the same manner as Bahdanau et al. (2015), and then trained a bi-directional translation model as fine-tuning. Their experiments show that it offers significant improvement in BLEU scores in Chinese-English translation task. We confirm that our re-implementation also shows the same tendency and alleviates the problem of repeating and missing words in the translation on a English-Japanese task too. In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation.
To address this problem, Tu et al. (2017) proposed an encoder-decoder-reconstructor framework for NMT using back-translation.
Answer: By back-translation from the output sentences into the original source sentences.
Question: How much of American wine is produced in California?
Answer: California is responsibly for roughly 90% of American wine production
Question: Does having a real cork, artificial cork, or a screw-top have any impact on the quality of a bottle of wine?
Answer: Generally, no. There is little evidence to suggest any one closure is more superior than another for most wines. It is more important to have an airtight seal on a bottle of wine so as to prevent introduction of air into the bottle which would cause oxidation of the wine inside.
Question: Linux Second-stage boot loader
Evidence: Second-stage boot loaders, such as GNU GRUB, rEFInd, BOOTMGR, Syslinux, NTLDR or iBoot, are not themselves operating systems, but are able to load an operating system properly and transfer execution to it; the operating system subsequently initializes itself and may load extra device drivers. The second-stage boot loader does not need drivers for its own operation, but may instead use generic storage access methods provided by system firmware such as the BIOS or Open Firmware, though typically with restricted hardware functionality and lower performance.

Many boot loaders can be configured to give the user multiple booting choices. These choices can include different operating systems (for dual or multi-booting from different partitions or drives), different versions of the same operating system (in case a new version has unexpected problems), different operating system loading options (e.g., booting into a rescue or safe mode), and some standalone programs that can function without an operating system, such as memory testers (e.g., memtest86+), a basic shell (as in GNU GRUB), or even games (see List of PC Booter games).Some boot loaders can also load other boot loaders; for example, GRUB loads BOOTMGR instead of loading Windows directly. Usually, a default choice is preselected with a time delay during which a user can press a key to change the choice; after this delay, the default choice is automatically run so normal booting can occur without interaction.

The boot process can be considered complete when the computer is ready to interact with the user, or the operating system is capable of running system programs or application programs.

Many embedded systems must boot immediately. For example, waiting a minute for a digital television or a GPS navigation device to start is generally unacceptable. Therefore, such devices have software systems in ROM or flash memory so the device can begin functioning immediately; little or no loading is necessary, because the loading can be precomputed and stored on the ROM when the device is made.

Large and complex systems may have boot procedures that proceed in multiple phases until finally the operating system and other programs are loaded and ready to execute. Because operating systems are designed as if they never start or stop, a boot loader might load the operating system, configure itself as a mere process within that system, and then irrevocably transfer control to the operating system. The boot loader then terminates normally as any other process would.
Answer: A two-stage bootloader actually consists of two bootloaders after each other. The first being small with the sole purpose of loading the second one. The second one can then contain all the code needed for loading the kernel. GRUB uses two (or arguably, three) stages.
Question: Classify the below energy sources based on their type.
NUCLEAR, SOLAR ENERGY, COAL, HYDROPOWER, GEOTHERMAL, WIND ENERGY, NATURAL GAS
Answer: SOLAR ENERGY, WIND ENERGY, GEOTHERMAL, HYDROPOWER - Renewable energy.
COAL, NATURAL GAS, NUCLEAR - Non Renewable energy.
Question: Name the BRICS nations.
Answer: Brazil, Russia, India, China & S.Africa
Question: What is the QASENT dataset used for?
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
Wang et al. (2007) created the QASENT dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task.
Answer: The QASENT dataset has been widely used for benchmarking the answer selection task.
Question: What is one of the first recorded mentions of the sauna in Finnish literature?
Evidence: The sauna in Finland is an old phenomenon and its roots are difficult to trace, but its earliest versions are believed to be from 7000 BC.[citation needed] Bath houses were recorded in Europe during the same time period, but Finnish bathing habits were poorly documented for most of history. One of the first written mentions of what is believed to be the sauna customs of the forefathers of the Finns was written by Nestor the Chronicler in 1112. He told of "hot wooden saunas in which naked bathers beat themselves with branches and finally pour cold water over themselves".

During the Reformation in Scandinavia the popularity of saunas expanded to other countries because the European bath houses were being destroyed.

One reason the sauna culture has always flourished in Finland has been because of the versatility of the sauna. When people were moving, the first thing they did was to build a sauna. Finns have used the sauna to live in, eat, address matters of hygiene, and, most importantly, give birth in an almost sterile environment. Unlike many other, more densely populated places in Europe, the availability of wood needed to build and warm the sauna has never been an issue. Another reason for its popularity is that in such a cold climate, the sauna allows people warmth for at least a short period of time. However, it is just as popular in the summer as in the winter.
Answer: Nestor the Chronicler in 1112. He told of "hot wooden saunas in which naked bathers beat themselves with branches and finally pour cold water over themselves"".
Question: What are the ways to travel from Delhi to Varanasi? And how long does each route take?
Answer: One can use any of the following 3 ways to travel from Delhi to Varanasi. 
1. Travel by flight from Delhi airport to Varanasi.  Depending on where you stay in Delhi, your travel time to Delhi airport could be between 45 mins to 2 hours.  The flight time from Delhi to Varanasi is 1 hour 30 minutes.  On arrival at Varanasi airport, it takes about 1 hour to get to the city.  The total time that this route takes is about 7- 8 hours taking into account the airport security time. 
2. You could take a train from New Delhi railway station to go to Varanasi.  The fastest train travel time is about 6 hours and the Varanasi railway station is in the heart of the city.  The total time this route takes is also between 7-8 hours depending on how far one is from the New Delhi railway station. 
3. The last option one could consider is driving by road from Delhi to Varanasi.  The total travel time by this route is bout 11-12 hours with a couple of breaks on the way.
Question: Who introduced the concept of the electric field and what is it?
Evidence: The concept of the electric field was introduced by Michael Faraday. An electric field is created by a charged body in the space that surrounds it, and results in a force exerted on any other charges placed within the field. The electric field acts between two charges in a similar manner to the way that the gravitational field acts between two masses, and like it, extends towards infinity and shows an inverse square relationship with distance. However, there is an important difference. Gravity always acts in attraction, drawing two masses together, while the electric field can result in either attraction or repulsion. Since large bodies such as planets generally carry no net charge, the electric field at a distance is usually zero. Thus gravity is the dominant force at distance in the universe, despite being much weaker.
An electric field generally varies in space and its strength at any one point is defined as the force (per unit charge) that would be felt by a stationary, negligible charge if placed at that point. The conceptual charge, termed a 'test charge', must be vanishingly small to prevent its own electric field disturbing the main field and must also be stationary to prevent the effect of magnetic fields. As the electric field is defined in terms of force, and force is a vector, having both magnitude and direction, so it follows that an electric field is a vector field.
The study of electric fields created by stationary charges is called electrostatics. The field may be visualised by a set of imaginary lines whose direction at any point is the same as that of the field. This concept was introduced by Faraday, whose term 'lines of force' still sometimes sees use. The field lines are the paths that a point positive charge would seek to make as it was forced to move within the field; they are however an imaginary concept with no physical existence, and the field permeates all the intervening space between the lines. Field lines emanating from stationary charges have several key properties: first, that they originate at positive charges and terminate at negative charges; second, that they must enter any good conductor at right angles, and third, that they may never cross nor close in on themselves.
Answer: The concept of the electric field was introduced by Michael Faraday. An electric field is created by a charged body in the space that surrounds it, and results in a force exerted on any other charges placed within the field.
Question: Extract the unincorporated communities in the below text that begin with starting with the letter R, W and G and put them in a pipe separated list
Evidence: According to the U.S. Census Bureau, the township had a total area of 82.92 square miles (214.77 km2), including 70.24 square miles (181.92 km2) of land and 12.69 square miles (32.86 km2) of water (15.30%).

Burleigh (with a 2010 Census population of 725), Cape May Court House (5,338), Rio Grande (2,670) and Whitesboro (2,205) are unincorporated communities and census-designated places (CDPs) located within Middle Township; Whitesboro and Burleigh had previously been combined for statistical purposes by the Census Bureau as Whitesboro-Burleigh through the 2000 Census. Other unincorporated communities, localities and place names located partially or completely within the township include Del Haven, Dias Creek, Goshen, Goshen Landing, Green Creek, Holly Beach, Mayville, Norburys Landing, Nummytown, Pierces, Pierces Point, Reeds Beach, Shellbed Landing, Swain Point, Swainton, Wildwood Gardens and Wildwood Junction.
Answer: Reeds Beach | Wildwood Gardens | Wildwood Junction | Goshen | Goshen Landing | Green Creek
Question: How many members will NATO have once Finland and Sweden join the organization?
Evidence: NATO formed with twelve founding members and has added new members eight times, most recently when North Macedonia joined the alliance in March 2020. Following the acceptance of their applications for membership in June 2022, Finland and Sweden are anticipated to become the 31st and 32nd members, with their Accession Protocols to the North Atlantic Treaty now in the process of being ratified by the existing members. In addition, NATO currently recognizes Bosnia and Herzegovina, Georgia, and Ukraine as aspiring members. Enlargement has led to tensions with non-member Russia, one of the twenty additional countries participating in NATO's Partnership for Peace programme. Another nineteen countries are involved in institutionalized dialogue programmes with NATO.
Answer: 32
Question: What are the top Sports Movies ever?
Answer: The top sports movies are somewhat subjective but certainly could include the following movies.
1. Miracle
2. Caddyshack
3. The Blind Side
4. Rocky 1
5. The Blind Side
6. Hooziers
7. Slap Shot
8. A field of Dreams
9. Prefontaine
10. Moneyball
Question: Give me 4 words to describe designer shoes
Answer: creative, exquisite, expensive, fashionable
Question: Make a list of 10 things I should buy at the grocery store.
Answer: Eggs, milk, yogurt, bread, deli meat, cheese, spinach, apples, chicken breast, chocolate
Question: Extract the most commonly used term to describe bitcoin and simplify it in one sentence
Evidence: Bitcoin is a protocol which implements a highly available, public, permanent, and decentralized ledger. In order to add to the ledger, a user must prove they control an entry in the ledger. The protocol specifies that the entry indicates an amount of a token, bitcoin with a minuscule b. The user can update the ledger, assigning some of their bitcoin to another entry in the ledger. Because the token has characteristics of money, it can be thought of as a digital currency.
Answer: Bitcoin is basically a secure way to maintain a virtual ledger
Question: What are the improvements of the authors' model?
Evidence: Recent years, great efforts have been made on extracting relational fact from unstructured raw texts to build large structural knowledge bases. A relational fact is often represented as a triplet which consists of two entities (subject and object) and semantic relation between them. Early works BIBREF0, BIBREF1, BIBREF2 mainly focused on the task of relation classification which assumes the entity pair are identified beforehand. This limits their practical application since they neglect the extraction of entities. To extract both entities and their relation, existing methods can be divided into two categories : the pipelined framework, which first uses sequence labeling models to extract entities, and then uses relation classification models to identify the relation between each entity pair; and the joint approach, which combines the entity model and the relation model through different strategies, such as constraints or parameters sharing.

Our model is based on this framework and make three improvements: (1) BERT [1] is introduced as a feature extraction layer in place of BiLSTM. We also optimize the pre-training process of BERT by introducing a semanticenhanced task. (2) A large-scale Baidu Baike corpus is introduced for entity recognition pretraining, which is of weekly supervised learning since there is no actual named entity label. (3) Soft label embedding is proposed to effectively transmit information between entity recognition and relation extraction.
Answer: (1) BERT is introduced as a feature extraction layer in place of BiLSTM. The authors also optimize the pre-training process of BERT by introducing a semanticenhanced task. (2) A large-scale Baidu Baike corpus is introduced for entity recognition pretraining, which is of weekly supervised learning since there is no actual named entity label. (3) Soft label embedding is proposed to effectively transmit information between entity recognition and relation extraction.
Question: By how much does their method outperform state-of-the-art OOD detection?
Evidence: The goal of this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns in the context of a dialog. Most prior approaches do not consider dialog context and make predictions for each utterance independently. We will show that this independent decision leads to suboptimal performance even when actual OOD utterances are given to optimize the model and that the use of dialog context helps reduce OOD detection errors. To consider dialog context, we need to connect the OOD detection task with the overall dialog task. Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ). Furthermore, we release new dialog datasets which are three publicly available dialog corpora augmented with OOD turns in a controlled way (exemplified in Table TABREF2 ) to foster further research.
The result is shown in Table TABREF23 . Since there are multiple actions that are appropriate for a given dialog context, we use per-utterance Precision@K as performance metric. We also report f1-score for OOD detection to measure the balance between precision and recall. The performances of HCN on Test-OOD are about 15 points down on average from those on Test, showing the detrimental impact of OOD utterances to such models only trained on in-domain training data. AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test. Interestingly, AE-HCN-CNN has even better performance than HCN on Test, indicating that, with the CNN encoder, counterfeit OOD augmentation acts as an effective regularization. In contrast, AE-HCN-Indep failed to robustly detect OOD utterances, resulting in much lower numbers for both metrics on Test-OOD as well as hurting the performance on Test. This result indicates two crucial points: 1) the inherent difficulty of finding an appropriate threshold value without actually seeing OOD data; 2) the limitation of the models which do not consider context. For the first point, Figure FIGREF24 plots histograms of reconstruction scores for IND and OOD utterances of bAbI6 Test-OOD. If OOD utterances had been known a priori, the threshold should have been set to a much higher value than the maximum reconstruction score of IND training data (6.16 in this case).
Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ). 
AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test. 
Answer: AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average
Question: Who's the protagonist in the book "Gone with the wind"
Answer: Scarlett O Hara
Question: Where can I see the remnants of a meteor strike on earth?
Answer: Meteor Crater located in Arizona, USA is one of the best preserved impact crater site on earth. The crater width is more than one kilometer and is visible from passenger airplanes flying overhead. The site is a best example of the destruction the meteors can cause. The site contains a visitor center that has various educational shows about space, meteor and human space flight history, remnants of the meteors the visitors can touch and feel, observation deck and guided tours of the crater outer rim. The site is owned by a private family who generates money by selling visitor tickets.
Question: Given this paragraph about skydiving, give me the average time skydiver spends in freefall.
Evidence: Skydiving includes free falling (usually from an airplane) through the air prior to opening a parachute. Typically skydives are carried out from around 4,000m (or 12,500ft) offering 40 to 50 seconds of freefall time. Longer free fall times can be achieved by exiting an aircraft at altitudes much higher than 4,000m, but very high jumps require pressurized air within the aircraft, and bottled oxygen for the diver.
Answer: Typically skydives are carried out from around 4,000m (or 12,500ft) offering 40 to 50 seconds of freefall time.