DataHammer/scimrc huggingface dataset:
Question: How does the context-aware variational autoencoder learn event background information?
Evidence: In addition to the traditional VAE structure, we introduces an extra context-aware latent variable in CWVAE to learn the event background knowledge. In the pretrain stage, CWVAE is trained on an auxiliary dataset (consists of three narrative story corpora and contains rich event background knowledge), to learn the event background information by using the context-aware latent variable. Subsequently, in the finetune stage, CWVAE is trained on the task-specific dataset to adapt the event background information to each specific aspect of If-Then inferential target (e.g., intents, reactions, etc.).
In addition to the traditional VAE structure, we introduces an extra context-aware latent variable in CWVAE to learn the event background knowledge. In the pretrain stage, CWVAE is trained on an auxiliary dataset (consists of three narrative story corpora and contains rich event background knowledge), to learn the event background information by using the context-aware latent variable. Subsequently, in the finetune stage, CWVAE is trained on the task-specific dataset to adapt the event background information to each specific aspect of If-Then inferential target (e.g., intents, reactions, etc.).
Answer:  CWVAE is trained on an auxiliary dataset to learn the event background information by using the context-aware latent variable.  Then, in finetute stage, CWVAE is trained on the task-specific dataset to adapt the event background information to each specific aspect of If-Then inferential target.
Question: Which is a species of fish? Tetra or Quart
Answer: Tetra
Question: Can cars have odd number of wheels?
Answer: Cars are vehicles that allow you to commute from one point to another. These are built by companies across the world and designed based on the companies choice, local context, historic reference. The car has three main parts: i) the engine that provides the thrust or power to the vehicle. ii) The body or the frame that provides the space to transport things and people. iii) The chasis which is the structural component that does the load bearing and has wheels which transfer the power of the engine into horizontal motion. 

Cars generally have four wheel but can sometime have odd numbered wheels as well. For example cars have a fifth wheel, the spare wheel, bolted to the body for differentiated look. It is not uncommon for cars to have a wheel stolen in which case they have three wheel. Few cooler car designs, modified cars, smaller cars can have three wheels as well but it is rare for a car to have zero or one wheel.
Question: Please sum up the main content of this paper.
Evidence: We described a method for monolingual and crosslingual clustering of an incoming stream of documents. The method works by maintaining centroids for the monolingual and crosslingual clusters, where a monolingual cluster groups a set of documents and a crosslingual cluster groups a set of monolingual clusters. We presented an online crosslingual clustering method which auto-corrects past decisions in an efficient way. We showed that our method gives state-of-the-art results on a multilingual news article dataset for English, Spanish and German. Finally, we discussed how to leverage different SVM training procedures for ranking and classification to improve monolingual and crosslingual clustering decisions. Our system is integrated in a larger media monitoring project BIBREF10 , BIBREF11 and solving the use-cases of monitors and journalists, having been validated with qualitative user testing.
We described a method for monolingual and crosslingual clustering of an incoming stream of documents. The method works by maintaining centroids for the monolingual and crosslingual clusters, where a monolingual cluster groups a set of documents and a crosslingual cluster groups a set of monolingual clusters. We presented an online crosslingual clustering method which auto-corrects past decisions in an efficient way. We showed that our method gives state-of-the-art results on a multilingual news article dataset for English, Spanish and German. Finally, we discussed how to leverage different SVM training procedures for ranking and classification to improve monolingual and crosslingual clustering decisions.
Answer: The authors describe a method for monolingual and cross-lingual clustering of incoming document flows. The approach works by maintaining a hub for monolingual and cross-lingual clustering. The authors propose an online cross-language clustering method and show that their method gives state-of-the-art results on a multilingual news article dataset in English, Spanish, and German. Finally, the author discusses how to use different SVM training programs for sorting and classification.
Question: What is typical GAN architecture for each text-to-image synhesis group?
Evidence: In this section, we propose a taxonomy to summarize advanced GAN based text-to-image synthesis frameworks, as shown in Figure FIGREF24. The taxonomy organizes GAN frameworks into four categories, including Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANs, and Motion Enhancement GAGs. Following the proposed taxonomy, each subsection will introduce several typical frameworks and address their techniques of using GANS to solve certain aspects of the text-to-mage synthesis challenges.
FLOAT SELECTED: Figure 9. A Taxonomy and categorization of advanced GAN frameworks for Text-to-Image Synthesis. We categorize advanced GAN frameworks into four major categories: Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANs, and Motion Enhancement GAGs. The relationship between relevant frameworks and their publication date are also outlined as a reference.
In this section, we propose a taxonomy to summarize advanced GAN based text-to-image synthesis frameworks, as shown in Figure FIGREF24.
FLOAT SELECTED: Figure 9. A Taxonomy and categorization of advanced GAN frameworks for Text-to-Image Synthesis. We categorize advanced GAN frameworks into four major categories: Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANs, and Motion Enhancement GAGs. The relationship between relevant frameworks and their publication date are also outlined as a reference.
Answer: Semantic Enhancement GANs: DC-GANs, MC-GAN
Resolution Enhancement GANs: StackGANs, AttnGAN, HDGAN
Diversity Enhancement GANs: AC-GAN, TAC-GAN etc.
Motion Enhancement GAGs: T2S, T2V, StoryGAN
Question: Given this paragraph about Corsica, when was Napoleon Bonaparte born and where?
Evidence: Corsica was ruled by the Republic of Genoa from 1284 to 1755, when it seceded to become a self-proclaimed, Italian-speaking Republic. In 1768, Genoa officially ceded it to Louis XV of France as part of a pledge for the debts it had incurred by enlisting France's military help in suppressing the Corsican revolt, and as a result France went on to annex it in 1769. The future Emperor of the French, Napoleon Bonaparte, was a native Corsican, born that same year in Ajaccio: his ancestral home, Maison Bonaparte, is today a visitor attraction and museum. Because of Corsica's historical ties to Tuscany, the island has retained many Italian cultural elements and many Corsican surnames are rooted in the Italian peninsula.
Answer: Napoleon Bonaparte was born in 1769, the same year that France annexed Corsica. He was born in Ajaccio, Corsica.
Question: Why do audiophiles spend lots of money on high-end audio equipment and does more expensive gear always sound better than less expensive hardware?
Answer: While it is generally true that audiophiles are willing to spend more money on high-end gear than those less passionate about audio, justification for this additional financial outlay is not solely based on the equipment’s definitive sonic superiority.
 
There are a handful of key attributes that define audio equipment’s performance, including: frequency response, detail, texture, layering ability, coherency, vocal presentation, and timbre and tonality. Audiophiles typically seek out gear capable of performing well in many or all of these areas, and it is often these elevated expectations that drive an audiophile’s willingness to spend more. However, there are elements of subjectivity at play, as well as physiological differences, that can lead to variations in interpretation of what sounds “accurate” or “engaging.” Therefore, even if it is generally true that most expensive audio equipment objectively performs better than their lesser priced counterparts, individual preference and anatomy can cause differences in expectations or hearing that can lead to different perceptions of sonic performance, even with high-end audio gear.
 
The Erlkonig is an In-Ear Monitor made by German producer, Vision Ears. By many, it is thought to be one of the best In-Ear Monitors ever made. At a retail price of over $4,000, it is also generally considered to be quite expensive. Widely regarded to perform extremely well, the Erlkonig has 13 balance armature drivers, 4 user-selectable sound settings, and shells made out of silver. While non-audiophiles will likely scoff at its price and be indifferent to the Erlkonig’s specifications, many audiophiles will salivate at the chance for even a single listen. Even though many deep-pocketed audiophiles could justifiably purchase the Erlkonig, there are plenty of audiophiles and non-audiophiles who do not enjoy the IEM’s sound. For others, the shell is ergonomically incompatible with their ears. So, even if expensive audio gear on the whole performs and sounds “better” than lesser priced gear, unique preferences and anatomical variation also shape perception of sonic performance.
Question: Is the performance compared against a baseline model?
Evidence: In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel).
In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel).
Answer: Yes.
Question: How big are datasets used in experiments?
Evidence: There is usually a trade-off between the number of training samples and the number of trainable parameters in a deep network model BIBREF16. In general, more data we have, better result are generated by supervised deep learning methods. Data augmentation helps to increase the number of training data, but a bigger dataset needs a better and most likely bigger network architecture in terms of generalization. Otherwise, the model might over-fitted or under-fitted on training data. Using our annotation software, we automatically extracted landmarks of 2000 images from the UOttawa database BIBREF14 had been annotated for image segmentation tasks. The database was randomly divided into three sets: 90$\%$ training, 5$\%$ validation, and 5$\%$ testing datasets. For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model. During the training process of TongueNet, we employed an online data augmentation, including rotation (-25 to 25 degrees), translation (-30 to 30 points in two directions), scaling (from 0.5 to 2 times), horizontal flipping, and combination of these transformations, and annotation point locations are also was transformed, correspondingly. From our extensive random search hyper-parameter tuning, learning rate, the number of iterations, mini-batch sizes, the number of epochs was determined as 0.0005, 1000, 30, 10, respectively. We deployed our experiments using Keras with TensorFlow as the backend on a Windows PC with Core i7, 4.2 GHz speed using one NVIDIA 1080 GPU unit, and 32 GB of RAM. Adam optimization with fixed momentum values of 0.9, was utilized for training.
Using our annotation software, we automatically extracted landmarks of 2000 images from the UOttawa database BIBREF14 had been annotated for image segmentation tasks. 
Answer: The answers are shown as follows:
* 2000 images
Question: iPhone 14 pro vs Samsung s22 ultra
Answer: Large OLED screens on both with 1-120Hz dynamic refresh rate
iPhone has Face ID, Galaxy uses a fingerprint scanner
Dynamic Island on iPhone is unique
Galaxy has S Pen
Similar main three cameras, but Galaxy also has a unique 10X zoom lens
Galaxy has faster charging
Similar prices
iPhone battery lasts longer
Question: What is geostatistics?
Answer: Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets. Developed originally to predict probability distributions of ore grades for mining operations, it is currently applied in diverse disciplines including petroleum geology, hydrogeology, hydrology, meteorology, oceanography, geochemistry, geometallurgy, geography, forestry, environmental control, landscape ecology, soil science, and agriculture (esp. in precision farming). Geostatistics is applied in varied branches of geography, particularly those involving the spread of diseases (epidemiology), the practice of commerce and military planning (logistics), and the development of efficient spatial networks. Geostatistical algorithms are incorporated in many places, including geographic information systems (GIS).
Question: what is a fast break in basketball?
Answer: A fast break in basketball is when the team with the ball either passes or runs the ball quickly down the court to their own goal, with the intent of scoring quickly before the defensive team can get set into their usual positions.
Question: What problem do the authors define?
Evidence: Negotiations, either between individuals or entities, are ubiquitous in everyday human interactions ranging from sales to legal proceedings. Being a good negotiator is a complex skill, requiring the ability to understand the partner's motives, ability to reason and to communicate effectively, making it a challenging task for an automated system. While research in building automatically negotiating agents has primarily focused on agent-agent negotiations BIBREF0, BIBREF1, there is a recent interest in agent-human negotiations BIBREF2 as well. Such agents may act as mediators or can be helpful for pedagogical purposes BIBREF3.
Efforts in agent-human negotiations involving free-form natural language as a means of communication are rather sparse. Researchers BIBREF4 recently studied natural language negotiations in buyer-seller bargaining setup, which is comparatively less restricted than previously studied game environments BIBREF5, BIBREF6. Lack of a well-defined structure in such negotiations allows humans or agents to express themselves more freely, which better emulates a realistic scenario. Interestingly, this also provides an exciting research opportunity: how can an agent leverage the behavioral cues in natural language to direct its negotiation strategies? Understanding the impact of natural language on negotiation outcomes through a data-driven neural framework is the primary objective of this work.
We focus on buyer-seller negotiations BIBREF4 where two individuals negotiate the price of a given product. Leveraging the recent advancements BIBREF7, BIBREF8 in pre-trained language encoders, we attempt to predict negotiation outcomes early on in the conversation, in a completely data-driven manner (Figure FIGREF3). Early prediction of outcomes is essential for effective planning of an automatically negotiating agent. Although there have been attempts to gain insights into negotiations BIBREF9, BIBREF10, to the best of our knowledge, we are the first to study early natural language cues through a data-driven neural system (Section SECREF3). Our evaluations show that natural language allows the models to make better predictions by looking at only a fraction of the negotiation. Rather than just realizing the strategy in natural language, our empirical results suggest that language can be crucial in the planning as well. We provide a sample negotiation from the test set BIBREF4 along with our model predictions in Table TABREF1.
We define a novel problem of predicting negotiation outcomes from a fraction of the dialogue (Section 2).
Answer: The authors define a novel problem of predicting negotiation outcomes from a fraction of the dialogue.
Question: How many oceans in the world?
Answer: There are 7 oceans in the world
Question: Give me a short list with five ideas for hobbies I can do at home.
Answer: Hobbies you can do at home include:
- painting
- cooking
- reading
- working out
- board games
Question: What are the toxic house plants for cats?
Answer: If you have cats as pets, be careful about having these house plants around. Ingesting part of these plants can be very dangerous for your feline friends.
-  Lilies such as easter lilies, stargazers, daylilies, etc. can cause kidney failure and death
- Sago palm leaves can release a toxin that causes liver damage
- Desert rose can affect heart rate and cause vomiting
- Bulbs of plants such as daffodils and tulips can cause convulsions, diarrhea, and vomiting
- a toxic chemical from azaleas can lead to coma, cardiovascular collapse and death
Question: what is the current location of Voyager 1 spacecraft in the cosmos?
Answer: As of the moment when this question was asked, Voyager 1 is 14,798,213,369 miles away from the earth, 14,825,446,288 miles away from the sun. It takes approximately 22 hours for the light to travel in one way direction from Voyager 1 to the planet earth.
Question: What is shown through multitask learning analysis?
Evidence: In this work we find that all six of the ERP components from BIBREF0 can be predicted above chance by a model which has been pretrained using a language modeling objective and then directly trained to predict the components. This is in contrast to prior work which has successfully linked language models to the N400 BIBREF0 and P600 BIBREF7 but not the other ERP components. We also note that contrary to BIBREF7 , we find that an LSTM does contain information that can be used to predict EEG data, and in particular that it can predict the P600. We speculate that the analysis used in BIBREF7 did not find reliable effects because the language models were related to the EEG data through functions chosen a priori (the surprisal, and the `distance' metric). These functions, though interpretable, might be interpretable at the cost of losing much of the information in the representations learned by the network.
In addition, we show through our multitask learning analysis that information is shared between ERP components, and between ERP components and behavioral data. Although these relationships must be viewed with caution until they can be verified across multiple datasets and with more variation in neural network architectures, here we consider some potential reasons for our findings. The broad point we wish to make is that by better understanding which ERP components share information with each other and with behavioral data through the type of analysis we present here (multitask learning) or other means, we can better understand what drives each ERP component and in turn the processes involved in human language comprehension.
In addition, we show through our multitask learning analysis that information is shared between ERP components, and between ERP components and behavioral data. Although these relationships must be viewed with caution until they can be verified across multiple datasets and with more variation in neural network architectures, here we consider some potential reasons for our findings.
Answer: Information is shared between ERP components, and between ERP components and behavioral data.
Question: What climate are cacti typically found in?
Answer: Cacti are plants that are specialized for growing in desert climates.
Question: What data augmentation techniques are used?
Evidence: Team 9 BIBREF24: This team submitted the winning entry, beating the second-placed team by around 9% in terms of joint goal accuracy. They use two separate models for categorical and non-categorical slots, and treat numerical categorical slots as non-categorical. They also use the entire dialogue history as input. They perform data augmentation by back translation between English and Chinese, which seems to be one of the distinguishing factors resulting in a much higher accuracy.
 They perform data augmentation by back translation between English and Chinese, which seems to be one of the distinguishing factors resulting in a much higher accuracy.
Answer: The answers are shown as follows:
* back translation between English and Chinese
Question: Tell me which of these are served hot or cold: soup, ice cream, potato salad, mashed potatoes, cake, cheese platter
Answer: Soup and mashed potatoes are both served hot. Ice cream, potato salad, cake, and cheese platters are all served cold
Question: What are the features offered by Github Copilot?
Evidence: When provided with a programming problem in natural language, Codex is capable of generating solution code. It is also able to describe input code in English and translate code between programming languages.

According to its website, GitHub Copilot includes assistive features for programmers, such as the conversion of code comments to runnable code and autocomplete for chunks of code, repetitive sections of code, and entire methods and/or functions. GitHub reports that Copilot’s autocomplete feature is accurate roughly half of the time; with some Python function header code, for example, Copilot correctly autocompleted the rest of the function body code 43% of the time on the first try and 57% of the time after ten attempts.

GitHub states that Copilot’s features allow programmers to navigate unfamiliar coding frameworks and languages by reducing the amount of time users spend reading documentation.
Answer: GitHub Copilot includes assistive features for programmers, such as the conversion of code comments to runnable code and autocomplete for chunks of code, repetitive sections of code, and entire methods and/or functions.
Question: Map the following models sold in the UK to either the Ford or Vauxhall brands: Escort, Cavalier, Nova, Ka
Answer: The Ka and Escort models were sold by Ford, and Cavalier and Nova were Vauxhall models.