DataHammer/scimrc huggingface dataset:
Question: What is impala?
Answer: The impala or rooibok is a medium-sized antelope found in eastern and southern Africa.
Question: What is dopamine?
Answer: A chemical released by the brain to feel good. Relieves pain.
Question: Please write some key points on Booker prize from the given passage.
Evidence: The 2022 Booker Prize was a literary award given for the best English novel of the year. It was announced on 17 October 2022, during a ceremony hosted by Sophie Duker at the Roundhouse in London. The longlist was announced on 26 July 2022. The shortlist was announced on 6 September. Leila Mottley, at 20, was the youngest longlisted writer to date, and Alan Garner, at 87, the oldest. The majority of the 13 titles were from independent publishers. The prize was awarded to Shehan Karunatilaka for his novel, The Seven Moons of Maali Almeida, receiving £50,000. He is the second Sri Lankan to win the prize, after Michael Ondaatje
Answer: 1. The 2022 Booker Prize was a literary honour presented to the year's top English novel.
2. It was revealed on October 17, 2022, at a ceremony Sophie Duker presided over at London's Roundhouse.
3. On July 26, 2022, the longlist was announced.
4. On September 6th, the shortlist was announced.
5. Alan Garner, aged 87, was the oldest longlisted author to date, and Leila Mottley, at 20 years old, was the youngest.
6. Independent publ ishers produced the majority of the 13 books.
7. Shehan Karunatilaka received the award and £50,000 for his book The Seven Moons of Maali Almeida.
8. After Michael Ondaatje, he is the second Sri Lankan to win the award.
Question: What are the words of House Greyjoy?
Answer: "We Do Not Sow"
Question: What’s the best way to cook scrambled eggs?
Answer: Heat a pan to medium heat and melt some butter. Crack eggs into the pan and break the yolk so that the mixture in the pan has a nice variation. Continue slowly stirring the eggs. Every 30 seconds, remove the pan from the heat. Then return to the heat for 30 seconds. Continue this process and continue stirring until the eggs reach your preferred consistency.
Question: How do they measure performance?
Evidence: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. The detailed answers by the participants and available online.
For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question.
Answer: The answers are shown as follows:
* average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values
Question: Ways to use Pencil other than writing
Answer: Use Pencil to play truth or Dare
Use pencil as darts over a styrofoam target
Use pencil as chopsticks 
Use pencil as a pointer
Use pencil to block small holes
Question: Who are some famous Greek philosophers?
Answer: Plato, Aristotle and Socrates are all famous Greek philosophers
Question: Which characters belong to DC or Marvel Universe? Daredevil, Robin
Answer: Robin is DC, Daredevil is Marvel
Question: What is a Japanese dish of prepared rice, usually with some sugar and salt, accompanied by a variety of ingredients, such as seafood, often raw, and vegetables?
Evidence: Sushi (すし, 寿司, 鮨, 鮓, pronounced [sɯɕiꜜ] or [sɯꜜɕi]) is a Japanese dish of prepared vinegared rice (鮨飯, sushi-meshi), usually with some sugar and salt, accompanied by a variety of ingredients (ねた, neta), such as seafood, often raw, and vegetables. Styles of sushi and its presentation vary widely, but the one key ingredient is "sushi rice", also referred to as shari (しゃり), or sumeshi (酢飯).

The inventor of modern sushi is believed to be Hanaya Yohei, who invented nigiri-zushi, a type of sushi most known today, in which seafood is placed on hand-pressed vinegared rice, around 1824 in the Edo period (1603–1867). It was the fast food of the chōnin class in the Edo period.

Sushi is traditionally made with medium-grain white rice, though it can be prepared with brown rice or short-grain rice. It is very often prepared with seafood, such as squid, eel, yellowtail, salmon, tuna or imitation crab meat. Many types of sushi are vegetarian. It is often served with pickled ginger (gari), wasabi, and soy sauce. Daikon radish or pickled daikon (takuan) are popular garnishes for the dish.

Sushi is sometimes confused with sashimi, a similar dish in Japanese cuisine that consists of thinly sliced raw fish or occasionally meat.
Answer: Sushi is the answer to the question.
Question: What type of car should I buy if I live in a city?
Answer: When living in the city, it is important to drive a vehicle that is compact enough to fit in small parking spaces and one that has sufficient fuel efficiency to drive for longer periods of time without requiring additional fuel. This is due to the scarcity of gas stations within city limits.

Small to mid-sized sedans or other compact cars with either good fuel efficiency or electric batteries are best for city driving. Examples include Toyota Prius, Volkswagen Golf, or Tesla Model 3.

It is imperative to avoid large vehicles when driving in the city. Samples vehicles to avoid include Ford F-150 pick up truck, Chevrolet Suburban, or Hummer.
Question: What is mutually assured destruction?
Answer: Mutually assured destruction is the belief that two countries will not attack each other if each country has the ability to destroy the other with the same amount of military power. Mutually assured destruction or "MAD" is a principle from the era of the Cold War and is considered part of a US military doctrine. The policy is often applied to the use of nuclear weapons. For example, because both the United States and Russia have nuclear weapons that could annihilate large populations, neither uses them. To do so, would mean the end of both populations, and a large part of humanity.
Question: What does supporting fact supervision mean?
Evidence: We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.
the facts that are relevant for answering a particular question) are labeled during training.
Answer: The answers are shown as follows:
*  the facts that are relevant for answering a particular question) are labeled during training.
Question: What does the author conclude?
Evidence: This paper provided a taxonomy of stereotype-driven descriptions in the Flickr30K dataset. I have divided these descriptions into two classes: linguistic bias and unwarranted inferences. The former corresponds to the annotators' choice of words when confronted with an image that may or may not match their stereotypical expectancies. The latter corresponds to the tendency of annotators to go beyond what the physical data can tell us, and expand their descriptions based on their past experiences and knowledge of the world. Acknowledging these phenomena is important, because on the one hand it helps us think about what is learnable from the data, and on the other hand it serves as a warning: if we train and evaluate language models on this data, we are effectively teaching them to be biased.
I have also looked at methods to detect stereotype-driven descriptions, but due to the richness of language it is difficult to find an automated measure. Depending on whether your goal is production or interpretation, it may either be useful to suppress or to emphasize biases in human language. Finally, I have discussed stereotyping behavior as the addition of a contextual layer on top of a more basic description. This raises the question what kind of descriptions we would like our models to produce.
Depending on whether your goal is production or interpretation, it may either be useful to suppress or to emphasize biases in human language.
Answer: Depending on whether your goal is production or interpretation, it may either be useful to suppress or to emphasize biases in human language.
Question: How they complete a user query prefix conditioned upon an image?
Evidence: To adapt the FactorCell BIBREF4 for our purposes, we replace user embeddings with a low-dimensional image representation. Thus, we are able to modify each query completion to be personalized to a specific image representation. We extract features from an input image using a CNN pretrained on ImageNet, retraining only the last two fully connected layers. The image feature vector is fed into the FactorCell through the adaptation matrix. We perform beam search over the sequence of predicted characters to chose the optimal completion for the given prefix.
To adapt the FactorCell BIBREF4 for our purposes, we replace user embeddings with a low-dimensional image representation. Thus, we are able to modify each query completion to be personalized to a specific image representation.
Answer: The answers are shown as follows:
* we replace user embeddings with a low-dimensional image representation
Question: What does the decoder consist of?
Evidence: Our datasets can be used for various hate speech tasks. In this paper, we focus on generative hate speech intervention.
The goal of this task is to generate a response to hate speech that can mitigate its use during a conversation. The objective can be formulated as the following equation:
where $c$ is the conversation, $r$ is the corresponding intervention response, and $D$ is the dataset. This task is closely related to the response generation and dialog generation, though several differences exist including dialog length, language cadence, and word imbalances. As a baseline, we chose the most common methods of these two tasks, such as Seq2Seq and VAE, to determine the initial feasibility of automatically generate intervention responses. More recent Reinforcement Learning method for dialog generation BIBREF21 can also be applied to this task with slight modification. Future work will explore more complex, and unique models.
Similar to BIBREF21, a generative model is considered as an agent. However, different from dialog generation, generative intervention does not have multiple turns of utterance, so the action of the agent is to select a token in the response. The state of the agent is given by the input posts and the previously generated tokens. Another result due to this difference is that the rewards with regard to ease of answering or information flow do not apply to this case, but the reward for semantic coherence does. Therefore, the reward of the agent is:
where $rw(c,r)$ is the reward with regard to the conversation $c$ and its reference response $r$ in the dataset. $p(r|c)$ denotes the probability of generating response $r$ given the conversation $c$, and $p_{back}(c|r)$ denotes the backward probability of generating the conversation based on the response, which is parameterized by another generation network. The reward is a weighted combination of these two parts, which are observed after the agent finishing generating the response. We refer the readers to BIBREF21 for details.
The decoder consists of 2 GRU layers followed by a 3-layer MLP (Multi-Layer Perceptron).
Answer: The decoder consists of 2 GRU layers followed by a 3-layer MLP (Multi-Layer Perceptron).
Question: Describe to me in clear detail what the amazon logo looks like.
Answer: The Amazon logo is a curved, yellow arrow pointing to the right. The arrow head is separated from the stem. Sometimes the brand will be colored black instead but it is a well recognized logo regardless. It is a simple design and used throughout all products that Amazon owns including Amazon Prime, Amazon.com, Amazon Go, and more.
Question: How big dataset is used for training this system?
Evidence: We use MS COCO, Bing and Flickr datasets from BIBREF26 to train the model that generates questions. These datasets contain natural questions about images with the purpose of knowing more about the picture. As can be seen in the Figure FIGREF8, questions cannot be answered by only looking at the image. Each source contains 5,000 images with 5 questions per image, adding a total of 15,000 images with 75,000 questions. COCO dataset includes images of complex everyday scenes containing common objects in their natural context, but it is limited in terms of the concepts it covers. Bing dataset contains more event related questions and has a wider range of questions longitudes (between 3 and 20 words), while Flickr questions are shorter (less than 6 words) and the images appear to be more casual.
We use two datasets to train our chatbot model. The first one is the Persona-chat BIBREF15 which contains dialogues between two people with different profiles that are trying to know each other. It is complemented by the Cornell-movie dialogues dataset BIBREF27, which contains a collection of fictional conversations extracted from raw movie scripts. Persona-chat's sentences have a maximum of 15 words, making it easier to learn for machines and a total of 162,064 utterances over 10,907 dialogues. While Cornell-movie dataset contains 304,713 utterances over 220,579 conversational exchanges between 10,292 pairs of movie characters.
We use MS COCO, Bing and Flickr datasets from BIBREF26 to train the model that generates questions. These datasets contain natural questions about images with the purpose of knowing more about the picture. As can be seen in the Figure FIGREF8, questions cannot be answered by only looking at the image. Each source contains 5,000 images with 5 questions per image, adding a total of 15,000 images with 75,000 questions.
We use two datasets to train our chatbot model. The first one is the Persona-chat BIBREF15 which contains dialogues between two people with different profiles that are trying to know each other. It is complemented by the Cornell-movie dialogues dataset BIBREF27, which contains a collection of fictional conversations extracted from raw movie scripts. Persona-chat's sentences have a maximum of 15 words, making it easier to learn for machines and a total of 162,064 utterances over 10,907 dialogues. While Cornell-movie dataset contains 304,713 utterances over 220,579 conversational exchanges between 10,292 pairs of movie characters.
Answer: For the question generation model 15,000 images with 75,000 questions. For the chatbot model, around 460k utterances over 230k dialogues.
Question: Who are el Marinid ?
Evidence: The Marinid Sultanate was a Berber Muslim empire from the mid-13th to the 15th century which controlled present-day Morocco and, intermittently, other parts of North Africa (Algeria and Tunisia) and of the southern Iberian Peninsula (Spain) around Gibraltar. It was named after the Banu Marin (Arabic: بنو مرين, Berber: Ayt Mrin), a Zenata Berber tribe. The sultanate was ruled by the Marinid dynasty (Arabic: المرينيون al-marīniyyūn), founded by Abd al-Haqq I.
Answer: The Marinid was a berber muslim empire, they were founded in the mid-13th and rulled until the 15 century. They controlled Morocco, Algeria, Tunisia and Al Andalus around Gibraltar.
It was named after the Banu Marin, a zenata Berber Tribe.
Question: How many cents do I have if I have two nickels?
Answer: If you have two nickels, you have 10 cents.
Question: What is a Wonton?
Answer: A wonton is a type of Chinese dumpling that is generally served in a soup broth.  The dumpling filling is placed onto a thin dough wrapper, then the wrapper is then sealed to prevent the filling from spilling out when cooking.  The filling is commonly made with pork and shrimp.  Filled wonton dumplings are then placed into boiling water for cooking.  Wontons can also be deep fried with a variety of fillings.
Question: What is Scotland's largest lake?
Answer: Loch Ness is the largest lake with a volume of 7.45 km³.
Question: What is the Von Neumann architecture?
Answer: Nearly all modern computers are based on this famous architecture consisting of a central processing unit, a memory for storing instructions and data, and a bus to transmit data between components including input and output devices.
Question: Where was the first capital of Alaska?
Answer: Alaska's first capital was in Sitka, following the transfer of the Alaska territory from Russia to the United States in 1867, and remained the capital until 1906 when it transferred to Juneau.
Question: What is the easy-adopting debiasing training framework?
Evidence: Natural language inference (NLI) is a widely-studied problem in natural language processing. It aims at comparing a pair of sentences (i.e. a premise and a hypothesis), and inferring the relationship between them (i.e., entailment, neutral and contradiction). Large-scaled datasets like SNLI BIBREF0 and MultiNLI BIBREF1 have been created by crowd-sourcing and fertilized NLI research substantially.
However, several works BIBREF2, BIBREF3, BIBREF4 have pointed out that crowd-sourcing workers have brought a bias pattern named annotation artifacts in these NLI datasets. Such artifacts in hypotheses can reveal the labels and make it possible to predict the labels solely by looking at the hypotheses. For example, models trained on SNLI with only the hypotheses can achieve an accuracy of 67.0%, despite the always predicting the majority-class baseline is only 34.3% BIBREF2.
Classifiers trained on NLI datasets are supposed to make predictions by understanding the semantic relationships between given sentence pairs. However, it is shown that models are unintentionally utilizing the annotation artifacts BIBREF4, BIBREF2. If the evaluation is conducted under a similar distribution as the training data, e.g., with the given testing set, models will enjoy additional advantages, making the evaluation results over-estimated. On the other hand, if the bias pattern cannot be generalized to the real-world, it may introduce noise to models, thus hurting the generalization ability.
In this paper, we use cross-dataset testing to better assess models' generalization ability. We investigate the impacts of annotation artifacts in cross-dataset testing. Furthermore, we propose an easy-adopting debiasing training framework, which doesn't require any additional data or annotations, and apply it to the high-performing Densely Interactive Inference Network BIBREF5. Experiments show that our method can effectively mitigate the bias pattern and improve the cross-dataset generalization ability of models. To the best of our knowledge, our work is the first attempt to alleviate the annotation artifacts without any extra resources.
Furthermore, we propose an easy-adopting debiasing training framework, which doesn't require any additional data or annotations, and apply it to the high-performing Densely Interactive Inference Network.
Answer: It doesn't require any additional data or annotations, and apply it to the high-performing Densely Interactive Inference Network.
Question: Given the paragraph about the Grateful Dead, tell me when they were founded, who were the original members, and what other members were in the group.
Evidence: The Grateful Dead was founded in the San Francisco Bay Area amid the rise of the counterculture of the 1960s. The founding members were Jerry Garcia (lead guitar, vocals), Bob Weir (rhythm guitar, vocals), Ron "Pigpen" McKernan (keyboards, harmonica, vocals), Phil Lesh (bass, vocals), and Bill Kreutzmann (drums). Members of the Grateful Dead, originally known as the Warlocks, had played together in various Bay Area ensembles, including the traditional jug band Mother McCree's Uptown Jug Champions. Lesh was the last member to join the Warlocks before they changed their name to the Grateful Dead; replacing Dana Morgan Jr., who had played bass for a few gigs. Drummer Mickey Hart and non-performing lyricist Robert Hunter joined in 1967. With the exception of McKernan, who died in 1973, and Hart, who took time off from 1971 to 1974, the core of the band stayed together for its entire 30-year history. The other official members of the band are Tom Constanten (keyboards; 1968–1970), John Perry Barlow (non-performing lyricist; 1971–1995), Keith Godchaux (keyboards, occasional vocals; 1971–1979), Donna Godchaux (vocals; 1972–1979), Brent Mydland (keyboards, vocals; 1979–1990), and Vince Welnick (keyboards, vocals; 1990–1995). Bruce Hornsby (accordion, piano, vocals) was a touring member from 1990 to 1992, as well as a guest with the band on occasion before and after the tours.
Answer: The Grateful Dead were founded in the 1960's as the Warlocks by Jerry Garcia, Bob Weir, Ron "Pigpen" McKernan, Phil Lesh and Bill Kreutzmann. Over the years, the band had many other members, including Tom Constanten, John Perry Barlow, Keith Godchaux, Donna Godchaux, Brent Mydland, Vince Welnick and Bruce Hornsby.
Question: Who is Hermenegildo Sosa and what is he known for?
Evidence: Hermenegildo Sosa (born April 12, 1946) is a Mexican painter and art professor who is best known for colorful depictions of Mexican rural landscapes, especially those of his home state of Tlaxcala. He was born there into a farming family, whose economic condition delayed his education as he had to work from a young age. In his teens he arrived to Mexico City to work as a domestic, but this allowed him to attend school, including painting classes. Eventually, he entered the Escuela Nacional de Pintura, Escultura y Grabado "La Esmeralda", where he eventually gained a career as a professor.
Answer: Hermenegildo Sosa is a Mexican painter and art professor born in 1946. He is best known for his colorful depictions of Mexican rural landscapes, especially those of him home state in Tlaxcala.
Question: For the purposes of this paper, how is something determined to be domain specific knowledge?
Evidence: Amazon Reviews Dataset BIBREF24 is a large dataset with millions of reviews from different product categories. For our experiments, we consider a subset of 20000 reviews from the domains Cell Phones and Accessories(C), Clothing and Shoes(S), Home and Kitchen(H) and Tools and Home Improvement(T). Out of 20000 reviews, 10000 are positive and 10000 are negative. We use 12800 reviews for training, 3200 reviews for validation and 4000 reviews for testing from each domain.
Amazon Reviews Dataset BIBREF24 is a large dataset with millions of reviews from different product categories. For our experiments, we consider a subset of 20000 reviews from the domains Cell Phones and Accessories(C), Clothing and Shoes(S), Home and Kitchen(H) and Tools and Home Improvement(T). Out of 20000 reviews, 10000 are positive and 10000 are negative. We use 12800 reviews for training, 3200 reviews for validation and 4000 reviews for testing from each domain.
Answer: reviews under distinct product categories are considered specific domain knowledge
Question: What makes the banded bullfrog special
Answer: The banded bullfrog (Kaloula pulchra) is a species of frog in the narrow-mouthed frog family Microhylidae. Native to Southeast Asia, it is also known as the Asian painted frog, digging frog, Malaysian bullfrog, common Asian frog, and painted balloon frog. In the pet trade, it is sometimes called the chubby frog. Adults measure 5.4 to 7.5 cm (2.1 to 3.0 in) and have a dark brown back with stripes that vary from copper-brown to salmon pink.

The banded bullfrog lives at low altitudes and is found in both urban and rural settings, as well as in forest habitats. They bury themselves underground during dry periods and emerge after heavy rainfall to emit calls and breed. They feed primarily on ants and termites; predators of adults and tadpoles include snakes, dragonfly larvae, and snails. When threatened, they inflate their lungs and secrete a noxious white substance. The species is prevalent in the pet trade and is a potential invasive species being introduced in Taiwan, the Philippines, Guam, Singapore, Borneo, and Sulawesi.