DataHammer/scimrc huggingface dataset:
Question: Why the agreement between the sources was low?
Evidence: We created a new dataset called CW-USPD-2016 (check-worthiness in the US presidential debates 2016) for finding check-worthy claims in context. In particular, we used four transcripts of the 2016 US election: one vice-presidential and three presidential debates. For each debate, we used the publicly-available manual analysis about it from nine reputable fact-checking sources, as shown in Table TABREF7. This could include not just a statement about factuality, but any free text that journalists decided to add, e.g., links to biographies or behavioral analysis of the opponents and moderators. We converted this to binary annotation about whether a particular sentence was annotated for factuality by a given source. Whenever one or more annotations were about part of a sentence, we selected the entire sentence, and when an annotation spanned over multiple sentences, we selected each of them.
Ultimately, we ended up with a dataset of four debates, with a total of 5,415 sentences. The agreement between the sources was low as Table TABREF8 shows: only one sentence was selected by all nine sources, 57 sentences by at least five, 197 by at least three, 388 by at least two, and 880 by at least one. The reason for this is that the different media aimed at annotating sentences according to their own editorial line, rather than trying to be exhaustive in any way. This suggests that the task of predicting which sentence would contain check-worthy claims will be challenging. Thus, below we focus on a ranking task rather than on absolute predictions. Moreover, we predict which sentence would be selected (i) by at least one of the media, or (ii) by a specific medium.
Note that the investigative journalists did not select the check-worthy claims in isolation. Our analysis shows that these include claims that were highly disputed during the debate, that were relevant to the topic introduced by the moderator, etc. We will make use of these contextual dependencies below, which is something that was not previously tried in related work.
Ultimately, we ended up with a dataset of four debates, with a total of 5,415 sentences. The agreement between the sources was low as Table 2 shows: only one sentence was selected by all nine sources, 57 sentences by at least five, 197 by at least three, 388 by at least two, and 880 by at least one. The reason for this is that the different media aimed at annotating sentences according to their own editorial line, rather than trying to be exhaustive in any way.
Answer: The reason for this is that the different media aimed at annotating sentences according to their own editorial line, rather than trying to be exhaustive in any way.
Question: When was slavery abolished in the United States?
Evidence: After the United States was founded in 1776, abolition of slavery occurred in the Northern United States. The country was split into slave and free states. Still, slavery was not finally ended throughout the nation until near the end of the American Civil War and the passage of the Thirteenth Amendment in 1865.
Answer: Slavery was abolished via the Thirteenth Amendment in 1865 near the end of the American Civil War
Question: https://en.wikipedia.org/wiki/Narendra_Modi#Public_perception_and_image
Evidence: A vegetarian and teetotaler, Modi has a frugal lifestyle and is a workaholic and introvert. A person named Badri Meena has been his cook since 2002. Modi used to maintain a close and publicised relationship with his centenarian mother, Hiraben. Modi's 31 August 2012 post on Google Hangouts made him the first Indian politician to interact with citizens on a live chat. Modi has also been called a fashion-icon for his signature crisply ironed, half-sleeved kurta, as well as for a suit with his name embroidered repeatedly in the pinstripes that he wore during a state visit by US President Barack Obama, which drew public and media attention and criticism. Modi's personality has been variously described by scholars and biographers as energetic, arrogant, and charismatic.
Answer: Among his followers, Narendra Modi is regarded as a charismatic leader with excellent communication abilities. He has, however, also come under fire for how he has handled intercommunal violence and for pushing a Hindu nationalist agenda. Modi's reputation has been strengthened by the success of his economic policies in Gujarat, and he has sought to build a solid international reputation through travel and social media. Critics and protests have been sparked by his government's execution of contentious measures including demonetization and the Citizenship Amendment Act. Despite this, Modi is still a well-liked figure in Indian politics and has been elected prime minister more than once.
Question: Are these things you would find in a jungle? Categorize them as Yes, Maybe and No Way:
Rivers
Fence
Trees
Iphones
Subway
Animals
Mosquitos
Wildfires
Mountains
Electric scooters
Flora
Crocodiles
Trump Tower
Fauna
Answer: Yes:
Trees
Animals
Mosquitos
Flora
Fauna

Maybe:
Wildfires
Mountains
Rivers
Fence
Crocodiles

No Way:
Iphones
Subway
Electric scooters
Trump Tower
Question: How can I become a pilot?
Answer: The first step to become a private or commercial pilot is to join a flight school and get a license. Pilots must also meet basic medical requirements to fly, applying for a medical certificate.
Private pilots can fly light aircraft such as Cessnas and Pipers for leisure. They can fly solo or take passengers with them. Commercial pilots undergo further training to private pilots, allowing them to get a Commercial Pilots Licence (CPL) to fly for companies and airlines.
Question: Write a April Fools' joke for a data platform company
Answer: At [Company Name], we've been working on data for many years to solve the problems of storing and analyzing it reliably at scale. These problems are really hard though, so many enterprises struggle with them. This is why we are now introducing LLM-DB: a database based on an AI model that simply generates new data for you if it loses any. LLM-DB dramatically lowers the storage costs over existing methods, while giving each executive in your company exactly the data they want to see. Try it today at [URL].
Question: What is a automatic winding mechanical watch?
Answer: A mechanical watch utilizes a non-electronic mechanism to provide the time of day.  A mainspring is used to store energy in order for the mechanism to operate.  An automatic winding mechanical watch utilizes a free rotating weight to wind the mainspring as the watch is worn on the wrist.  In contrast, a manual winding watch requires the wearer to manually wind the mainspring.
Question: What are the major advantages of their estimation scheme?
Evidence: We demonstrated that the sentence–level context in which a concept appears is indicative of its controversiality. This follows BIBREF10, who show this for concept abstractness and suggest to explore further properties identifiable in this way. Importantly, we observed that this method may pick up signals which are not directly related to the property of interest. For example, since many controversial concepts have to do with religion, part of what this method may learn is thematic relatedness to religion. However, when controlling for this effect, the drop in accuracy is fairly small.
The major advantages of our estimation scheme are its simplicity and reliance on abundantly accessible features. At the same time, its accuracy is similar to state-of-the-art classifiers, which depend on complex meta-data, and rely on sophisticated - in some cases impractical - algorithmic techniques. Because the features herein are so simple, our estimators are convertible to any corpus, in any language, even of moderate size.
Recently, IBM introduced Project Debater BIBREF15, an AI system that debates humans on controversial topics. Training and evaluating such a system undoubtedly requires an extensive supply of such topics, which can be enabled by the automatic extraction methods suggested here as well as the new datasets.
The major advantages of our estimation scheme are its simplicity and reliance on abundantly accessible features.
Answer: Its simplicity and reliance on abundantly accessible features.
Question: If the Curated Golden Square was built as planned, what would the average cost (rounded to the nearest dollar) per square foot have been?
Evidence: In 2019, Singh had stated his next venture, set to open October 2019, was Curated Golden Square, described as a "$100 million, 30,000 square foot apartment hotel". In a 2021 follow up interview with Vanity Fair, Singh claimed that the COVID-19 pandemic was the reason the venture did not move forward. The site in question, 37 Golden Square, is a collection of 23 apartments developed by Halamar in 2019.
Answer: $3,333 per square foot = $100,000,000/ 30,000 square feet
Question: Why is it difficult to build an ASR model to handle speech in Vietnamese?
Evidence: Informal speech is different from formal speech, especially in Vietnamese due to many conjunctive words in this language. Building an ASR model to handle such kind of speech is particularly difficult due to the lack of training data and also cost for data collection. There are two components of an ASR system that contribute the most to the accuracy of it, an acoustic model and a language model. While collecting data for acoustic model is time-consuming and costly, language model data is much easier to collect.
The language model training for the Automatic Speech Recognition (ASR) system usually based on corpus crawled on formal text, so that some conjunctive words which often used in conversation will be missed out, leading to the system is getting biased to writing-style speech.
In this paper, we present our attempt to mitigate the problems using a large scale data set and a language model combination technique that only require a small amount of conversation data but can still handle very well conversation speech.
Informal speech is different from formal speech, especially in Vietnamese due to many conjunctive words in this language. Building an ASR model to handle such kind of speech is particularly difficult due to the lack of training data and also cost for data collection.
Answer: Because the lack of training data and also cost for data collection.
Question: Give me a list of dog names
Answer: Sparky, Rusty, Tank, Fred, Coco, Bailey, Pierre, Libby
Question: What is API abuse?
Answer: API abuse refers to different ways an attacker misuses a software or system. In most cases such behavior violates Terms of Services. Some example of abusive behavior are: access to to sensitive data, bulk data exfiltration (scraping), crypto-mining.
Question: Who was Thomas Ransom
Answer: Thomas Harvey Ransom (August 8, 1870 – June 3, 1946) was an American football and basketball coach and a doctor.
Question: What is Bubble tea?
Evidence: Bubble tea (also known as pearl milk tea, bubble milk tea, tapioca milk tea, boba tea, or boba; Chinese: 珍珠奶茶; pinyin: zhēnzhū nǎichá, 波霸奶茶; bōbà nǎichá) is a tea-based drink that originated in Taiwan in the early 1980s. Taiwanese immigrants brought it to the United States in the 1990s, initially in California through regions like Los Angeles County, but the drink has also spread to other countries where there is a large East Asian diaspora population.

Bubble tea most commonly consists of tea accompanied by chewy tapioca balls ("boba" or "pearls"), but it can be made with other toppings as well, such as grass jelly, aloe vera, red bean, or popping boba. It has many varieties and flavors, but the two most popular varieties are pearl black milk tea and pearl green milk tea ("pearl" signifies the tapioca balls at the bottom).
Answer: Boba tea is a very delicious drink originally from Taiwan. It started with cold milk tea with tapioca balls in the early 1990s. When the demand gets so popular, the boba tea store owner tries to be fancier by adding different toppings such as grass jelly, pudding cluster, aloe vera, red beans, gummy, mochi and fruit bites. In addition, many store owners become more creative by using different flavors of tea (black, green, oolong, jasmine and other fragrance tea)  as a drink base to outstand other competitors.
Question: How does it know the subgoals and which low-level instruction corresponds to the current subgoal?
Evidence: Specifically, since each step-by-step language instruction corresponds to a subgoal, we use a Transformer-based Vaswani et al. (2017) subgoal parser to process the text and predict the subgoal g ∈ T is for navigation or not.****Figure 1: Exploration Flow: The agent aims to explore the environment given the guidance from low-level language instructions, previous exploration actions, and the explored area where the agent has visited and made observations. Execution Flow: Given the language instructions and the affordance-aware semantic representation (i.e., semantic map) acquired during exploration, the agent executes the subgoals sequentially. It uses a planning module (which consumes the semantic map) for navigation subgoals and an object interaction transformer for other subgoals.
Answer: In Sec. 4.1, we introduce that the agent processes each instruction to predict whether each subgoal is for navigation or not and the target object types for the navigation subgoals, by using the subgoal parser and the target object parser. The details of these modules are in section 4.4. See Fig. 1 (right) as a high-level illustration of the data flow in the execution phase.We will reorganize Sec. 4 to improve the presentation.
Question: Tell me whether those countries are in Europe or America: England, United states, France, Spain, Canada, Italy, Colombia, Peru
Answer: England is in Europe, United States is in America, France is in Europe, Spain is in Europe, Canada is in America, Italy is in Europe, Colombia is in America, Peru is in America
Question: Why do people drink coffee in the morning?
Answer: Drinking coffee helps people stay awake and get energy
Question: Which other embeddings do they compare against?
Evidence: To demonstrate the effectiveness of the proposed solutions, we evaluated our model along with the following strong baselines. ( INLINEFORM0 ) Topology only embeddings: MMB BIBREF45 , DeepWalk BIBREF1 , LINE BIBREF33 , Node2vec BIBREF46 . ( INLINEFORM1 ) Joint embedding of topology & text: Naive combination, TADW BIBREF5 , CENE BIBREF6 , CANE BIBREF9 , WANE BIBREF10 , DMTE BIBREF34 . A brief summary of these competing models is provided in the Supplementary Material (SM).
Topology only embeddings: MMB BIBREF45 , DeepWalk BIBREF1 , LINE BIBREF33 , Node2vec BIBREF46 . ( INLINEFORM1 ) Joint embedding of topology & text: Naive combination, TADW BIBREF5 , CENE BIBREF6 , CANE BIBREF9 , WANE BIBREF10 , DMTE BIBREF34 . 
Answer: The answers are shown as follows:
* MMB
* DeepWalk
* LINE
*  Node2vec
* TADW
* CENE
* CANE
* WANE
* DMTE
Question: Why you should sleep at least 7 hours
Answer: Because your body need rest minimum 7 hours to operate properly everyday
Question: How to optimize space usage in garage
Answer: Based on the size of the garage the most left over space you can use to anything else will be the space left after the cars are parked. And that space is usually by the wall. However; there is one interesting aspect that can be considered and that's the vertical space. Usually the space close to the ceiling. One of the things you can do is look for study heavy duty hooks that can be put high up on the garage walls. These hooks than can be used to hold Bicycles, Ladders, Backyard equipments and several other things. Get creative !!
Question: Why kids ask more questions?
Answer: Kids are more curious and enthusiastic compared to adults as they are fairly new to the world and wants to understand the world around them. They are eager to grasp all the information around them and develop their skills to live. According to psychology studies, it is good to encourage kids to ask more questions as it would help building rapport and establishing trust with them.
Question: Write a response to a veteran interested in renting my house
Answer: Hello,

Thank you for your inquiry, it’s so nice to meet you. We’d love to keep our home in the vet community, thank you for your service! When would you prefer to move in?

I’ll send you our rental application for you to complete as a next step.
Question: What is the size of their collected dataset?
Evidence: Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of passenger intents are identified and annotated as: Set/Change Destination, Set/Change Route (including turn-by-turn instructions), Go Faster, Go Slower, Stop, Park, Pull Over, Drop Off, Open Door, and Other (turn music/radio on/off, open/close window/trunk, change AC/temp, show map, etc.). Relevant slots are identified and annotated as: Location, Position/Direction, Object, Time-Guidance, Person, Gesture/Gaze (this, that, over there, etc.), and None. In addition to utterance-level intent types and their slots, word-level intent keywords are annotated as Intent as well. We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators.
We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators.
Answer: 3347 unique utterances
Question: Classify the products based on the brand.
Android, safari, chrome, iOS, Alexa, prime videos
Answer: Android, chrome - Google
Safari, iOS - Apple
Prime videos - Amazon
Question: What contribution does the paper assess?
Evidence: In recent years, there has been a movement to leverage social medial data to detect, estimate, and track the change in prevalence of disease. For example, eating disorders in Spanish language Twitter tweets BIBREF0 and influenza surveillance BIBREF1 . More recently, social media has been leveraged to monitor social risks such as prescription drug and smoking behaviors BIBREF2 , BIBREF3 , BIBREF4 as well as a variety of mental health disorders including suicidal ideation BIBREF5 , attention deficient hyperactivity disorder BIBREF6 and major depressive disorder BIBREF7 . In the case of major depressive disorder, recent efforts range from characterizing linguistic phenomena associated with depression BIBREF8 and its subtypes e.g., postpartum depression BIBREF5 , to identifying specific depressive symptoms BIBREF9 , BIBREF10 e.g., depressed mood. However, more research is needed to better understand the predictive power of supervised machine learning classifiers and the influence of feature groups and feature sets for efficiently classifying depression-related tweets to support mental health monitoring at the population-level BIBREF11 .
This paper builds upon related works toward classifying Twitter tweets representing symptoms of major depressive disorder by assessing the contribution of lexical features (e.g., unigrams) and emotion (e.g., strongly negative) to classification performance, and by applying methods to eliminate low-value features.
This paper builds upon related works toward classifying Twitter tweets representing symptoms of major depressive disorder by assessing the contribution of lexical features (e.g., unigrams) and emotion (e.g., strongly negative) to classification performance, and by applying methods to eliminate low-value features.
Answer: Assess the contribution of lexical features (e.g., unigrams) and emotion (e.g., strongly negative) to classifying Twitter tweets representing symptoms of major depressive disorder.
Question: What shortcoming is in the work of Lloret et al.?
Evidence: Lloret.2013 describe several experiments to crowdsource reference summaries. Workers are asked to read 10 documents and then select 10 summary sentences from them for a reward of $0.05. They discovered several challenges, including poor work quality and the subjectiveness of the annotation task, indicating that crowdsourcing is not useful for this purpose.
To overcome these issues, we introduce a new task design, low-context importance annotation, to determine summary-worthy parts of documents. Compared to Lloret et al.'s approach, it is more in line with crowdsourcing best practices, as the tasks are simple, intuitive and small BIBREF30 and workers receive reasonable payment BIBREF31 . Most importantly, it is also much more efficient and scalable, as it does not require workers to read all documents in a cluster.
Lloret et al. (2013) describe several experiments to crowdsource reference summaries. Workers are asked to read 10 documents and then select 10 summary sentences from them for a reward of $0.05. They discovered several challenges, including poor work quality and the subjectiveness of the annotation task, indicating that crowdsourcing is not useful for this purpose.
Answer: Including poor work quality and the subjectiveness of the annotation task, indicating that crowdsourcing is not useful for this purpose.
Question: Do the authors mention any confounds to their study?
Evidence: A key challenge is to obtain an annotated dataset. We use hashtag-based supervision so that the authors of the tweets mention if they were drunk at the time of posting a tweet. We create three datasets by using different strategies that are related to the use of hashtags. We then present SVM-based classifiers that use N-gram and stylistic features such as capitalisation, spelling errors, etc. Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. To the best of our knowledge, this is a first study that shows the feasibility of text-based analysis for drunk-texting prediction.
Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work.
Answer: No.
Question: Who is the heir to the throne in England.
Answer: As the oldest son of King Charles, William, the Price of Wales, is heir to the throne.
Question: I am looking for a new religion to convert to, what do you recommend?
Answer: The choice of religion is very personal. It depends on many personal and societal factors that vary among individuals. However, just based on historical data, Polytheism was the typical form of religion for most of human's history. It has been practiced since the first human gatherings, with records going back to 12,000 years BC. Even today polytheistic religions collectively have the largest number of followers throughout the world. If you just go by numbers, I would recommend a polytheistic religion. Fortunately, there many to chose from.