DataHammer/scimrc huggingface dataset:
Question: How big is the performance difference between this method and the baseline?
Evidence: FLOAT SELECTED: Table 3: Compare our model with other baseline methods on different types of datasets. The evaluation metric is micro F1.
FLOAT SELECTED: Table 3: Compare our model with other baseline methods on different types of datasets. The evaluation metric is micro F1.
Answer: Comparing with the highest performing baseline: 1.3 points on ACE2004 dataset, 0.6 points on CWEB dataset, and 0.86 points in the average of all scores.
Question: Classify each of the following as either a true statement or a false statement: 
1. Albert Einstein was a Chinese mathematician that lived in the 19th and 20th century.
2. Pigs are not mammals.
3. This question contains four statements that need classification.
Answer: Statement number 1 is mostly false, since Albert Einstein was neither Chinese or a mathematician. He was a German theoretical physicist. However, he did in fact live during the 19th and 20th centuries.

Statement number 2 is false. Pigs are in fact mammals. 

Statement number 3 is false. This question actually contained 3 statements.
Question: What is the main purpose of collecting the data set?
Evidence: From the point of view of dialog systems providing general information from a knowledge base, the most limiting factor is that a large portion of the questions is understood poorly.
Current approaches BIBREF3 , BIBREF4 can only achieve around 50% accuracy on some question answering datasets. Therefore, we think that there is a room for improvements which can be achieved by interactively asking for additional information in conversational dialogs with users. This extra information can be used for improving policies of dialog systems. We call this approach the interactive learning from dialogs.
We can improve dialog systems in several aspects through interactive learning in a direct interaction with users. First, the most straightforward way obviously is getting the correct answer for questions that the system does not know. We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. We hypothesize that users are willing to give such explanations because it could help them to find answers for their own questions. The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording. This area is extensively studied for the purposes of information retrieval BIBREF5 , BIBREF6 .
The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.
The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.
Answer: The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.
Question: Given a reference text about Michał Rozenfeld, tell me what he did before the war and where he's buried.
Evidence: Michał Rozenfeld (born 1916, died September 2, 1943, in Krawcowizna) – a Jewish resistance activist during the Second World War, participant of the Warsaw Ghetto uprising, partisan of the Mordechai Anielewicz Unit of the People's Guard.

Before the outbreak of the war, he studied psychology with, among others, Tadeusz Kotarbiński. After the outbreak of World War II, Rozenfeld was sent to the Warsaw Ghetto, where he worked as a teacher. He belonged to the Polish Workers' Party and was a member of the Main Command of the Jewish Combat Organization. He stayed in a bunker with Mordechai Anielewicz at 18 Miła Street. On May 18, 1943, together with a group of Jewish fighters, he got through the sewers to Prosta Street.

After the fall of the uprising, Rozenfeld took refuge in forests near Wyszków, where he fought in a unit of the People's Guard. Together with his companions he was handed over to the Germans by a forest ranger from Krawcowizna. Twelve partisans, including Michał Rozenfeld, were killed at that time.

In 1945 he was posthumously awarded the Silver Cross of the Order of Virtuti Militari.

Rozenfeld is buried in a mass grave of the People's Guard partisans in the Jewish cemetery on Okopowa Street in Warsaw (quarter 31, row 3).
Answer: Michał Rozenfeld studied psychology before being sent to the Warsaw Ghetto during the war. He is buried at the Jewish Cemetery on Okopowa Street in Warsaw.
Question: Write a brief passage on how to make french toast
Answer: In order to make a french toast, you need egg, vanilla extract, cinnamon, milk and few slices of bread. To start with, beat an egg along with one teaspoon of vanilla essence and a pinch of cinnamon. Add 1/4 cup of milk and stir the contents. Dip the bread slices in the mixture so that both sides of the bread are covered evenly. Heat a nonstick griddle and grease it lightly with butter. Place the bread on the griddle until it is brown on both sides. Serve it with maple syrup. Your delicious french toast is ready!
Question: How large is raw corpus used for training?
Evidence: As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.
We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16.
As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. 
From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO.
Answer: The answers are shown as follows:
* 100 million sentences
Question: how can you generate passive income?
Answer: Compared with active income which you earn in exchange of your time and efforts, passive income comes passively as you can sit aside after proper initial setups. Some argue that time-average investing in index funds is considered passive investing. Time-average-investing or dollar cost averaging investing means you invest a fixed amount in the index fund recurringly with a given frequency, usually monthly or biweekly, regardless of the fund price. You can set it up with auto-payment or auto-investment through your brokerage and let it build your wealth over time. Index funds mirror the performance of a collection of selected stocks, thus providing a diversified strategy by its nature. SP500 index has a proven performance record that beats most active fund managers. In addition, real estate investment is somehow passive if you leave all the property management to professionals. However, the fact that it requires a certain amount of equity to get started makes younger investors shy away from it. For the younger generation, generating passive income from content they publish on social media has become a popular choice. For example, publishing videos to your YouTube channel requires time and effort to create the content. But once the video is live, it starts to generate income streams with more and more people watching your videos for years to come.
Question: What is the timeline for the band Level 42?
Evidence: Level 42 is an English jazz-funk band formed on the Isle of Wight in 1979. They had a number of UK and worldwide hits during the 1980s and 1990s.

Their highest-charting single in the UK was "Lessons in Love", which reached number three on the UK Singles Chart, and number 12 on the US Billboard Hot 100 chart, upon its release in 1986. An earlier single, "Something About You", was their most successful chart-wise in the United States, reaching number 7 on the Billboard Hot 100 chart.

After much success as a live and studio band in the 1980s, Level 42's commercial profile diminished during the early 1990s following a series of personnel changes and musical shifts. Disbanding in 1994, the band reformed in 2001.
Answer: Level 42 formed on 1979. They enjoyed success throughout the 1980s but had disbanded by 1994. The band reformed in 2001.
Question: Classify each one of the following as bird or fish - sparrow, whale, tuna, parrot.
Answer: Sparrow and parrot are birds and whale and tuna are fishes in the list
Question: Do the authors report performance of conditional bert on tasks without data augmentation?
Evidence: Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does. For we masked words randomly, the masked words may be label-sensitive or label-insensitive. If label-insensitive words are masked, words predicted through BERT may not be compatible with original labels. The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks.
Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does.
Answer: Yes.
Question: What is a verb?
Answer: A verb is a word that in syntax generally conveys an action, an occurrence, or a state of being. In the usual description of English, the basic form, with or without the particle to, is the infinitive. In many languages, verbs are inflected to encode tense, aspect, mood, and voice
Question: Who is Gavin Newsom?
Evidence: Gavin Christopher Newsom (born October 10, 1967) is an American politician and businessman who has been the 40th governor of California since 2019. A member of the Democratic Party, he served as the 49th lieutenant governor of California from 2011 to 2019 and the 42nd mayor of San Francisco from 2004 to 2011.
Answer: Gavin Christopher Newsom (born October 10, 1967) is an American politician and businessman who has been the 40th governor of California since 2019. A member of the Democratic Party, he served as the 49th lieutenant governor of California from 2011 to 2019 and the 42nd mayor of San Francisco from 2004 to 2011.

Newsom attended Redwood High School and graduated from Santa Clara University. After graduation, he founded the PlumpJack wine store with billionaire heir and family friend, Gordon Getty, as an investor. The PlumpJack Group grew to manage 23 businesses, including wineries, restaurants and hotels. Newsom began his political career in 1996, when San Francisco mayor Willie Brown appointed him to the city's Parking and Traffic Commission. Brown appointed Newsom to fill a vacancy on the Board of Supervisors the next year and Newsom was elected to the board in 1998, 2000 and 2002.

In 2003, at age 36, Newsom was elected the 42nd mayor of San Francisco, the city's youngest in a century. He was reelected in 2007 with 72% of the vote.

Newsom was elected lieutenant governor of California in 2010, defeating incumbent Republican Abel Maldonado, and reelected in 2014. He was elected governor in the 2018 election. He faced criticism for his personal behavior and leadership during the COVID-19 pandemic, which was followed by an attempt to recall him from office. He prevailed in the 2021 recall election, "emphatically beating" what he labeled the Republican effort to remove him. Newsom was elected to a second term in the 2022 election.

Newsom hosted The Gavin Newsom Show on Current TV from 2012 to 2013 and wrote the 2013 book Citizenville, about using digital tools for democratic change. Political science analysis has suggested he is moderate relative to almost all Democratic legislators in California.
Question: What is hidden at Oak Island?
Evidence: Oak Island has been a subject for treasure hunters ever since the late 1700s, with rumours that Captain Kidd's treasure was buried there. While there is little evidence to support what went on during the early excavations, stories began to be published and documented as early as 1856. Since that time there have been many theories that extend beyond that of Captain Kidd which include among others religious artifacts, manuscripts, and Marie Antoinette's jewels. The "treasure" has also been prone to criticism by those who have dismissed search areas as natural phenomena.
Answer: Oak Island is rumored to have Captain Kidd's treasure buried. However despite numerous excavation attempts over the past 100 years nothing has been discovered. There are other theories which claim religious artifacts, manuscripts, or Marie Antionette's jewels are hidden at Oak Island.
Question: Which is a species of fish? Javelin or Discus
Answer: Javelin
Question: What does it mean to be customer obsessed or achieve customer obsession?
Answer: Customer obsession is a state of mind. To embody a Customer Obsessed state of mind, one must consider the customer they are obsessed with in all things. For example, when at the grocery store you see an advertisement from a competitor of your customer, a customer-obsessed state of mind will naturally conceive of methods that a customer might employ, out-competing the viewed advertisement. When going to sleep, the customer and their needs are the last thoughts that drift from your mind as you fall asleep. When waking up, the customer should be the first thought to naturally occur as you begin to consider your activities. When eating your lunch, you should imagine what your customer is eating for their lunch.

When your customer is on your mind every waking moment, you've begun to walk the path of being customer obsessed. Once you've begun dreaming of your customer, then you will then have achieved true customer obsession.
Question: Identify which car manufacturer is German or American: Mercedes-Benz, Chevrolet
Answer: Chevrolet is American, Mercedes-Benz is German
Question: What is the performance of the model?
Evidence: The test accuracy curves with the noise rates [0, $0.1$, $0.2$, $0.3$, $0.4$, $0.5$] are shown in Figure FIGREF13. From the figure, we can see that the test accuracy drops from around 0.8 to 0.5 when the noise rate increases from 0 to 0.5, but our NetAb outperforms CNN. The results clearly show that the performance of the CNN drops quite a lot with the noise rate increasing.
The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop. The results demonstrate the superiority of NetAb. (2) NetAb outperforms the baselines designed for learning with noisy labels. These baselines are inferior to ours as they were tailored for image classification. Note that we found no existing method to deal with noisy labels for SSC. Training Details. We use the publicly available pre-trained embedding GloVe.840B BIBREF48 to initialize the word vectors and the embedding dimension is 300.
FLOAT SELECTED: Table 2: Accuracy (ACC) of both classes, F1 (F1 pos) of positive class and F1 (F1 neg) of negative class on clean test data/sentences. Training data are real noisy-labeled sentences.
FLOAT SELECTED: Figure 2: Accuracy (ACC) on clean test data. For training, the labels of clean data are flipped with the noise rates [0, 0.1, 0.2, 0.3, 0.4, 0.5]. For example, 0.1means that 10% of the labels are flipped. (Color online)
The test accuracy curves with the noise rates [0, $0.1$, $0.2$, $0.3$, $0.4$, $0.5$] are shown in Figure FIGREF13. From the figure, we can see that the test accuracy drops from around 0.8 to 0.5 when the noise rate increases from 0 to 0.5, but our NetAb outperforms CNN
The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop. The results demonstrate the superiority of NetAb. (2) NetAb outperforms the baselines designed for learning with noisy labels.
FLOAT SELECTED: Table 2: Accuracy (ACC) of both classes, F1 (F1 pos) of positive class and F1 (F1 neg) of negative class on clean test data/sentences. Training data are real noisy-labeled sentences.
FLOAT SELECTED: Figure 2: Accuracy (ACC) on clean test data. For training, the labels of clean data are flipped with the noise rates [0, 0.1, 0.2, 0.3, 0.4, 0.5]. For example, 0.1means that 10% of the labels are flipped. (Color online)
Answer: Experiment 1: ACC around 0.5 with 50% noise rate in worst case - clearly higher than baselines for all noise rates
Experiment 2: ACC on real noisy datasets: 0.7 on Movie, 0.79 on Laptop, 0.86 on Restaurant (clearly higher than baselines in almost all cases)
Question: Identify which instrument is string or percussion: Xylophone, Ramkie
Answer: Ramkie is string, Xylophone is percussion.
Question: Tell me which of these terms are related to artificial intelligence versus gardening: tilling, seed, gradient descent, production, Bayesian optimization, genetically modified organism, heirloom, transfer learning
Answer: These terms are all unmistakably related to artificial intelligence:
- Gradient descent: a technique used when training a machine learning model to improve results over time.
- Bayesian optimization: a probabilistic approach to arrive at the best set of parameters, most often used in training machine learning models. 
- Transfer learning: a technique to re-train an already trained deep learning model on new data, most commonly used to customize models to a particular use-case, such as detecting a specific object in photos. 

While these terms are all unmistakably related to gardening:
- Tilling: mechanical preparation of soil for planting.
- Genetically modified organism: this is a term to describe plants that have undergone genetic modification to optimize for specific traits, such as drought resistance.
- Heirloom: this describe certain non-hybrid plant types that must be open pollinated, namely by insects, birds, wind, or other natural factors. 

These terms relate to both artificial intelligence and gardening:
- Seed: in artificial intelligence, a "seed" is a number chosen to help reproduce random outcomes, such as picking random numbers. In gardening, a seed is the raw input that a gardener plants that will eventually become a plant. 
- Production: in artificial intelligence, production is often the term used to describe a lifecycle stage of a model that is in real-life use, rather than testing or development. In gardening, production is another term used to describe the total output of a harvest.
Question: Extract the ingredients in bee pollen from the text.
Evidence: Bee pollen, also known as bee bread and ambrosia, is a ball or pellet of field-gathered flower pollen packed by worker honeybees, and used as the primary food source for the hive. It consists of simple sugars, protein, minerals and vitamins, fatty acids, and a small percentage of other components. Bee pollen is stored in brood cells, mixed with saliva, and sealed with a drop of honey. Bee pollen is harvested as food for humans and marketed as having various, but yet unproven, health benefits.
Answer: Bee pollen consists of simple sugars, protein, minerals, vitamins, fatty acids, and some other components.
Question: Which is a species of fish? Cisco or Palo Alto
Answer: Cisco
Question: From the following list, categorize the what country these dishes originate from: Tamales, Butter Chicken, Mac and Cheese, Pizza, Pozole, Nicoise Salad, Ramen, Trifle, Empanadas, Ceviche, Feijoda, Burrito, Jollaf Rice, Tagine, Couscous
Answer: Mexico, India, United States, Italy, Mexico, France, Japan, England, Spain, Peru, Brazil, United States, Nigeria, Morocco, Algeria
Question: How can I make my bike more aerodynamic?
Answer: Assuming you are keeping your existing bike frame, the top two ways to decrease aerodynamic drag are to ensure you are comfortable riding in an aerodynamic position, and upgrading your wheel set and using road tires. Improving body positioning will have the most benefit at the lowest cost. Because wheel spokes are continuously cutting through air as you ride, they induce drag. Spokes on wheels with less aerodynamic drag allow for the wheels to roll with less resistance. Aerodynamic wheels are generally lighter which also decreases rolling drag.
Question: Who are the last three french presidents ?
Answer: Nicolas Sarkozy was the president from May 2007 to May 2012 then Francois Hollande from May 2012 to May 2017 and finally Emmanuel Macron from May 2017 to now
Question: My manager is available on Tuesday, Wednesday, and Thursday after 12 PM PST and her manager is available on Wednesday after 4:30 PM CST and Thursday from 3:30 PM to 4:30 PM CST. Can you suggest a time for me to schedule a meeting with the two of them if I have to sign off at 5 PM EST every day?
Answer: Your manager is available after 3 PM EST but her manager is only available on Wednesday after 5:30 PM EST or Thursday from 4:30 PM to 5:30 PM EST.

The only time that the three of you are able to meet is on Thursday at 4:30 PM EST. If you can be available after 5 PM EST then all three of you are also available to meet on Wednesday at 5:30 PM EST or Thursday at 5 PM EST.