DataHammer/scimrc huggingface dataset:
Question: What are their goals in this study?
Evidence: Furthermore, this dataset was encoded with 7 feature groups with associated feature values binarized (i.e., present=1 or absent=0) to represent potentially informative features for classifying depression-related classes. We describe the feature groups by type, subtype, and provide one or more examples of words representing the feature subtype from a tweet:
lexical features, unigrams, e.g., “depressed”;
syntactic features, parts of speech, e.g., “cried” encoded as V for verb;
emotion features, emoticons, e.g., :( encoded as SAD;
demographic features, age and gender e.g., “this semester” encoded as an indicator of 19-22 years of age and “my girlfriend” encoded as an indicator of male gender, respectively;
sentiment features, polarity and subjectivity terms with strengths, e.g., “terrible” encoded as strongly negative and strongly subjective;
personality traits, neuroticism e.g., “pissed off” implies neuroticism;
LIWC Features, indicators of an individual's thoughts, feelings, personality, and motivations, e.g., “feeling” suggestions perception, feeling, insight, and cognitive mechanisms experienced by the Twitter user.
A more detailed description of leveraged features and their values, including LIWC categories, can be found in BIBREF10 .
Based on our prior initial experiments using these feature groups BIBREF10 , we learned that support vector machines perform with the highest F1-score compared to other supervised approaches. For this study, we aim to build upon this work by conducting two experiments: 1) to assess the contribution of each feature group and 2) to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy.
For this study, we aim to build upon this work by conducting two experiments: 1) to assess the contribution of each feature group and 2) to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy.
Answer: 1) to assess the contribution of each feature group and 2) to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy.
Question: Why do people like golf?
Answer: Golf is an enjoyable and social sport that one can play for their entire life. People like it because it gives them a chance to be outdoors and enjoy the fresh air. Some golf courses also provide picturesque views of the ocean or the mountains, depending upon their location. This is why one sees lots of golf vacations. Golf can be technically challenging and requires a mix of both physical and mental skills to play the game, which is what makes it an exciting game to enjoy with friends, family and competitors.
Question: Given this paragraph, extract a list of names that are playable by the user of this game.
Evidence: During the single-player campaign, the player controls five different characters from a first-person perspective. The player primarily controls Sergeant Gary "Roach" Sanderson, a British member of an international special forces unit named Task Force 141. In addition to Roach, the player will also assume control of Private First Class Joseph Allen (Troy Baker) and Private James Ramirez of the U.S. Army's 1st Battalion/75th Ranger Regiment. The now Captain John "Soap" MacTavish (Kevin McKidd) serves as a senior member of Task Force 141, acting as Roach's superior officer and becomes the playable character in the final three missions of the game. Lastly, the player briefly assumes the role of an astronaut stationed on the International Space Station during the height of the war between the United States and Russia.

The game's non-playable characters (NPCs) feature prominently in the story: For Task Force 141, Lieutenant Simon "Ghost" Riley (Craig Fairbrass), who conceals his face with a skull print balaclava serves as Soap's second in command. For the United States Army Rangers, Sergeant Foley (Keith David), along with his subordinate, Corporal Dunn (Barry Pepper), is initially Joseph Allen's squad leader, and becomes James Ramirez's squad leader later in the story. Lieutenant General Shepherd (Lance Henriksen) is the commander of the United States Army Rangers and Task Force 141. Other supporting characters returning from Call of Duty 4 include Captain John Price (Billy Murray), MacTavish's former commanding officer in the British Special Air Service who was imprisoned in Russia, and Russian informant turned mercenary pilot "Nikolai" (Sven Holmberg).
Answer: 1. Sergeant Gary "Roach" Sanderson
2. Joseph Allen
3. James Ramirez
4. John "Soap" MacTavish
5. An unnamed astronaut
Question: Does my data need to fit in memory to use Spark?
Answer: No. Spark's operators spill data to disk if it does not fit in memory, allowing it to run well on any sized data. Likewise, cached datasets that do not fit in memory are either spilled to disk or recomputed on the fly when needed, as determined by the RDD's storage level.
Question: Is Dosa a South Indian Food?
Answer: A dose, also called dosai, dosey, dwashi, dosa or dosha is a thin pancake in South Indian cuisine made from a fermented batter of ground black lentils and rice.

Dosas originated in South India, but its precise geographical origins are unknown. According to historian P. Thankappan Nair, dosa originated in the town of Udupi in present-day Karnataka. However, according to food historian K. T. Achaya, references in the Sangam literature suggest that dosa was already in use in the ancient Tamil country around the 1st century CE. Achaya states that the earliest written mention of dosa appears in literature of present-day Tamil Nadu, in the 8th century, while the earliest mention of dosa in the Kannada literature appears a century later.

In popular tradition, the origin of the dosa is linked to Udupi, probably because of the dish's association with Udupi restaurants. The Tamil dosai is softer and thicker. The thinner and crispier version of dosa was first made in present-day Karnataka. A recipe for dosa can be found in Manasollasa, a 12th-century Sanskrit encyclopedia compiled by Someshvara III, who ruled from present-day Karnataka.

After the Independence of India, South Indian cuisine became gradually popular in the North. In Delhi, the Madras Hotel in Connaught Place became one of the first restaurants to serve South Indian cuisine. It arrived in Mumbai with the Udupi restaurants in the 1930s.
Question: What are the top 4 healthier sodas that I should buy?
Answer: If you're looking for a healthier alternative to traditional soda, try the following:
1. La Croix
2. Zevia Soda
3. Olipop
4. Poppi
Question: Do they evaluate only on English data?
Evidence: This phase collected tweets using Twitter's Application Programming Interfaces (API) BIBREF43 . Within the Twitter API, diabetes, diet, exercise, and obesity were selected as the related words BIBREF4 and the related health areas BIBREF19 . Twitter's APIs provides both historic and real-time data collections. The latter method randomly collects 1% of publicly available tweets. This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame. We used the queries to collect approximately 4.5 million related tweets between 06/01/2016 and 06/30/2016. The data will be available in the first author's website. Figure FIGREF3 shows a sample of collected tweets in this research.
This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame. 
Answer: Yes.
Question: Give me a list of actresses who won an Oscar in the last 5 years and tell me for which movie they won it.
Answer: 1. Michelle Yeoh 2022, Everything Everywhere All At Once
2. Jessica Chastain, 2021, The Eyes of Tammy Faye
3. Frances McDormand, 2020, Nomadland
4. Renée Zellweger, 2019, Judy
5. Olivia Colman, 2018, The Favourite
Question: How do I search for a document that I created in Google Drive? I think the document starts with the title Finance Forecast
Answer: To search for documents that you own in Google Drive, you can use the `creator:me` operator in your search term. So to search for documents you own that have the words "finance" and "forecast" in them you can use this search string in the search field

`creator:me finance forecast`

If your document is in a Shared Drive that you no longer have access to, that document will not appear in your list of results
Question: What previous methods is the proposed method compared against?
Evidence: FLOAT SELECTED: Table 4: Comparison results with the previous approaches and our approaches on SwDA dataset.
FLOAT SELECTED: Table 5: Experiment results about the hyperparameter W and P on SwDA dataset and online prediction result. W,P indicate the size of sliding window and context padding length during training and testing.
FLOAT SELECTED: Table 4: Comparison results with the previous approaches and our approaches on SwDA dataset.
FLOAT SELECTED: Table 5: Experiment results about the hyperparameter W and P on SwDA dataset and online prediction result. W,P indicate the size of sliding window and context padding length during training and testing.
Answer: BLSTM+Attention+BLSTM
Hierarchical BLSTM-CRF
CRF-ASN
Hierarchical CNN (window 4)
mLSTM-RNN
DRLM-Conditional
LSTM-Softmax
RCNN
CNN
CRF
LSTM
BERT
Question: What is lucid dream and how can someone learn to do it?
Answer: Lucid dreaming is the ability to be consciously awake in your dreams. This allows you to make active decisions in your dreams and even shape the dream world. It is very useful for not just entertainment (e.g. who doesn't like to fly like a bird?) but also for self-help (e.g. practice facing your fears like public speaking) and discovery. To best way to get better at lucid dreaming is to keep a dream journal (writing down your dreams very morning). I also suggest you buy the original book "Lucid Dreaming" by Stephen LaBerge.
Question: Whats the difference between a CPU and RAM?
Answer: A CPU is a Centralized Processing Unit that processes data, RAM is Random Access Memory which is used to store temporary data for fast access by the processor.  The processor processes the data that is stored in the memory.
Question: How many grand slams did Boris Becker win?
Answer: Boris Becker won a total of six Grand Slam titles during his career. He won Wimbledon in 1985, 1986 and 1989, the US Open in 1989 and the Australian Open in 1991 and 1996.
Question: I'm interested in the interplay between the Q and R terms in Eq. 6. How important is it to have the R to penalize the magnitude of actions? When does it help and hurt? My guess is R might perform some kind of regularization that allows the model to perform better in extrapolation. Can you add a comment describing what you've found.
Evidence: We investigate the effect of penalizing the magnitude of the action ak in the reward function (7). To this effect, we repeat the experiments of Sections 5.1 and 5.2, this time training the RL-ROE using different magnitudes R for the matrix R = RI. When evaluating the performance of the trained RL-ROE, we consider different amounts of Gaussian observation noise added to the measurements yk. The results for the unforced case are shown in Figures 10, 11, and 12 for observation noise of standard deviation σ = 0, 0.1, and 0.3, respectively. The results for the forced case are shown in Figures 13, 14, and 15 for observation noise of standard deviation σ = 0, 0.1, and 0.3, respectively. In absence of noise, the highest estimation accuracy is obtained for R = 0, and decreases as R increases. However, in the presence of noise, Figures 11, 12, and 14, 15, the estimation accuracy is generally highest for R = 10. This confirms that penalizing the magnitude of the action ak in the reward function acts as a regularization that allows the RL-ROE to perform better on noisy measurement data.
Answer: This is an excellent point raised by the reviewer; we also think that the R term might act as a regularization that allows the estimator to perform better when faced with trajectories it has not previously seen or in the presence of noise. We tried training our estimator for different magnitudes of R and different amounts of additive observation noise. Without noise (similar to the examples shown in the paper), the estimation accuracy decreases as R increases. In the presence of noise, the estimator is indeed more accurate for larger magnitudes of R. We have added an appendix describing these findings in the revised paper. The effect of R on the generalization performance of the estimator is a very interesting question that we leave for future work.
Question: How can I decide what bike I should get?
Answer: When it comes to bicycles for adults, there are different kinds available in the market. Select a bike based on how you'd like to use it. Bikes can be categorized as follows:
1. Road bikes - These bikes have super thin tires and are often used for biking on roads. These help you attain high speeds relatively quickly.
2. Mountain bikes - These bikes are made to sustain the rough terrain that you could come across while biking. The tires are much thicker made to protect you and give you the support you need.
3. Hybrid/Commuter bikes - These bikes fall into the middle of the above 2 categories. If you think you might do a bit of road biking and mountain biking, then this might be your best option.
You can also go to a store that specializes in selling and repairing bikes to get more information.
Question: What is meant by "dual federalism," and how is it different from "cooperative federalism"?
Answer: Federalism is a system of government where the power is distributed across different levels. In the United States, we have a system with two levels, a federal or national government, and underneath that state governments.  Dual federalism is a period between 1819 and 1936 where there was a clear separation of powers between the federal government and the state governments.  During this time the “national government left the states largely to their own devices in major policy areas” which included “education, transportation, and civil rights, among others”.  
During the dual federalism period, two important cases set the stage for the next time period called “cooperative federalism” (1936-present).  The first case was McCulloch v. Maryland which cited the Constitution's  supremacy clause and implied powers of the federal government in a verdict that prohibited the state of Maryland from taxing a federal bank.  The second case was Gibbons v. Ogden which cited the power of the federal government to regulate interstate commerce giving Gibbons a monopoly to ferry people from New Jersey to New York.  These two cases were used by the Federal government during the New Deal as precedent to enable cooperative federalism which is “an intermingling of power, policy, and resources among the national and state governments”. The “government established programs providing federal funding in areas that were traditionally the exclusive domains of the state, such as health, transportation, education, employment, and poverty relief”.  This differs from dual federalism in that we now have a mixing and sharing of powers between the state and national governments.
Question: What does GPT stand for in ChatGPT?
Answer: GPT stands for generative pre-trained transformer which is a type of deep neural network model for natural language processing, i.e. large language model (LLM).
Question: How is fluency automatically evaluated?
Evidence: Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation ::: Language Fluency
We fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs.
Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation ::: Language Fluency
We fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs.
Answer: The answers are shown as follows:
* fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs
Question: How does the model perform with a weighted rewards of DAE, NER-P, NER-R and similar RL based training?
Evidence: The Joint model that uses average of DAE, NER-P and NER-R rewards and trains on data filtered according to all three metrics, perform slightly better than MoFEW on DAE-A/S and NER-PS metric. However, it obtains 1.42 points lower entity recall as well as performs consistently worse than the DAE expert across all metrics. Notably, MoFEW has the flexibility to include multiple experts and adjust for degradation in performance on any metric by including an appropriate expert during the decoding time, as discussed in the next section. Therefore, joint model can also be used as a new expert in MoFE and resulting degradation in NER recall can be adjusted by the NER-R expert.
Answer: A model trained with joint reward (average of DAE/ NER-P/ NER-R) performs comparable to MoFE_weight on DAE-A/S and NER-PS metrics, and obtains ~1.42 points lower NER-recall. Also, joint model performs consistently worse than DAE expert. Note that we haven't tuned reward-weights for joint training. This is particularly the advantage of using modular MoFE, we can adjust for different experts to obtain the desired behavior unlike joint training where we need to retrain model for any adjustment.
Question: What are the names of the first three songs from Edmond Leung's The Story of June?
Evidence: The Story of June is a Cantopop album by Edmond Leung.

Track listing:
Love Blind (戀愛盲)
Elevator Men (電梯男)
Hey June
Driven To Distraction (失魂落魄)
Daydream (白日夢)
Romantic (羅曼蒂克)
Y3
Get Away with Miriam Yeung (滾)
The Aftermath (戰後餘生)
The Long Journey (路漫漫)
Answer: Love Blind (戀愛盲)
Elevator Men (電梯男)
Hey June
Question: What types are most adversarial attack methods for text inputs derived into?
Evidence: Most existing adversarial attack methods for text inputs are derived from those for image inputs. These methods can be categorised into three types including gradient-based attacks, optimisation-based attacks and model-based attacks.
Gradient-based attacks are mainly white-box attacks that rely on calculating the gradients of the target classifier with respect to the input representation. This class of attacking methods BIBREF6, BIBREF7, BIBREF6 are mainly derived from the fast gradient sign method (FGSM) BIBREF1, and it has been shown to be effective in attacking CV classifiers. However, these gradient-based methods could not be applied to text directly because perturbed word embeddings do not necessarily map to valid words. Other methods such as DeepFool BIBREF8 that rely on perturbing the word embedding space face similar roadblocks. BIBREF5 propose to use nearest neighbour search to find the closest word to the perturbed embedding.
Both optimisation-based and model-based attacks treat adversarial attack as an optimisation problem where the constraints are to maximise the loss of target classifiers and to minimise the difference between original and adversarial examples. Between these two, the former uses optimisation algorithms directly; while the latter trains a seperate model to generate adversarial examples and therefore involves a training process. Some of the most effective attacks for images are achieved by optimisation-based methods, such as the L-BFGS attack BIBREF1 and the C&W attack BIBREF9 in white-box attacks and the ZOO method BIBREF10 in black-box attacks. For texts, the white-box attack HotFlip BIBREF3 and black-box attack DeepWordBug BIBREF11 and TextBugger BIBREF12 are proposed in this category.
In a similar vein, a few model-based attacks have been proposed for images, e.g. BIBREF13 design a generative adversarial network (GAN) to generate the image perturbation from a noise map. The attacking method and target classifier typically form a single large network and the attacking method is trained using the loss from the target classifier. For this reason, it is not very straightforward to use these model-based techniques for text because there is a discontinuity in the network (since words in the adversarial examples are discrete) and so it is not fully differentiable.
Most adversarial attack methods for text inputs are derived from methods originally designed for image inputs. These methods can be categorised into three types: gradient-based attacks, optimisationbased attacks and model-based attacks.
Answer: Gradient-based attacks, optimisationbased attacks and model-based attacks.
Question: Classify each of the following as either a brass instrument or a woodwind instrument: flute, euphonium, bassoon, trumpet, clarinet, saxophone, tuba.
Answer: Flute: woodwind
Euphonium: brass
Bassoon: woodwind
Trumpet: brass
Clarinet: woodwind
Saxophone: woodwind
Tuba: brass
Question: Extract the names of the the Handler family from these passages
Evidence: Ruth Handler watched her daughter Barbara play with paper dolls, and noticed that she often enjoyed giving them adult roles. At the time, most children's toy dolls were representations of infants. Realizing that there could be a gap in the market, Handler suggested the idea of an adult-bodied doll to her husband Elliot, a co-founder of the Mattel toy company. He was unenthusiastic about the idea, as were Mattel's directors.

During a trip to Europe in 1956 with her children Barbara and Kenneth, Ruth Handler came across a German toy doll called Bild Lilli. The adult-figured doll was exactly what Handler had in mind, so she purchased three of them. She gave one to her daughter and took the others back to Mattel. The Lilli doll was based on a popular character appearing in a comic strip drawn by Reinhard Beuthin for the newspaper Bild. Lilli was a blonde bombshell, a working girl who knew what she wanted and was not above using men to get it. The Lilli doll was first sold in Germany in 1955, and although it was initially sold to adults, it became popular with children who enjoyed dressing her up in outfits that were available separately.
Answer: Ruth Handler, Barbara Handler, Elliot Handler, Kenneth Handler
Question: Classify each of the following as either a fruit or a vegetables: tomato, apple, orange, potato, banana.
Answer: Tomato is a fruit. Apple is a fruit. Orange is a fruit. Potato is a vegetable. Banana is a fruit.
Question: Identify which instrument is string or percussion: Carillon, Rebab
Answer: Rebab is string, Carillon is percussion.
Question: Where did peanut butter and jelly sandwiches originate?
Evidence: The first known reference for a peanut butter and jelly sandwich appeared in the Boston Cooking School Magazine in 1901; it called for "three very thin layers of bread and two of filling, one of peanut paste, whatever brand you prefer, and currant or crabapple jelly for the other", and called it as "so far as I know original".
Answer: In 1901, the Boston Cooking School Magazine published the first known reference for a peanut butter and jelly sandwich. This magazine article called for "three very thin layers of bread and two of filling, one of peanut paste, whatever brand you prefer, and currant or crabapple jelly for the other."
Question: What flower does the Netherlands produce most?
Answer: The Netherlands produces the most tulips, followed by daffodils and hyacinths.
Question: Which NBA player has the most championships in NBA history?
Evidence: William Felton "Bill" Russell (February 12, 1934 – July 31, 2022) was an American professional basketball player who played as a center for the Boston Celtics of the National Basketball Association (NBA) from 1956 to 1969. A five-time NBA Most Valuable Player (MVP) and a 12-time NBA All-Star, he was the centerpiece of the Celtics dynasty that won 11 NBA championships during his 13-year career.
Answer: William Felton "Bill" Russell won 11 NBA championships during his 13-year career with the Boston Celtics.