DataHammer/scimrc huggingface dataset:
Question: Which dataset do they experiment with?
Evidence: From a character-level view, natural language is a discrete sequence of data, where discrete symbols form a distinct and shallow tree structure: the sentence is the root, words are children of the root, and characters are leafs. However, compared to word-level language modeling, character-level language modeling requires the model to handle longer-term dependencies. We evaluate a character-level variant of our proposed language model over a preprocessed version of the Penn Treebank (PTB) and Text8 datasets.
The unsupervised constituency parsing task compares hte tree structure inferred by the model with those annotated by human experts. The experiment is performed on WSJ10 dataset. WSJ10 is the 7422 sentences in the Penn Treebank Wall Street Journal section which contained 10 words or less after the removal of punctuation and null elements. Evaluation was done by seeing whether proposed constituent spans are also in the Treebank parse, measuring unlabeled F1 ( INLINEFORM0 ) of unlabeled constituent precision and recall. Constituents which could not be gotten wrong (those of span one and those spanning entire sentences) were discarded. Given the mechanism discussed in Section SECREF14 , our model generates a binary tree. Although standard constituency parsing tree is not limited to binary tree. Previous unsupervised constituency parsing model also generate binary trees BIBREF11 , BIBREF13 . Our model is compared with the several baseline methods, that are explained in Appendix .
We evaluate a character-level variant of our proposed language model over a preprocessed version of the Penn Treebank (PTB) and Text8 datasets.
The unsupervised constituency parsing task compares hte tree structure inferred by the model with those annotated by human experts. The experiment is performed on WSJ10 dataset.
Answer: The answers are shown as follows:
* Penn Treebank
* Text8
* WSJ10
Question: What are some good costumes I can wear for Halloween? Give me at least five in a bullet list
Answer: - A pumpkin
- A witch
- A lifeguard
- A fairy
- Harry Potter
Question: The girl was wearing a red dress whilst her friend was wearing a white dress. What colour dress was the girl wearing?
Answer: The girl was wearing a red dress.
Question: Provide a list of superpower you wish you had
Answer: 1. Fly
2. Hold breath forever
3. Read minds
4. Infinite strength
5. Time Travel
6. Invisibility 
7. Future Telling
Question: In terms of which metric their method achieves the state-of-the-art performance?
Evidence: We perform experiments on RumourEval and PHEME datasets to evaluate the performance of our method and the baselines. The experimental results are shown in Table TABREF27. We gain the following observations:
On the whole, most well-designed deep learning methods, such as ours, Bayesian-DL, and TRNN, outperform feature engineering-based methods, like SVM. This illustrates that deep learning methods can represent better intrinsic semantics of claims and replies.
In terms of recall (R), our method and MTL-LSTM, both based on multi-task learning, achieve more competitive performances than other baselines, which presents that sufficient features are shared for each other among multiple tasks. Furthermore, our method reflects a more noticeable performance boost than MTL-LSTM on both datasets, which extrapolates that our method earns more valuable shared features.
Although our method shows relatively low performance in terms of precision (P) and recall (R) compared with some specific models, our method achieves the state-of-the-art performance in terms of accuracy (A) and F1-score (F1) on both datasets. Taking into account the tradeoff among different performance measures, this reveals the effectiveness of our method in the task of fake news detection.
Although our method shows relatively low performance in terms of precision (P) and recall (R) compared with some specific models, our method achieves the state-of-the-art performance in terms of accuracy (A) and F1-score (F1) on both datasets.
Answer: Accuracy (A) and F1-score (F1).
Question: Which movie was originally declared the winner for Best Picture?
Evidence: The winners were announced during the awards ceremony on February 26, 2017. Moonlight became the first film with an all-black cast and the first LGBT-themed film to win Best Picture. In an event unprecedented in the history of the Oscars, La La Land was incorrectly announced as the Best Picture, and, a few minutes later, the error was corrected and Moonlight was declared the winner. O.J.: Made in America, at 467 minutes, became the longest film to win an Academy Award, surpassing the 431-minute long War and Peace, which won the Academy Award for Best Foreign Language Film in 1969. Following the five-part documentary's win, new academy rules barred any "multi-part or limited series" from being eligible for documentary categories. With Casey Affleck winning the Oscar for Best Actor, he and his older brother, Ben Affleck, became the 16th pair of siblings to win Academy Awards. Mahershala Ali became the first Muslim actor to win an Oscar. Viola Davis became the first black person to achieve the Triple Crown of Acting with her Oscar, Emmy, and Tony wins.
Answer: La La Land was originally declared the winner for Best Picture
Question: What were the best TV shows of 2022?
Answer: The best television shows from 2022 (with the network in parentheses) were:
1. The Bear (FX)
2. Reservation Dogs (Hulu)
3. Barry (HBO)
4. Better Call Saul (AMC/Netflix)
5. White Lotus (HBO)
6. Severance (HBO)
7. Atlanta (FX)
8. Andor (Disney)
9. For All Mankind (HBO)
10. Euphoria (HBO)
Question: What is rain good for?
Answer: Rain comes from the sky by ocean and land water evaporating into the clouds.  When these nimbus clouds move to land, and the the rain in them is gets too heavy the rain will fall to the ground.  Rain is needed for humans, animals, and plants.  Purified rain water helps humans to stay hydrated, animals to grow, and plants to grow to make food for humans and animals.  Rain also helps with cleaning out pollution from the air because pollution particles stick to the rain and then it falls to the ground.
Question: What is DROP-score?
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
DROP-score: a new metric that accounts for possibly dropped words.
Answer: It is a new metric that accounts for possibly dropped words.
Question: In your mind, what is most concerning about the state of American politics today?
Answer: I think the most distressing aspect of American politics today is the lack of civil discourse on any issue. Pick an issue, and Americans try to distill often complex topics to a single tweet of 140 characters. The discourse lacks depth and educated discussion. Furthermore, politicians try to rally their base with increasingly polarized takes, and each party shifts further and further towards to their respective extremes. Abandoned in the middle, are the nation's centrists who care more for results and general prosperity than token, empty political victories.
Question: What happens when someone throws a phone onto a mattress?
Answer: The phone hits the mattress and causes a momentary indent on the mattress. Depending on which part of the phone hits the mattress and the angle as well as speed of the throw, the phone may jump to another spot on the mattress, or it may stay where it first made contact. Regardless, it will likely bounce a few times and then eventually stop moving.
Question: Can you classify what each NFL team is named after?
Answer: Arizona Cardinals - Animal
Atlanta Falcons - Animal
Baltimore Ravens - Animal
Buffalo Bills - Named after Buffalo Bill Cody
Carolina Panthers - Animal
Chicago Bears - Animal
Cincinnati Bengals - Animal
Cleveland Browns - Named after Head Coach Paul Brown
Dallas Cowboys - Named after men who tend to cattle
Denver Broncos - Animal
Detroit Lions - Animal
Green Bay Packers - Named after an early sponsor of the team, Indian Packing Company
Houston Texans - Named after people who live in Texas
Indianapolis Colts - Animal
Jacksonville Jaguars - Animal
Kansas City Chiefs - Named after former mayor of Kansas City
Las Vegas Raiders - Named after a person who attacks their enemy
Los Angeles Chargers - Named after a person that moves forward with vigor and force
Los Angeles Rams - Animal
Miami Dolphins - Animal
Minnesota Vikings - Named after a group of people originating in Scandinavia
New England Patriots - Named after a person who supports their country
New Orleans Saints - Named after a person that religions recognize as holy and virtuous
New York Giants - Named after of person of exceptional size
New York Jets - Named after a jet airplane
Philadelphia Eagles - Animal
Pittsburgh Steelers - Named after a person who works in a steel factory
San Francisco 49ers - Named after a person who participated in the Gold Rush
Seattle Seahawks - Animal
Tampa Bay Buccaneers - Named after a pirate
Tennessee Titans - Named after a person who is very strong
Washington Commanders - Named after a person in the military
Question: What should I do this spring in San Francisco?
Answer: During the spring time, people in San Francisco enjoy visiting the parks for the cherry blossom, check out the open markets when the weather is nice, and explore the Golden Gate Park. If you want to venture farther, you can enjoy the wildflower festival in Half Moon Bay, take the ferry to Sausalito, or even spot Humpback whales in the Bay Area!
Question: How do they incorporate sentiment analysis?
Evidence: These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient’s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain.
These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. In our paper we also showed that this automatic pipeline achieves reasonably strong F-scores, with an overall performance of 0.828 F1 for the topic extraction component and 0.5 F1 on the clinical sentiment component.
45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. These features can be grouped into three categories (See Table TABREF5 for complete list of features):
These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient’s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain.

These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text.
45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier.
Answer: The answers are shown as follows:
* features per admission were extracted as inputs to the readmission risk classifier
Question: How to sell?
Answer: 1. Create a need
2. Offer a product or service to fill the need
3. Rewards and consequences
4. Create urgency and deadlines to act
Question: Which of the following grow on trees?: apples, lemons, potatoes, carrots
Answer: Apples and lemons grow on trees
Question: Name some popular breakfast foods in the United States
Answer: In the United States of America popular breakfasts include bacon and eggs, waffles, French toast, cereal and pancakes
Question: What is used to apply Vietnamese properly to Image Captioning?
Evidence: After finishing constructing UIT-ViIC dataset, we have a look in statistical analysis on our corpus in this section. UIT-ViIC covers 3,850 images described by 19,250 Vietnamese captions. Sticking strictly to our annotation guidelines, the majority of our captions are at the length of 10-15 tokens. We are using the term “tokens” here as a Vietnamese word can consist of one, two or even three tokens. Therefore, to apply Vietnamese properly to Image Captioning, we present a tokenization tool - PyVI BIBREF17, which is specialized for Vietnamese language tokenization, at words level. The sentence length using token-level tokenizer and word-level tokenizer are compared and illustrated in Fig. FIGREF23, we can see there are variances there. So that, we can suggest that the tokenizer performs well enough, and we can expect our Image Captioning models to perform better with Vietnamese sentences that are tokenized, as most models perform more efficiently with captions having fewer words.
Table TABREF24 summarizes top three most occuring words for each part-of-speech. Our dataset vocabulary size is 1,472 word classes, including 723 nouns, 567 verbs, and 182 adjectives. It is no surprise that as our dataset is about sports with balls, the noun “bóng” (meaning “ball") occurs most, followed by “sân” and "cầu thủ" (“pitch” and “athlete” respectively). We also found that the frequency of word “tennis” stands out among other adjectives, which specifies that the set covers the majority of tennis sport, followed by “bóng chày” (meaning “baseball”). Therefore, we expect our model to generate the best results for tennis images.
Therefore, to apply Vietnamese properly to Image Captioning, we present a tokenization tool - PyVI , which is specialized for Vietnamese language tokenization, at words level.
Answer: They  present a tokenization tool - PyVI.
Question: Determine which companies are primarily cloud-software companies and which are semiconductor companies: Intel, Google, Workday, Zscaler, AMD, NVIDIA, and Texas Instruments.
Answer: Intel, AMD, NVIDIA, and Texas Instruments are primarily semiconductor companies. Workday and Zscaler are fully cloud-software companies. Google is primarily a software company, however, they also develop custom-developed application-specific integrated circuits which are semiconductors.
Question: Are they the first one tosystematically explore the cross-lingual transferring ability of multi-BERT on RC tasks?
Evidence: The new generation of Neural Machine Translation (NMT) systems is known to be extremely data hungry BIBREF0 . Yet, most existing NMT training pipelines fail to fully take advantage of the very large volume of monolingual source and/or parallel data that is often available. Making a better use of data is particularly critical in domain adaptation scenarios, where parallel adaptation data is usually assumed to be small in comparison to out-of-domain parallel data, or to in-domain monolingual texts. This situation sharply contrasts with the previous generation of statistical MT engines BIBREF1 , which could seamlessly integrate very large amounts of non-parallel documents, usually with a large positive effect on translation quality.
Such observations have been made repeatedly and have led to many innovative techniques to integrate monolingual data in NMT, that we review shortly. The most successful approach to date is the proposal of BIBREF2 , who use monolingual target texts to generate artificial parallel data via backward translation (BT). This technique has since proven effective in many subsequent studies. It is however very computationally costly, typically requiring to translate large sets of data. Determining the “right” amount (and quality) of BT data is another open issue, but we observe that experiments reported in the literature only use a subset of the available monolingual resources. This suggests that standard recipes for BT might be sub-optimal.
This paper aims to better understand the strengths and weaknesses of BT and to design more principled techniques to improve its effects. More specifically, we seek to answer the following questions: since there are many ways to generate pseudo parallel corpora, how important is the quality of this data for MT performance? Which properties of back-translated sentences actually matter for MT quality? Does BT act as some kind of regularizer BIBREF3 ? Can BT be efficiently simulated? Does BT data play the same role as a target-side language modeling, or are they complementary? BT is often used for domain adaptation: can the effect of having more in-domain data be sorted out from the mere increase of training material BIBREF2 ? For studies related to the impact of varying the size of BT data, we refer the readers to the recent work of BIBREF4 .
To answer these questions, we have reimplemented several strategies to use monolingual data in NMT and have run experiments on two language pairs in a very controlled setting (see § SECREF2 ). Our main results (see § SECREF4 and § SECREF5 ) suggest promising directions for efficient domain adaptation with cheaper techniques than conventional BT.
To our knowledge, this is the first work systematically exploring the cross-lingual transferring ability of multi-BERT on RC tasks.
Answer: Yes.
Question: What architectures are explored to improve the seq2seq model?
Evidence: Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. Except Pashto, Georgian and Tokpisin, the multilingual VGG-BLSTM model gave 8.8 % absolute gain in average over monolingual model. In case of multilingual BLSTMP, except Pashto and Georgian an absolute gain of 5.0 % in average is observed over monolingual model. Even though the VGG-BLSTM gave improvements, we were not able to perform stage-1 and stage-2 retraining with it due to time constraints. Thus, we proceed further with multilingual BLSTMP model for retraining experiments tabulated below.
We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences. We use all available paired text in the corresponding target language to train the LM for the language. No external text data were used. All language models are trained separately from the seq2seq models. When building dictionary, we combined all the characters over all 15 languages mentioned in table TABREF14 to make them work with transferred models. Regardless of the amount of data used for transfer learning, the RNNLM provides consistent gains across all languages over different data sizes.
Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance.
We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences.
Answer: The answers are shown as follows:
* VGG-BLSTM
* character-level RNNLM
Question: What are 5 potential uses for an empty La Croix can?
Answer: 1. Make your bike sound like it has an engine by wrapping the can around the back tire.
2. A percussion instrument to play along with friends in a jam session.  You could just crinkle it, hit it with a pen, or fill it with sand or debris and shake it.  
3. A baking tool.  You can used the bottom of the can to stamp out perfectly circle, uniform cookies from a rolled-out sheet of cookie dough.
4. A field razor.  Tear the can carefully to create an effective cutting device for emergency use.
5. A make shift ball to throw around with friends at the park. An empty can could be a fun projectile to use in a friendly game of 3 Flies Up.
Question: How long is a professional football field.
Answer: A professional football field is 100 yards long.
Question: Extract the names of the noble award winners, respective category and year in which they got  from given text and list them using bullets in the format {Name} - {Category} - {Year}
Evidence: Five people have received two Nobel Prizes. Marie Curie received the Physics Prize in 1903 for her work on radioactivity and the Chemistry Prize in 1911 for the isolation of pure radium, making her the only person to be awarded a Nobel Prize in two different sciences. Linus Pauling was awarded the 1954 Chemistry Prize for his research into the chemical bond and its application to the structure of complex substances. Pauling was also awarded the Peace Prize in 1962 for his activism against nuclear weapons, making him the only laureate of two unshared prizes. John Bardeen received the Physics Prize twice: in 1956 for the invention of the transistor and in 1972 for the theory of superconductivity. Frederick Sanger received the prize twice in Chemistry: in 1958 for determining the structure of the insulin molecule and in 1980 for inventing a method of determining base sequences in DNA. Karl Barry Sharpless was awarded the 2001 Chemistry Prize for his research into chirally catalysed oxidation reactions, and the 2022 Chemistry Prize for click chemistry.
Answer: •	Marie Curie - Physics - 1903
•	Marie Curie - Chemistry - 1911
•	Linus Pauling - Chemistry - 1954
•	Linus Pauling - Peace - 1962
•	John Bardeen - Physics - 1956
•	John Bardeen - Physics - 1972
•	Frederick Sanger - Chemistry - 1958
•	Frederick Sanger - Chemistry - 1980
•	Karl Barry Sharpless - Chemistry - 2001
•	Karl Barry Sharpless - Chemistry - 2022
Question: Which of these foods are from North and South of India: Chicken Tikka, Chole Samosa, Chole Batura, Idli Sambar, Dosa, Upama, Tamarind Rice
Answer: Chicken Tikka, Chole Samosa, Chole Batura are North Indian Foods. Idli Sambar, Dosa, Upama, Tamarind Rice are from South of India.
Question: Classify each item as being related to "golf" or "football": Iron, driver, quarterback, lineman, putter, tee, linebacker
Answer: Iron: Golf 
Driver: Golf
Quarterback: Football
Lineman: Football
Putter: Golf 
Tee: Golf 
Linebacker: Football