DataHammer/scimrc huggingface dataset:
Question: What are some guidelines in writing input vernacular so model can generate 
Evidence: 1) In classical Chinese poems, poetic images UTF8gbsn(意象) were widely used to express emotions and to build artistic conception. A certain poetic image usually has some fixed implications. For example, autumn is usually used to imply sadness and loneliness. However, with the change of time, poetic images and their implications have also changed. According to our observation, if a vernacular paragraph contains more poetic images used in classical literature, its generated poem usually achieves higher score. As illustrated in Table TABREF12, both paragraph 2 and 3 are generated from pop song lyrics, paragraph 2 uses many poetic images from classical literature (e.g. pear flowers, makeup), while paragraph 3 uses modern poetic images (e.g. sparrows on the utility pole). Obviously, compared with poem 2, sentences in poem 3 seems more confusing, as the poetic images in modern times may not fit well into the language model of classical poems.
2) We also observed that poems generated from descriptive paragraphs achieve higher scores than from logical or philosophical paragraphs. For example, in Table TABREF12, both paragraph 4 (more descriptive) and paragraph 5 (more philosophical) were selected from famous modern prose. However, compared with poem 4, poem 5 seems semantically more confusing. We offer two explanations to the above phenomenon: i. Limited by the 28-character restriction, it is hard for quatrain poems to cover complex logical or philosophical explanation. ii. As vernacular paragraphs are more detailed and lengthy, some information in a vernacular paragraph may be lost when it is summarized into a classical poem. While losing some information may not change the general meaning of a descriptive paragraph, it could make a big difference in a logical or philosophical paragraph.
According to our observation, if a vernacular paragraph contains more poetic images used in classical literature, its generated poem usually achieves higher score.
We also observed that poems generated from descriptive paragraphs achieve higher scores than from logical or philosophical paragraphs.
Answer: The answers are shown as follows:
*  if a vernacular paragraph contains more poetic images used in classical literature, its generated poem usually achieves higher score
* poems generated from descriptive paragraphs achieve higher scores than from logical or philosophical paragraphs
Question: Identify which instrument is string or percussion: Tsymbaly, Yehu
Answer: Yehu is string, Tsymbaly is percussion.
Question: Extract the name and year of release from all albums released by the band Radiohead. List in the format {name} - {year} in a semicolon list.
Evidence: Radiohead are an English rock band formed in Abingdon, Oxfordshire, in 1985. The band consists of Thom Yorke (vocals, guitar, piano, keyboards); brothers Jonny Greenwood (lead guitar, keyboards, other instruments) and Colin Greenwood (bass); Ed O'Brien (guitar, backing vocals); and Philip Selway (drums, percussion). They have worked with the producer Nigel Godrich and the cover artist Stanley Donwood since 1994. Radiohead's experimental approach is credited with advancing the sound of alternative rock.

Radiohead signed to EMI in 1991 and released their debut album, Pablo Honey, in 1993. Their debut single, "Creep", became a worldwide hit. Radiohead's popularity and critical standing rose with the release of The Bends in 1995. Radiohead's third album, OK Computer (1997), brought them international fame; noted for its complex production and themes of modern alienation, it is acclaimed as a landmark record and one of the best albums in popular music.

Radiohead's fourth album, Kid A (2000), marked a dramatic change in style, incorporating influences from electronic music, jazz, classical music and krautrock. Though Kid A divided listeners, it was later named the best album of the decade by multiple outlets. It was followed by Amnesiac (2001), recorded in the same sessions. Hail to the Thief (2003), with lyrics addressing the War on Terror, blended the band's rock and electronic sides, and was Radiohead's final album for EMI.

Radiohead self-released their seventh album, In Rainbows (2007), as a download for which customers could set their own price, to critical and chart success. Their eighth album, The King of Limbs (2011), an exploration of rhythm, was developed using extensive looping and sampling. A Moon Shaped Pool (2016) prominently featured Jonny Greenwood's orchestral arrangements. Yorke, Jonny Greenwood, Selway, and O'Brien have released solo albums; in 2021, Yorke and Jonny Greenwood debuted a new band, the Smile.

By 2011, Radiohead had sold more than 30 million albums worldwide. Their awards include six Grammy Awards and four Ivor Novello Awards, and they hold five Mercury Prize nominations, the most of any act. Seven Radiohead singles have reached the top 10 on the UK Singles Chart: "Creep" (1992), "Street Spirit (Fade Out)" (1996), "Paranoid Android" (1997), "Karma Police" (1997), "No Surprises" (1998), "Pyramid Song" (2001), and "There There" (2003). "Creep" and "Nude" (2008) reached the top 40 on the US Billboard Hot 100. Rolling Stone named Radiohead one of the 100 greatest artists of all time, and Rolling Stone readers voted them the second-best artist of the 2000s. Five Radiohead albums have been included in Rolling Stone's 500 Greatest Albums of All Time lists. Radiohead were inducted into the Rock and Roll Hall of Fame in 2019.
Answer: Pablo Honey  - 1993; The Bends - 1995; OK Computer - 1997; Kid A - 2000; Amnesiac - 2001; Hail to the Thief - 2003; In Rainbows - 2007; The King of Limbs - 2011; A Moon Shaped Pool - 2016
Question: Which of the following is not a Hemsworth brother? Chris, Liam, Reginald, Bobert, Robert, Luke
Answer: Reginald, Bobert, and Robert are not Hemsworth brothers.
Question: What are  the main claims ?The proposed method shows neither a speed advantage nor a clear performance boost.”
Evidence: Based on the neural network method, AdaGCCN takes advantage of the multi-dimension representation ability of capsules, and further explores to strengthen the information extraction ability of the model by changing depth and width of graph convolutional layers. We reproduce CapsGNN in this paper and take the test accuracies reported in other baseline works for the comparison with our
model.
It is shown in Table 2 that our model outperforms CapsGNN on 6 out of 7 datasets by up to 6.57% on ENZYMES except for the performance on PROTEINS. Note that the graphs on PROTEINS are more complex in inner relationships, and the ∆D list and the ∆W list in current AM are manually set, thus the extraction ability of AdaGCCN would be restricted somehow. However, the main point in this paper is to propose an RL-based methodology in adaptively adjusting the model structure to improve its representation ability on various workloads, some hyper-parameters defined
in the assistant module could be tuned to present better model performance in the future. Besides, AdaGCCN achieves state-of-the-art results almost on all datasets including biological and social graphs, which further demonstrates the effectiveness of adjusting D and W during only one full training. 
Figure 3 depicts the change of accuracies on the test fold when training with CapsGNN or AdaGCCN. Since the assistant module in AdaGCCN evaluates the loss reduction speed on the train fold and the accuracy change on the validation fold, as the training process goes on, it helps to select the ∆D and ∆W with the biggest accumulated reward. It is observed in the figure that AdaGCCN presents higher test accuracy than CapsGNN at arbitrary intermediate training stages. Besides, the
dynamic adjustment in our model does not do any harm to its convergency compared to static CapsGNN. Note that the number of training epochs is set to be large on all datasets to ensure either CapsGNN or AdaGCCN achieves optimal classification performance during training.
Answer: (a) The main claim of this paper is to propose an RL-based strategy for improving the classification performance of an existing graph capsule neural network, namely CapsGNN, by adaptively refining the structure of the network at runtime. Specifically, we design an assistant module to continuously adjust the depth and width of graph convolutional layers over a pre-determined sliding epoch window, based on the accuracy results on the validation set and the loss reduction rate on the train set. (b) Figure 4 implies that the speedup in training brought by the parallel approach described in Section 3.3 outweighs the computational overhead introduced by the assistant module. (c) Table 2 shows that AdaGCCN outperforms CapsGNN on 6 out of 7 datasets and achieves state-of-the-art results on almost all the datasets.
Question: Are results reported only for English data?
Evidence: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. We also analyze the output text and the output label distribution of the models, showing the power of the proposed approach. Finally, we show the cases where the correspondences learned by the proposed approach are still problematic, which can be explained based on the approach we adopt.
Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo. The whole dataset is split into three parts, with 2,400,591 pairs in PART I for training, 10,666 pairs in PART II for validation, and 1,106 pairs in PART III for testing. The authors of the dataset have manually annotated the relevance scores, ranging from 1 to 5, of the text-summary pairs in PART II and PART III. They suggested that only pairs with scores no less than three should be used for evaluation, which leaves 8,685 pairs in PART II, and 725 pairs in PART III. From the statistics of the PART II and PART III, we can see that more than 20% of the pairs are dropped to maintain semantic quality. It indicates that the training set, which has not been manually annotated and checked, contains a huge quantity of unrelated text-summary pairs.
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model.
Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo. 
Answer: No.
Question: Give me a list of the last iceskate i've never seen
Answer: Patins Bauer Vapor Hyperlite senior Marque Bauer 1259.95 € 984.95 € Patins Bauer Supreme Mach senior Patins Bauer Supreme Mach. Patins Bauer Supreme Mach senior Marque Bauer 1259.95 € 984.95 € Patins CCM JetSpeed FT4 Pro senior CCM JetSpeed FT4 Pro senior.
Question: What are a some nicknames for the name William?
Answer: Will, Bill, Billy, Willy
Question: What was their performance?
Evidence: In this section, we report the results obtained by our model according to challenge official evaluation metric, which is based cosine-similarity and described in BIBREF27 . Results are reported for three diverse configurations: (i) the full system; (ii) the system without using word embeddings (i.e. Glove and DepecheMood); and (iii) the system without using pre-processing. In Table TABREF17 we show model's performances on the challenge training data, in a 5-fold cross-validation setting.
Further, the final performances obtained with our approach on the challenge test set are reported in Table TABREF18 . Consistently with the cross-validation performances shown earlier, we observe the beneficial impact of word-representations and basic pre-processing.
FLOAT SELECTED: Table 1: Cross-validation results
FLOAT SELECTED: Table 2: Final results
In Table TABREF17 we show model's performances on the challenge training data, in a 5-fold cross-validation setting.

Further, the final performances obtained with our approach on the challenge test set are reported in Table TABREF18 . Consistently with the cross-validation performances shown earlier, we observe the beneficial impact of word-representations and basic pre-processing.
FLOAT SELECTED: Table 1: Cross-validation results
FLOAT SELECTED: Table 2: Final results
Answer: The answers are shown as follows:
* beneficial impact of word-representations and basic pre-processing
Question: Make a pro and con list for two different options for a first date: dinner and a movie, or a trip to a bowling alley. Give at least 3 pros and cons for each option
Answer: Option 1: Dinner and a Movie:
- Pros: Can chat at dinner and then relax during the movie, don't need to think too much, very safe bet
- Cons: Movie could be boring, dinner is expensive, what if my date is a picky eater and doesn't like the restaurant? 
Option 2: Bowling Alley:
- Pros: Lots of opportunities to talk the whole time, no need to worry about picking a bad movie, can focus on the game if the conversation is stilted
- Cons: I'm not very good at bowling, bowling is associated with old people, bowling alley might be crowded and loud, making it hard to hear
Question: What is Utah known for?
Answer: Utah is known for its beautiful natural landscape throughout the state. It is home to five world renowned National Parks. Utah is also called Silicone Slopes due to the large number of tech and software companies that have made Utah their homes. Utah is a very safe place to live and raise a family. It is home to the Utah Jazz, Real Salt Lake, Utah Grizzlies, and six well-known Universities, like BYU and University of Utah to name a few.
Question: Based on this paragraph, list the schools Ted attended as a child, separated by bullets.
Evidence: As a child, Ted was frequently uprooted by his family's moves among Bronxville, New York; Hyannis Port, Massachusetts; Palm Beach, Florida; and the Court of St. James's, in London, England. His formal education started at Gibbs School in Kensington, London. He had attended ten schools by the age of eleven; these disruptions that interfered with his academic success. He was an altar boy at the St. Joseph's Church and was seven when he received his First Communion from Pope Pius XII in the Vatican. He spent sixth and seventh grades at the Fessenden School, where he was a mediocre student, and eighth grade at Cranwell Preparatory School; both schools located in Massachusetts. He was the youngest child and his parents were affectionate toward him, but they also compared him unfavorably with his older brothers. Between the ages of eight and sixteen, Ted suffered the traumas of his sister Rosemary's failed lobotomy and the deaths of two siblings: Joseph Jr. in World War II and Kathleen in an airplane crash. Ted's affable maternal grandfather, John F. Fitzgerald, was the Mayor of Boston, a U.S. Congressman, and an early political and personal influence. Ted spent his four high-school years at Milton Academy, a preparatory school in Milton, Massachusetts, where he received B and C grades. In 1950, he finished 36th in a graduating class of 56. He did well at football there, playing on the varsity in his last two years; the school's headmaster later described his play as "absolutely fearless ... he would have tackled an express train to New York if you asked ... he loved contact sports". Kennedy also played on the tennis team and was in the drama, debate, and glee clubs.
Answer: -Gibbs School
-Fessenden School
Cranwell Preparatory School
-Milton Academy
Question: How Many Players are there in a Football Team?
Answer: Two teams of eleven (11) players are usually involved in a typical game of regulation football. It is worth noting that a variety of player formations get used in different competitions. For example, there are some variances in Premiership, League and in European matches.

In 11-a-side games, there must be at least seven (7) players. The rules allow for three (3) substitutes from the 7 available. But, their names must get nominated before the game begins.
Question: What method does the author take?
Evidence: Neural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, such as English-French BIBREF0, BIBREF1 and Chinese-English BIBREF2. However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
The understanding of NMT models has been approached primarily from two complementary perspectives. The first thread of work aims to understand the importance of representations by analyzing the linguistic information embedded in representation vectors BIBREF3, BIBREF4 or hidden units BIBREF5, BIBREF6. Another direction focuses on understanding the importance of input words by interpreting the input-output behavior of NMT models. Previous work BIBREF7 treats NMT models as black-boxes and provides explanations that closely resemble the attention scores in NMT models. However, recent studies reveal that attention does not provide meaningful explanations since the relationship between attention scores and model output is unclear BIBREF8.
In this paper, we focus on the second thread and try to open the black-box by exploiting the gradients in NMT generation, which aims to estimate the word importance better. Specifically, we employ the integrated gradients method BIBREF9 to attribute the output to the input words with the integration of first-order derivatives. We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
We analyze the linguistic behaviors of words with the importance and show its potential to improve NMT models. First, we leverage the word importance to identify input words that are under-translated by NMT models. Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods. Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language. For example, nouns are more important for Chinese$\Rightarrow $English translation, while prepositions are more important for English-French and -Japanese translation. This finding can inspire better design principles of NMT architectures for different language pairs. For instance, a better architecture for a given language pair should consider its own language characteristics.
Specifically, we employ the integrated gradients method (Sundararajan et al., 2017) to attribute the output to the input words with the integration of first-order derivatives.
Answer: They employ the integrated gradients method to attribute the output to the input words with the integration of first-order derivatives.
Question: Which model gave the best results on three datasets (SNLI, MultiNLI and Quora) when trained and tested on the same corpus?
Evidence: When trained and tested on the same corpus, the DL model with GloVe embeddings gave the best results on three datasets (SNLI, MultiNLI and Quora). Logistic Regression gave the best Accuracy on the Clinical-RQE dataset with 98.60%. When tested on our test set (850 medical CHQs-FAQs pairs), Logistic Regression trained on Clinical-QE gave the best performance with 73.18% Accuracy.
The SNLI and multi-NLI models did not perform well when tested on medical RQE data. We performed additional evaluations using the RTE-1, RTE-2 and RTE-3 open-domain datasets provided by the PASCAL challenge and the results were similar. We have also tested the SemEval-cQA-2016 model and had a similar drop in performance on RQE data. This could be explained by the different types of data leading to wrong internal conceptualizations of medical terms and questions in the deep neural layers. This performance drop could also be caused by the complexity of the test consumer health questions that are often composed of several subquestions, contain contextual information, and may contain misspellings and ungrammatical sentences, which makes them more difficult to process BIBREF48 . Another aspect is the semantics of the task as discussed in Section SECREF6 . The definition of textual entailment in open-domain may not quite apply to question entailment due to the strict semantics. Also the general textual entailment definitions refer only to the premise and hypothesis, while the definition of RQE for question answering relies on the relationship between the sets of answers of the compared questions.
When trained and tested on the same corpus, the DL model with GloVe embeddings gave the best results on three datasets (SNLI, MultiNLI and Quora).
Answer: The DL model with GloVe embeddings.
Question: What other non-neural baselines do the authors compare to? 
Evidence: is the standard word counting method whereby the feature vector represents the term frequency of the words in a sentence.
is similar to BoW, except that it is derived by the counting of the words in the sentence weighted by individual word's term-frequency and inverse-document-frequency BIBREF31 . This is a very competitive model especially on clean and small dataset.
is derived from clustering of words-embeddings with k-means into 5000 clusters, and follow by BoW representation of the words in 5000 clusters.
is the standard word counting method whereby the feature vector represents the term frequency of the words in a sentence.
is similar to BoW, except that it is derived by the counting of the words in the sentence weighted by individual word's term-frequency and inverse-document-frequency BIBREF31 .
is derived from clustering of words-embeddings with k-means into 5000 clusters, and follow by BoW representation of the words in 5000 clusters.
Answer: bag of words, tf-idf, bag-of-means
Question: In a concise bulleted list, tell me what audiologists do when they are seeing a patient
Evidence: Audiology (from Latin audīre, "to hear"; and from Greek -λογία, -logia) is a branch of science that studies hearing, balance, and related disorders. Audiologists treat those with hearing loss and proactively prevent related damage. By employing various testing strategies (e.g. behavioral hearing tests, otoacoustic emission measurements, and electrophysiologic tests), audiologists aim to determine whether someone has normal sensitivity to sounds. If hearing loss is identified, audiologists determine which portions of hearing (high, middle, or low frequencies) are affected, to what degree (severity of loss), and where the lesion causing the hearing loss is found (outer ear, middle ear, inner ear, auditory nerve and/or central nervous system). If an audiologist determines that a hearing loss or vestibular abnormality is present, they will provide recommendations for interventions or rehabilitation (e.g. hearing aids, cochlear implants, appropriate medical referrals).
Answer: * Audiologists aim to determine whether someone has normal sensitivity to sounds.
* If hearing loss is identified, audiologists determine which portions of hearing are affected, to what degree, and where the lesion causing the hearing loss is found
* If an audiologist determines that hearing loss is present, they will provide recommendations for interventions or rehabilitation