DataHammer/scimrc huggingface dataset:
Question: How do they combine text representations with the knowledge graph embeddings?
Evidence: The BERT architecture uses 12 hidden layers, each layer consists of 768 units. To derive contextualized representations from textual features, the book title and blurb are concatenated and then fed through BERT. To minimize the GPU memory consumption, we limit the input length to 300 tokens (which is shorter than BERT's hard-coded limit of 512 tokens). Only 0.25% of blurbs in the training set consist of more than 300 words, so this cut-off can be expected to have minor impact.
The non-text features are generated in a separate preprocessing step. The metadata features are represented as a ten-dimensional vector (two dimensions for gender, see Section SECREF10). Author embedding vectors have a length of 200 (see Section SECREF22). In the next step, all three representations are concatenated and passed into a MLP with two layers, 1024 units each and ReLu activation function. During training, the MLP is supposed to learn a non-linear combination of its input representations. Finally, the output layer does the actual classification. In the SoftMax output layer each unit corresponds to a class label. For sub-task A the output dimension is eight. We treat sub-task B as a standard multi-label classification problem, i. e., we neglect any hierarchical information. Accordingly, the output layer for sub-task B has 343 units. When the value of an output unit is above a given threshold the corresponding label is predicted, whereby thresholds are defined separately for each class. The optimum was found by varying the threshold in steps of $0.1$ in the interval from 0 to 1.
To derive contextualized representations from textual features, the book title and blurb are concatenated and then fed through BERT
The non-text features are generated in a separate preprocessing step. The metadata features are represented as a ten-dimensional vector (two dimensions for gender, see Section SECREF10). Author embedding vectors have a length of 200 (see Section SECREF22). In the next step, all three representations are concatenated and passed into a MLP with two layers, 1024 units each and ReLu activation function.
Answer: The answers are shown as follows:
* all three representations are concatenated and passed into a MLP
Question: Make up a plausible fishing story about the one that got away. Write in the first person.
Answer: Minnesota is the land of 10,000 lakes. A few years ago, my brother and I decided to try our luck at one of the best rumored spots for largemouth bass, Otter Tail Lake. We arrived just after dawn and walked about 30 minutes from public access parking to a secluded bank which we hoped to have to ourselves. 

There was not much action. My brother questioned our choice of bait, nightcrawlers, but I told him to keep faith. We were hungry, but realized we'd forgotten our pastries in the car. My brother offered to fetch breakfast. I continued to fish while he was away.

I felt a hard tug on my line. I pulled back on the rod to confirm the bite. I had something big on the line! Carefully I turned the crank, pausing periodically to give the fish a chance to exhaust itself. My patience paid off. I could see the bass in the water a few yards off shore. It appeared to be larger than my arm. I turned the crank and pulled hard on the rod to finish the job. The fish was a fighter, but I felt good about my chances. But suddenly my line snapped and the bass disappeared. 

When my brother returned, I told him my story. He was a skeptic. We did not go home empty handed as we landed 3 smaller bass, the largest weighing 4 pounds. But I felt disappointed. We were fishing with a 20 pound test: the one that got away must have been a monster!
Question: What geometric properties do embeddings display?
Evidence: The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. However considering the vocabulary was reduced to $V^* = 52$ words, those results lacked of consistency. Therefore for both languages we decided to re-train the RNNs using the same architecture, but with a larger vocabulary of the $V=300$ most relevant words (still in the RF sense) and on all the available data (i.e. everything is used as training) to compensate for the increased size of the vocabulary. We then calculated the distance of a few prominent words to the others. The analysis of the average cosine distance over $B=10$ runs for three major words is given by tables TABREF38 and TABREF39, and three other examples are given in the appendix tables TABREF57 and TABREF58. The first row corresponds to the reference word vector $\overrightarrow{w_1}$ used to calculate the distance from (thus the distance is always zero), while the following ones are the 9 closest to it. The two last rows correspond to words we deemed important to check the distance with (an antagonistic one or relevant one not in the top 9 for instance).
For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days.
Answer: Winter and summer words formed two separate clusters. Week day and week-end day words also formed separate clusters.
Question: What are the baselines of this paper？
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA.
Answer: Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA
Question: Extract how many meetings rooms and seats are available in the main hall from the passage provided
Evidence: It has a plenary hall that has 5,000 seats. JCC also has an assembly hall with an area of 3,921 square metres. JCC has 13 various sized meeting rooms. JCC is connected to The Sultan Hotel & Residence Jakarta (formerly Jakarta Hilton International) by a tunnel. The tunnel has moving walkways and is air-conditioned. 
Answer: The JCC or Jakarta Convention Center has 13 various sized meetings rooms. It currently has a capacity of 5,000 seats in the main plenary hall
Question: What is the difference between American football and European football?
Answer: American football and European football are two different sports. American football, also known as gridiron, was first played in 1869 in the United States and mostly known in the United States and Canada. It rivals two teams on a rectangular field. Both teams have an offence as well as a defence formation. The first offence and defence of the match is decided by coin tossed where the winner decide for the first half of the match while the opposite match starts at the half-time mark. The offence team tries to take the oval-shaped ball to the other side of the field by passing or running it at each play. The defence team blocks the offence team so they get the balls themselves either by steeling it or when the offence team hasn’t progressed 10 yards in 4 plays. Then, offence team becomes defence and vice versa. When the offence team successfully reach the 10-yard lines, they obtain a new set of 4 plays to get to the endzone, where they obtain points when succeeding, called a touchdown. They also get points from kicking the ball within the goalpost. The winning team is the one with the most points at the end of the game. European football, also known as soccer, is a match between two teams of eleven players on a rectangular field, known as pitch. Players primarily use their feet to propel a sphere-shaped ball across the field into the rectangular shaped goal guarded by a goalkeeper. The goalkeeper is the only player of each team that can use his hands and arms on the ball within a specific region of the field close to their goals. The game is played continuously until a fault is committed or the ball exits the field. Each team tried and kick the ball in the goal to get points and the winner is the team with the most point at the end of the match.
Question: What is the danger existing in geometric averages?
Evidence: PARENT evaluates each instance INLINEFORM0 separately, by computing the precision and recall of INLINEFORM1 against both INLINEFORM2 and INLINEFORM3 .
The danger with geometric averages is that if any of the components being averaged become 0, the average will also be 0.
Answer: If any of the components being averaged become 0, the average will also be 0.
Question: How does the TEXTFOOLER work？
Evidence: We experiment with five benchmark attacking methods for texts: FGM, FGVM, DeepFool BIBREF5, HotFlip BIBREF3) and TYC BIBREF4.
To perturb the discrete inputs, both FGM and FGVM introduce noises in the word embedding space via the fast gradient method BIBREF1 and reconstruct the input by mapping perturbed word embeddings to valid words via nearest neighbour search. Between FGM and FGVM, the former introduce noises that is proportional to the sign of the gradients while the latter introduce perturbations proportional to the gradients directly. The proportion is known as the overshoot value and denoted by $\epsilon $. DeepFool uses the same trick to deal with discrete inputs except that, instead of using the fast gradient method, it uses the DeepFool method introduced in BIBREF8 for image to search for an optimal direction to perturb the word embeddings.
Unlike the previous methods, HotFlip and TYC rely on performing one or more atomic flip operations to replace words while monitoring the label change given by the target classifier. In HotFlip, the directional derivatives w.r.t. flip operations are calculated and the flip operation that results in the largest increase in loss is selected. TYC is similar to FGM, FGVM and DeepFool in that it also uses nearest neighbour search to map the perturbed embeddings to valid words, but instead of using the perturbed tokens directly, it uses greedy search or beam search to flip original tokens to perturbed ones one at a time in order of their vulnerability.
TEXTFOOLER, the only black-box attack method tested, is a query-based method. Since it assumes no access to the full architecture of the target classifier, it learns the order of vulnerability of tokens in an input sentence according to the change of prediction scores produced by the target classifier when a specific token is discarded. Once the order of vulnerability of words is identified, similar to HOTFLIP and TYC, it greedily replaces tokens in the order of vulnerability one-at-a-time until the prediction of the target classifier changes.
Answer: It learns the order of vulnerability of tokens in an input sentence according to the change of prediction scores produced by the target classifier when a specific token is discarded. Once the order of vulnerability of words is identified, it greedily replaces tokens in the order of vulnerability one-at-a-time until the prediction of the target classifier changes.
Question: Which states make up the Pacific Northwest?
Answer: Most people consider the Pacific Northwest to be Washington, Oregon, and the Southern part of British Columbia. However, many consider a more broad definition that would include Northern California and Idaho as well.
Question: Provide a bulleted list of reasons why Tesla stock could go up in the future.
Answer: The following are reasons why Tesla stock could go up in the future:
1. Governments incentivizing citizens to purchase electric cars
2. Increased production of the Model S, 3, X, and Y
3. Launch of Tesla Cyber Truck
4. Launch of a cheaper, more widely used Tesla Model
5. Opening of more Giga Factories
6. Enhanced production capacity at current giga factories
7. Expansion of solar deployments
8. Expansion of the super charger network
9. Expansion of megapack deployments
10. Expansion of the Tesla insurance program
11. Improvement to full self driving
12. Launch of a robotaxi network.
Question: Give me a bulleted list of the the first five books in the bible new testament?
Answer: The New Testament is the second division of the Christian biblical canon. It discusses the teachings and person of Jesus, as well as events in first-century Christianity. The first five books of the new testament are 
1) The Gospel of  Matthew - It is one of the four gospels of the New Testament that tell the story of Jesus' life and teachings.
2 The Gospel of Mark - It tells of the ministry of Jesus from his baptism by John the Baptist to his death, burial, and the discovery of his empty tomb. It portrays Jesus as a teacher, an exorcist, a healer, and a miracle worker, though it does not expound upon the miraculous birth or divine pre-existence.
3) The Gospel of Luke - tells of the origins, birth, ministry, death, resurrection, and ascension of Jesus Christ.
4) The Gospel of John -  It contains a highly schematic account of the ministry of Jesus, with seven "signs" culminating in the raising of Lazarus and seven "I am" discourses culminating in Thomas' proclamation of the risen Jesus as "my Lord and my God". The gospel's concluding verses set out its purpose, "that you may believe that Jesus is the Christ, the Son of God, and that believing you may have life in his name
5) Acts of the Apostles - is an attempt to answer a theological problem, namely how the Messiah, promised to the Jews, came to have an overwhelmingly non-Jewish church; the answer it provides, and its central theme, is that the message of Christ was sent to the Gentiles because the Jews rejected it
Question: Given a reference text about Anne Elizabeth Howells, tell me the names' of her parents and how she died.
Evidence: Anne Elizabeth Howells (12 January 1941 – 18 May 2022) was a British operatic mezzo-soprano.

Biography
Howells was born in Southport, Lancashire on 12 January 1941, the daughter of Trevor Howells and Mona Howells (née Hewart). She was educated at Sale Grammar School, where her teachers included Alfred Higson. She further studied music at the Royal Manchester College of Music (now the Royal Northern College of Music), where her teachers included Frederic Cox. Howells continued singing studies with Vera Rózsa.

Howells sang in productions at Glyndebourne, the Royal Opera House, and with the Metropolitan Opera in New York, the Lyric Opera of Chicago, the opera houses of both San Francisco and Los Angeles, the four Parisian opera houses, the Grand Théâtre de Genève, the Salzburger Festspiele, the Vienna Staatsoper, the Carnegie Hall, and the Musikverein of Vienna. She sang in the premieres of Rolf Liebermann's Forêt (as Régine), Nicholas Maw's The Rising of the Moon (as Cathleen) and of Richard Rodney Bennett's Victory (as Lena). Following her retirement from public performance, Howells taught at the Royal Academy of Music.
Answer: Anne Elizabeth Howells parents are Trevor and Mona Howells. She died from myeloma.
Question: When and where was Lyubomir Nikolov Vladikin born?
Evidence: Vladikin was born on September 3, 1891 in the village of Golyamo Belovo, Pazardzhik area. His mother, Maria Morfova, sister of the opera singer Hristina Morfova and the composer Aleksandar Morfov, studied French Philology in Geneva. His father, Nikola Yonkov Vladikin, was a well-known journalist, scholar, writer, publicist, historian and political activist. Over the period from 1890 to 1912 he was an MP for three consecutive terms. He was a nephew of the Bishop of South Bulgaria (from Ohrid to Edirne), known under the church name Evstatiy Pelagoniyski.
Answer: Vladikin was born on September 3, 1891 in the village of Golyamo Belovo, Pazardzhik
Question: Did Damien Chazelle direct movies or TV shows first?
Evidence: Damien Sayre Chazelle (/ʃəˈzɛl/; born January 19, 1985) is a French-American film director, screenwriter and producer. He is known for directing the films Whiplash (2014), La La Land (2016), First Man (2018) and Babylon (2022).

For Whiplash, he was nominated for the Academy Award for Best Adapted Screenplay. His biggest commercial success came with La La Land, which was nominated for 14 Academy Awards, winning six including Best Director, making him the youngest person to win the award at age 32. He made his television debut directing the Netflix limited series The Eddy (2020).
Answer: Damien Chazelle directed movies first. His first directed TV show was in 2020 while he has directed movies since at least 2014.
Question: What are the classifications of Academic Degrees?
Answer: An academic degree is a qualification awarded to students upon successful completion of a course of study in higher education, usually at a college or university. These institutions commonly offer degrees at various levels, usually including undergraduate degrees, master's, and doctorates, often alongside other academic certificates and professional degrees. The most common undergraduate degree is the bachelor's degree, although in some countries there are lower-level higher education qualifications that are also titled degrees (e.g. associate degrees and foundation degrees).
Question: Who was the first to record the Needles and Pins song?
Evidence: "Needles and Pins" is a rock song credited to American writers Jack Nitzsche and Sonny Bono. Jackie DeShannon recorded it in 1963 and other versions followed. The most successful ones were by the Searchers, whose version reached No. 1 on the UK singles chart in 1964, and Smokie, who had a worldwide hit in 1977. Others who recorded the song include the Ramones, Gene Clark, and Tom Petty and the Heartbreakers with Stevie Nicks.

Jackie DeShannon version (1963)
In his autobiography, Bono states that he sang along with Nitzsche's guitar-playing, thus creating both the tune and the lyrics, being guided by the chord progressions. However, Jackie DeShannon claims that the song was written at the piano, and that she was a full participant in the song's creation, along with Nitzsche and Bono, although she did not get formal credit.

DeShannon was the first to record the song; in the US it peaked at number 84 on the Billboard Hot 100 singles chart in May 1963. Though it was only a minor US hit, DeShannon's recording of the song topped the charts in Canada, hitting number one on the CHUM Chart in July 1963
Answer: The first artist to record Needles and Pins song was DeShannon.
Question: Write an explanation for why someone would want to visit New York City. Use details about the nightlife, restaurants, arts and entertainment.
Answer: New York City is one of the most exciting places to visit in the United States. Among many factors, New York offers world-class museums and art galleries, as well as concerts, theatre productions. The city also offers a wide variety of dining options - ranging from Michelin-starred restaurants to street carts, and everything in between. In addition to eating, visitors can go to hundreds of different bars and nightclubs throughout the city.