DataHammer/scimrc huggingface dataset:
Question: Which datasets are used to train this model?
Evidence: We evaluate performance of our models on the SQUAD BIBREF16 dataset (denoted $\mathcal {S}$ ). We use the same split as that of BIBREF4 , where a random subset of 70,484 instances from $\mathcal {S}\ $ are used for training ( ${\mathcal {S}}^{tr}$ ), 10,570 instances for validation ( ${\mathcal {S}}^{val}$ ), and 11,877 instances for testing ( ${\mathcal {S}}^{te}$ ).
We evaluate performance of our models on the SQUAD BIBREF16 dataset (denoted $\mathcal {S}$ ). We use the same split as that of BIBREF4 , where a random subset of 70,484 instances from $\mathcal {S}\ $ are used for training ( ${\mathcal {S}}^{tr}$ ), 10,570 instances for validation ( ${\mathcal {S}}^{val}$ ), and 11,877 instances for testing ( ${\mathcal {S}}^{te}$ ).
Answer: The answers are shown as follows:
* SQUAD
Question: Does the proposed approach improve translation performance?
Evidence: Neural machine translation (NMT) is popularly implemented as an encoder-decoder framework BIBREF0, in which the encoder is right in charge of source sentence representation. Typically, the input sentence is implicitly represented as a contextualized source representation through deep learning networks. By further feeding the decoder, the source representation is used to learn dependent time-step context vectors for predicting target translation BIBREF1.
In state-of-the-art Transformer-based encoder, self-attention mechanisms are good at capturing the general information in a sentence BIBREF2, BIBREF3, BIBREF4. However, it is difficult to distinguish which kind of information lying deeply under the language is really salient for learning source representation. Intuitively, when a person reads a source sentence, he/she often selectively focuses on the basic sentence meaning, and re-reads the entire sentence to understand its meaning completely. Take the English sentence in Table TABREF2 as an example. We manually annotate its basic meaning as a shorter sequence of words than in the original sentence, called backbone information. Obviously, these words with the basic meaning contain more important information for human understanding than the remaining words in the sentence. We argue that such backbone information is also helpful for learning source representation, and is not explicitly considered by the existing NMT system to enrich the source sentence representation.
In this paper, we propose a novel explicit sentence compression approach to enhance the source representation for NMT. To this end, we first design three sentence compression models to accommodate the needs of various languages and scenarios, including supervised, unsupervised, and semi-supervised ways, to learn a backbone information words sequence (as shown in Table TABREF2) from the source sentence. We then propose three translation models, including backbone source-side fusion based NMT (BSFNMT), backbone target-side fusion (BTFNMT), and both-side fusion based NMT (BBFNMT), to introduce this backbone knowledge into the existing Transformer NMT system for improving translation predictions. Empirical results on the WMT14 English-to-German and English-to-French translation tasks show that the proposed approach significantly improves the translation performance over the strong even state-of-the-art NMT baselines.
Empirical results on the WMT14 English-to-German and English-to-French translation tasks show that the proposed approach significantly improves the translation performance over the strong even state-of-the-art NMT baselines.
Answer: Yes.
Question: Given this paragraph about Michel Warschawski, tell me the two organizations that Warschawski led during his life.
Evidence: Michel Warschawski (Mikado) (born 25 July 1949) is an Israeli anti-Zionist activist. He led the Marxist Revolutionary Communist League (previously Matzpen-Jerusalem) until its demise in the 1990s, and founded the Alternative Information Center, a joint Palestinian-Israeli non-governmental organization, in 1984.
Answer: Michel Warschawski, an anti-Zionist activist, led the Marxist Revolutionary Communist League and later the Alternative Information Center.
Question: When did King Charles II return to Navarre?
Evidence: King Charles II returned to Navarre in November 1361, leaving his wife and son behind in France. When in January 1363 it was her turn to move to Navarre, Queen Joan left Charles in the custody of his aunt Blanche, sister of his father and widow of his maternal great-grandfather King Philip VI of France. Charles was reunited with his mother when she returned to France in December 1365, and the following August Charles set foot in his father's kingdom for the first time.
Answer: King Charles II returned to Navarre in November 1361.
Question: How many windows does the building have based on the provided paragraph?
Evidence: The main entrance on the west side of the building faces North 14th Avenue. The building is fronted by a concrete porch with three steps leading to louvered double doors surrounded by windows. Panels of five lights each flank the doors, and above it a seven panel fanlight frames a sign with the club's name. Gabled returns supported by pilasters and a pair of Tuscan columns show a neoclassical architectural influence. Two four-light windows with green shutters are centered on either side of the door. Each of the windows has green two-panel shutters with cutouts of a full moon on top and a crescent moon on the bottom. The north and south side have four windows each, and the back of the building has three. The building is painted white and sheathed in clapboard except the four bottom courses covered with asbestos shingles. The porches and roof are green.
Answer: There are a total of 13 windows.

The side of the house with the entrance has two four-light windows, the north and south side have four windows each, and the back of the building has three.

Therefore, in total, the house has 2 + 4 + 4 + 3 windows or 13 windows.
Question: Give me a list of the usual accompaniments that are served with Peking duck
Evidence: Peking duck is a dish from Beijing (Peking) that has been prepared since the Imperial era. The meat is characterized by its thin, crispy skin, with authentic versions of the dish serving mostly the skin and little meat, sliced in front of the diners by the cook. Ducks bred especially for the dish are slaughtered after 65 days and seasoned before being roasted in a closed or hung oven. The meat is often eaten with spring onion, cucumber and sweet bean sauce with pancakes rolled around the fillings. Sometimes pickled radish is also inside. Crispy aromatic duck is a similar dish to Peking duck and is popular in the United Kingdom.
Answer: Peking duck is usually accompanied with spring onion, cucumber and sweet bean sauce
Question: What is the difference between a corporate venture function and institutional venture firm?
Answer: A corporate venture function, commonly known as a CVC, is typically a team within an operating entity that is focused on venture investing on behalf of its parent company. Investments completed by a corporate venture function is usually related to the industry the parent company operates in and investment goals are strategic in additional to being financial. An institutional venture firm is a standalone company whose operations is solely focused on investments and the focus is on financial returns.
Question: What is Detroit style pizza?
Answer: Detroit pizza is cooked in a square or rectangular pan made from steel or aluminum. The dough is thicker than a typical round pizza but is more airy. An oiled pan provides a nice, crispy dough. Caramelized cheese around the edges of the pizza is one of Detroit pizza’s key features. The pizza sauce is often applied on top of the cheese layer with this style of pizza.
Question: List a few female comedians that have Netflix specials
Answer: Ali Wong, Wanda Sykes, and Taylor Tomlinson
Question: Tell me whether these foods need to be refrigerated or be frozen: milk, meat, sea food, fruit
Answer: Milk and fruit need to be refrigerated while meat and sea food in general need to be frozen.
Question: What is the special aspect of Einstein's special theory of relativity?
Evidence: Until several years later when Einstein developed general relativity, which introduced a curved spacetime to incorporate gravity, the phrase "special relativity" was not used. A translation sometimes used is "restricted relativity"; "special" really means "special case".[p 2][p 3][p 4][note 1] Some of the work of Albert Einstein in special relativity is built on the earlier work by Hendrik Lorentz and Henri Poincaré. The theory became essentially complete in 1907.

The theory is "special" in that it only applies in the special case where the spacetime is "flat", that is, where the curvature of spacetime (a consequence of the energy–momentum tensor and representing gravity) is negligible.[note 2] In order to correctly accommodate gravity, Einstein formulated general relativity in 1915. Special relativity, contrary to some historical descriptions, does accommodate accelerations as well as accelerating frames of reference.
Answer: The "special" part of Einstein's special theory of relativity is a reference to it being a special case of his General Theory of Relativity. Special relativity only applies in the special case where the curvature of spacetime is negligible, i.e., spacetime is "flat". Special relativity was formulated in 1907, and in order to correctly accommodate gravity, Einstein formulated general relativity in 1915.
Question: How can I learn to snowboad?
Answer: While there are many different ways to learn how to snowboard, here are some of the more common ones:

1. Take a professional lesson. These are typically offered as half-day or full-day, and either as a private lesson or in a group of typically up to eight people.

2. Have a friend teach you. This is a more cost-conscious way, however, ensure that your friend knows how to properly teach.

3. Teach yourself. Utilize video tutorials found on various sites and get out there and try it.

Remember that snowboarding, while fun, is still an activity that could cause harm, so be sure to always wear a properly fitting helmet.
Question: Give me a list of the best sad songs to play.
Answer: ‘Nothing Compares 2 U’ by Sinéad O’Connor
‘Hurt’ by Johnny Cash
‘Only Love Can Break Your Heart’ by Neil Young
‘Teardrop’ by Massive Attack
‘I Know It’s Over’ by The Smiths
‘No Distance Left to Run’ by Blur
‘The Boxer’ by Simon & Garfunkel
‘Tom Traubert’s Blues (Four Sheets to the Wind in Copenhagen)’ by Tom Waits
‘Lazarus’ by David Bowie
Question: Identify which instrument is string or percussion: Onavillu, Lotar
Answer: Lotar is string, Onavillu is percussion.
Question: Who are some of the most famous weird fiction authors, who wrote in the early 20th century, aside from H. P. Lovecraft? And what is a good short story from each that I might like, if I like H. P. Lovecraft?
Answer: Some well-regarded weird fiction authors from the early 20th century include Arthur Machen ("The White People"), Clark Ashton Smith ("The Vaults of Yoh-Vombis"), and Robert W. Chambers ("The Repairer of Reputations").
Question: What was the first commercial relational database product launched in the market?
Answer: Oracle Database (commonly referred to as Oracle DBMS, Oracle Autonomous Database, or simply as Oracle) is a multi-model database management system produced and marketed by Oracle Corporation. It was the first RDBMS product commercially available in the market in the year 1979.
Question: What MLlib do they use in the experiment?
Evidence: We implement our method based on Scikit-Learn BIBREF11 and PyTorch , and the experiments are conducted on a server with NVIDIA Tesla K80 GPU.
We implement our method based on Scikit-Learn [10] and PyTorch5 , and the experiments are conducted on a server with NVIDIA Tesla K80 GPU.
Answer: Scikit-Learn and PyTorch.
Question: Tell me whether these are assets or liabilities: Cash, Accounts Receivables, Deferred Commissions, Prepaid Expenses, Long-term Investments, Property Plant & Equipment, Operating lease right-of-use assets, Goodwill, Intangible Assets, Accounts Payable, Accrued Expenses, Operating Lease Liabilities, Deferred Revenue
Answer: Assets: Cash, Accounts Receivables, Deferred Commissions, Prepaid Expenses, Long-term Investments, Property Plant & Equipment, Operating lease right-of-use assets, Goodwill, Intangible Assets

Liabilities: Accounts Payable, Accrued Expenses, Operating Lease Liabilities, Deferred Revenue
Question: What are some comfort food style breakfasts that include eggs?
Answer: Eggs are used in many breakfast recipes, including ones that are comfort food style. Some ideas for such foods would be:
- French toast
- Bacon, egg, and cheese sandwich
- Crepes with fruit and cheese filling
- Shakshuka with warmed pita
- Omelette
- Potato and vegetable hash with fried eggs
Question: Based on this paragraph from Wikipedia, where was Jerry Garcia born, how many fingers did he have, and where did he attend college?
Evidence: Jerry Garcia was born August 1, 1942, in San Francisco, California. His father was Jose Ramon Garcia, a Spanish immigrant. His mother, Ruth Marie Clifford, named him after her favorite composer, Jerome Kern. His father was a jazz musician who bought a bar. As a child Garcia took piano lessons. When he was four he lost part of a finger chopping wood. A year later his father drowned. When his mother took over the bar, Garcia went to live with his grandparents. They introduced him to the Grand Ole Opry. This along with his brother's collection of Chuck Berry records gave him an interest in country music. In 1953 his mother remarried and Garcia moved back with her. Garcia attended several different schools in and around San Francisco. But he had little interest in school until 1957 when he attended the California School of Fine Arts (now the San Francisco Art Institute). At age 15 his mother gave him a guitar. In high school he played in several bands. At age 17 he quit school. He joined the Army in 1960 but was given a general discharge a few months later.
https://simple.wikipedia.org/wiki/Jerry_Garcia
Answer: Based on this paragraph, Jerry Garcia was born in San Francisco, had nine fingers and he quit school at age 17 before attending college.
Question: Imagine you have to spend a week on a deserted island. What are 5 items you'd bring with you and why?
Answer: If I was stranded on a deserted island for a week the first thing I'd be sure to pack is sun screen!  My biggest fear would be exposure to the elements while trying to forage for food, build a shelter and come up with a game plan to be rescued.  My 2nd item would be some form of fishing tackle, I assume the easiest thing to catch out in the middle of the ocean would be fish. I'd be sure to bring plenty of hooks, line and artificial lures in my tackle box.  The 3rd item I'd be sure to pack would be a fire-starter.  I would choose this over matches because flint/steel work while wet where matches become useless pretty quickly when wet. The 4th item I'd bring would be a guitar, there's something very relaxing and peaceful about music while you're alone and I imagine it would help pass the time.  The last item I would bring would be a picture of my family.  That would keep me going in even the darkest of times. When the fish aren't biting or the weather isn't cooperating - knowing I have a lovely family to get back home to would provide all the motivation in the world to keep fighting!
Question: What is the reason behind the drop in performance using BERT for some popular task?
Evidence: It is clear from the above plots that as we increase the percentage of error, for each of the three tasks, we see a significant drop in BERT’s performance. Also, from the plots it is evident that the reason for this drop in performance is introduction of noise (spelling mistakes). After all we get very good numbers, for each of the three tasks, when there is no error (0.0 % error). To understand the reason behind the drop in performance, first we need to understand how BERT processes input text data. BERT uses WordPiece tokenizer to tokenize the text. WordPiece tokenizer utterances based on the longest prefix matching algorithm to generate tokens . The tokens thus obtained are fed as input of the BERT model.
When it comes to tokenizing noisy data, we see a very interesting behaviour from WordPiece tokenizer. Owing to the spelling mistakes, these words are not directly found in BERT’s dictionary. Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
To understand the reason behind the drop in performance, first we need to understand how BERT processes input text data. BERT uses WordPiece tokenizer to tokenize the text. WordPiece tokenizer utterances based on the longest prefix matching algorithm to generate tokens . The tokens thus obtained are fed as input of the BERT model.
When it comes to tokenizing noisy data, we see a very interesting behaviour from WordPiece tokenizer. Owing to the spelling mistakes, these words are not directly found in BERT’s dictionary. Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
Answer: The answers are shown as follows:
* Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
Question: Are some models evaluated using this metric, what are the findings?
Evidence: As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters. The model's architecture is shown in Figure FIGREF30.
As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters.
Answer: Yes.
Question: What is Nasdaq?
Answer: The Nasdaq Stock Market (National Association of Securities Dealers Automated Quotations Stock Market) is an American stock exchange based in New York City. It is the most active stock trading venue in the US by volume, and ranked second on the list of stock exchanges by market capitalization of shares traded, behind the New York Stock Exchange. The exchange platform is owned by Nasdaq, Inc., which also owns the Nasdaq Nordic stock market network and several U.S.-based stock and options exchanges. According to a Gallup poll conducted in 2022, approximately 58% of American adults reported having money invested in the stock market, either through individual stocks, mutual funds, or retirement accounts.
Question: However, the authors are missing key details: for example, when the baseline methods hit OOM, what memory budgets did the authors put?
Evidence: We compare the time and memory complexity of SpaLoc against NLM on the Has-Sister and Grandparent tasks. In Fig. 5, we plot the curve of memory consumption and inference time of each sample as a function of the number of objects in the evaluation domains. The experimental results show that our method can reduce the space complexity from the original O(n3) complexity of NLM to the O(n2) complexity required to solve the has-sister and grandparent task. In terms of the inference time, there is a constant level of optimization. On small graphs, adding sparsity does not necessarily improve the running time on GPUs because of the overhead of handling sparse tensors
due to the implementation. More efficient implementation of sparse operations is interesting future work.
Answer: Thank you for your suggestion. We have included details about our memory setting in the paper. Specifically, for all methods, we have allocated 12GB of GPU memory. We added a scalability analysis in the appendix. In a nutshell, our method focuses on optimizing the space complexity of inference. The experimental results show that our method can reduce the space complexity from the original  O(n^3)complexity of NLM to the O(n^2) complexity required to solve the has-sister and grandparent task. In terms of the inference time, there is a constant level of optimization.
Question: Give me 5 great landmarks to visit in the western united states
Answer: - The Grand Canyon
- Mount Rainier
- The Golden Gate Bridge
- Yellow Stone National Park
- Disney Land
Question: Why use an ereader to read a book?
Answer: Using an electronic reader over a book has many advantages. You have immediate access to large libraries of books. You can purchase and start reading a new book in minutes. You can also take your entire library with you when you travel without lugging around many or large books. E-readers also give the ability to read in the dark without using other lighting that may disturb those around you.

While tablets have many of these same benefits, the experience on ereaders is better for reading books. The screens cause much less eye strain due to the use of electron inc on many of the devices. They are also free from notifications and other distractions found on tablets and smartphones.
Question: When was Clarence Dietz born?
Evidence: Clarence E. Dietz (April 17, 1919 – June 23, 1993) was a former Republican member of the Pennsylvania House of Representatives. He was born in 1919 to Clarence and Mary Elizabeth Dietz.

He died in 1993 at his Bedford County home after a short illness
Answer: Clarence Dietz was born in April 17, 1919.
Question: Based on this paragraph, extract the names of the books about Phish and list them using bullets.
Evidence: Several books on Phish have been published, including two official publications: The Phish Book, a 1998 coffee table book credited to the band members and journalist Richard Gehr which focused on the band's activities during 1996 and 1997, and Phish: The Biography, a semi-official biographical book written by music journalist and Phish fan Parke Puterbaugh, was published in 2009 and was based on interviews with the four band members, their friends and crew. An installment of the 33⅓ book series on A Live One, written by Walter Holland, was published in 2015. The 2013 book You Don't Know Me but You Don't Like Me: Phish, Insane Clown Posse, and My Misadventures with Two of Music's Most Maligned Tribes, written by music critic Nathan Rabin, compares and contrasts the fanbases of Phish and Insane Clown Posse.
Answer: -The Phish Book
-Phish: The Biography
-A Live One
-You Don't Know Me But You Don't Like Me: Phish, Insane Clown Posse, and My Misadventures with Two of Music's Most Maligned Tribes
Question: What were the non-neural baselines used for the task?
Evidence: BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization. The model is globally normalized with the use of a second order linear-chain CRF. To efficiently calculate the partition function, the choice of lemmata are pruned with the use of pre-extracted edit trees.
BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization. 
Answer: The Lemming model in BIBREF17