DataHammer/scimrc huggingface dataset:
Question: Wthat is the difference between Speed and Velocity ?
Answer: Speed denotes only how fast an object is moving, whereas velocity describes both how fast and in which direction the object is moving. If a car is said to travel at 60 miles per hour, its speed has been specified. However, if the car is said to move at 60 miles per hour to the north, its velocity has now been specified.

The big difference can be discerned when considering movement around a circle. When something moves in a circular path and returns to its starting point, its average velocity is zero, but its average speed is found by dividing the circumference of the circle by the time taken to move around the circle. This is because the average velocity is calculated by considering only the displacement between the starting and end points, whereas the average speed considers only the total distance travelled.
Question: What are the typical subjects that are studied in grade school in America?
Answer: English - reading and writing, math, science, and social studies. Some schools also have dedicated class time for physical education, art, and music.
Question: Classify the fruits based on their color.
raspberries, lemon, strawberries, cranberries, star fruit, banana, cherries, apples
Answer: Red - raspberries, strawberries, cranberries, cherries, apples
Yellow - lemon, banana, star fruit
Question: Which of the following words are conjunctions, and which are prepositions: and, between, but, for, in, of, on, or, under, yet
Answer: The following are conjunctions: and, but, or, yet
The following are prepositions: between, for, in, of, on, under
Question: Classify each of the following as a ridesharing or food delivery company: Doordash, Lyft, Wolt, Deliveroo, Bolt, Didi, Rappi
Answer: Doordash: Food Delivery
Lyft: Ridesharing
Wolt: Food Delivery
Deliveroo: Food Delivery
Bolt: Ridesharing
Didi: Ridesharing
Rappi: Food Delivery
Question: Extract from the text the events won by the Kawasaki KX 500 motorcycle, and separate them by a comma.
Evidence: The Kawasaki KX 500 is a 499 cc (30.5 cu in) two-stroke single motocross motorcycle made by Kawasaki from 1983 until 2004.

The Kawasaki KX500 was developed as an air-cooled 500cc motocross bike for competition in the 500cc and Open-Class of motocross. At the time of its release, several top manufactured sported entries in this class, including Yamaha, Suzuki, Honda and the class-leading Maico.

Kawasaki developed the bike on an annual basis through the 80's. The addition of liquid cooling in 1985, a new frame with improved suspension in 1987, engine updates in 1988 and reversed forks in 1990 highlight these revisions.

Kawasaki would take several years to produce a competitive bike, and when it finally did, would find the KX500 winning in events for which it was not initially designed for. Its first victories came in 1986, when Donnie Griewe won two National Hare and Hound events. These only foreshadowed Kawasaki's dominance in distance and desert racing that exploded in the 90's.

In 1989 Kawasaki captures its first National MX championship on a KX500, repeating in '90 and '92 before the series was discontinued at the close of the '93 season. But that would not be the end of the KX500's racing legacy.

Beginning in 1992, the KX500 took the victory in every Desert Nationals race through 1995, and continued to win the Desert Nationals Championship every year though 2001. It further cemented its legendary dominance in desert racing by being the victors bike at the Baja 1000 from 1988 through 1996.
Answer: National Hare and Hound, National MX championship, Desert Nationals, Baja 1000
Question: Give me a list of the ten best rock guitarists of all time
Answer: Jimi Hendrix, Stevie Ray Vaughan, Jimmy Page, Eddie Van Halen, Eric Clapton, Jeff Beck, David Gilmour, Brian May, George Harrison, Randy Rhoads
Question: What conclusion did the author draw from the experiment?
Evidence: In the present work, we propose FriendsBERT and ChatBERT for the multi-utterance emotion recognition task on EmotionLines dataset. The proposed models are adapted from BERT BIBREF5 with three main improvement during the model training procedure, which are the causal utterance modeling mechanism, specific model pre-training, and adapt weighted loss. The causal utterance modeling takes the advantages of the sentence-level context information during model inference. The specific model pre-training helps to against the bias in different text domain. The weighted loss avoids our model to only predict on large size sample. The effectiveness and generalizability of the proposed methods are demonstrated from the experiments.
In future work, we consider to include the conditional probabilistic constraint $P ({\rm Emo}_{B} | \hat{\rm Emo}_{A})$. Model should predict the emotion based on a certain understanding about context emotions. This might be more reasonable for guiding model than just predicting emotion of ${\rm Sentence}_B$ directly. In addition, due to the limitation of BERT input format, ambiguous number of input sentences is now becoming an important design requirement for our future work. Also, personality embedding development will be another future work of the emotion recognition. The personality embedding will be considered as sentence embedding injected into word embedding, and it seems this additional information can contribute some improvement potentially.
The proposed models are adapted from BERT [Devlin et al., 2018] with three main improvement during the model training procedure, which are the causal utterance modeling mechanism, specific model pretraining, and adapt weighted loss. The causal utterance modeling takes the advantages of the sentence-level context information during model inference. The specific model pretraining helps to against the bias in different text domain. The weighted loss avoids our model to only predict on large size sample. The effectiveness and generalizability of the proposed methods are demonstrated from the experiments.
Answer: The effectiveness and generalizability of the proposed methods are demonstrated from the experiments.
Question: What causes an airplane wing to fly?
Answer: As Bernoulli's principal explains, because of the shape, air moving across the curved upper surface on top of the wing will travel faster and thus produce less pressure than the air moving more slowly along the flatter bottom of the wing's surface. This imbalance of high and low pressures results in a force called lift which is applied in the direction of the lower pressure.
Question: What are the words of House Marbrand?
Answer: "Burning Bright"
Question: Give me a simpler way to tell someone that Layli and Majnun is Persian literature that borrows storytelling techniques from Varqeh and Golshah, Orwa wa Afra, Vameq and Ozra, Metiochus and Parthenope, and Vis and Ramin.
Answer: Layli and Majnun is based on several prior romantic tales, such as Varqeh and Golshah, Orwa wa Afra, Vameq and Ozra, Metiochus and Parthenope, and Vis and Ramin.
Question: Where is Cabernet Sauvignon grown?
Evidence: Cabernet Sauvignon (French: [kabɛʁnɛ soviɲɔ̃]) is one of the world's most widely recognized red wine grape varieties. It is grown in nearly every major wine producing country among a diverse spectrum of climates from Australia and British Columbia, Canada to Lebanon's Beqaa Valley. Cabernet Sauvignon became internationally recognized through its prominence in Bordeaux wines, where it is often blended with Merlot and Cabernet Franc. From France and Spain, the grape spread across Europe and to the New World where it found new homes in places like California's Santa Cruz Mountains, Paso Robles, Napa Valley, New Zealand's Hawke's Bay, South Africa's Stellenbosch region, Australia's Margaret River, McLaren Vale and Coonawarra regions, and Chile's Maipo Valley and Colchagua. For most of the 20th century, it was the world's most widely planted premium red wine grape until it was surpassed by Merlot in the 1990s. However, by 2015, Cabernet Sauvignon had once again become the most widely planted wine grape, with a total of 341,000 hectares (3,410 km2) under vine worldwide.
Answer: France, Spain, California, New Zealand, South Africa, Australia, Chile, Canada
Question: What are two datasets models are tested on?
Evidence: In this paper, we try to fulfill the above insufficiency via an exploratory study on real-world semantic parsing in context. Concretely, we present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods. Our best model achieves state-of-the-art (SOTA) performances on both datasets with significant improvements. Furthermore, we summarize and generalize the most frequent contextual phenomena, with a fine-grained analysis on representative models. Through the analysis, we obtain some interesting findings, which may benefit the community on the potential research directions. We will open-source our code and materials to facilitate future work upon acceptance.
Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods.
Answer: The answers are shown as follows:
* SParC BIBREF2 and CoSQL BIBREF6
Question: Who is Robert Kiyosaki?
Answer: Robert Kiyosaki is an American entrepreneur and author who became famous primarily due to his book Rich Dad Poor Dad, a book which many refer to as "the bible of financial literacy". He has acquired much of his wealth through investing in real estate successfully and teaching others financial skills that are not taught in typical school systems.
Question: Identify which car manufacturer is Japanese or American: Lila, Lincoln
Answer: Lila is Japanese, Lincoln is American
Question: What kind of datasets are used to evaluate the authors' model?
Evidence: We evaluate our model on two publicly available datasets. The statistics for both are shown in Table TABREF3 . The details of these datasets are as follows:
OntoNotes: OntoNotes 5.0 BIBREF16 includes texts from five different text genres: broadcast conversation (200k), broadcast news (200k), magazine (120k), newswire (625k), and web data (300k). This dataset is annotated with 18 categories.
Wiki(gold): The training data consists of Wikipedia sentences and was automatically generated using a distant supervision method, mapping hyperlinks in Wikipedia articles to Freebase, which we do not use in this study. The test data, mainly consisting of sentences from news reports, was manually annotated as described in BIBREF8 . The class hierarchy is shown in Figure FIGREF2 . This dataset is annotated with 7 main categories (bold text in Figure FIGREF2 ), which maps directly to OntoNotes. The miscellaneous category in Figure FIGREF2 does not have direct mappings, so future work may include redefining these categories so the mappings are more meaningful.
We evaluate our model on two publicly available datasets.
Answer: Two publicly available datasets.
Question: How many BAE attack modes do the authors introduce?
Evidence: Recent studies have shown the vulnerability of ML models to adversarial attacks, small perturbations which lead to misclassification of inputs. Adversarial example generation in NLP BIBREF0 is more challenging than in common computer vision tasks BIBREF1, BIBREF2, BIBREF3 due to two main reasons: the discrete nature of input space and ensuring semantic coherence with the original sentence. A major bottleneck in applying gradient based BIBREF4 or generator model BIBREF5 based approaches to generate adversarial examples in NLP is the backward propagation of the perturbations from the continuous embedding space to the discrete token space.
Recent works for attacking text models rely on introducing errors at the character level in words BIBREF6, BIBREF7 or adding and deleting words BIBREF8, BIBREF9, BIBREF10, etc. for creating adversarial examples. These techniques often result in adversarial examples which are unnatural looking and lack grammatical correctness, and thus can be easily identified by humans.
TextFooler BIBREF11 is a black-box attack, that uses rule based synonym replacement from a fixed word embedding space to generate adversarial examples. These adversarial examples do not account for the overall semantics of the sentence, and consider only the token level similarity using word embeddings. This can lead to out-of-context and unnaturally complex replacements (see Table ), which can be easily identifiable by humans.
The recent advent of powerful language models BIBREF12, BIBREF13 in NLP has paved the way for using them in various downstream applications. In this paper, we present a simple yet novel technique: BAE (BERT-based Adversarial Examples), which uses a language model (LM) for token replacement to best fit the overall context. We perturb an input sentence by either replacing a token or inserting a new token in the sentence, by means of masking a part of the input and using a LM to fill in the mask (See Figure FIGREF1). BAE relies on the powerful BERT masked LM for ensuring grammatical correctness of the adversarial examples. Our attack beats the previous baselines by a large margin and confirms the inherent vulnerabilities of modern text classification models to adversarial attacks. Moreover, BAE produces more richer and natural looking adversarial examples as it uses the semantics learned by a LM.
To the best of our knowledge, we are the first to use a LM for adversarial example generation. We summarize our major contributions as follows:

We propose BAE, a novel strategy for generating natural looking adversarial examples using a masked language model.
We introduce 4 BAE attack modes, all of which are almost always stronger than previous baselines on 7 text classification datasets.
We show that, surprisingly, just a few replace/insert operations can reduce the accuracy of even a powerful BERT-based classifier by over $80\%$ on some datasets.

We introduce 4 BAE attack modes, all of which are almost always stronger than previous baselines on 7 text classification datasets.
Answer: 4.
Question: How to optimize the joint model by the authors?
Evidence: We treat document quality assessment as a classification problem, i.e., given a document, we predict its quality class (e.g., whether an academic paper should be accepted or rejected). The proposed model is a joint model that integrates visual features learned through Inception V3 with textual features learned through a biLSTM. In this section, we present the details of the visual and textual embeddings, and finally describe how we combine the two. We return to discuss hyper-parameter settings and the experimental configuration in the Experiments section.
We optimize our model based on cross-entropy loss.
Answer: They optimize the model based on cross-entropy loss.
Question: Does the latent dialogue state heklp their model?
Evidence: Recently, end-to-end approaches have trained recurrent neural networks (RNNs) directly on text transcripts of dialogs. A key benefit is that the RNN infers a latent representation of state, obviating the need for state labels. However, end-to-end methods lack a general mechanism for injecting domain knowledge and constraints. For example, simple operations like sorting a list of database results or updating a dictionary of entities can expressed in a few lines of software, yet may take thousands of dialogs to learn. Moreover, in some practical settings, programmed constraints are essential – for example, a banking dialog system would require that a user is logged in before they can retrieve account information.
A key benefit is that the RNN infers a latent representation of state, obviating the need for state labels.
Answer: Yes.
Question: What's the relation between the Kushan Empire and the Sassanid Empire in Iran?
Evidence: The Kushan Empire  was a syncretic empire, formed by the Yuezhi, in the Bactrian territories in the early 1st century. It spread to encompass much of modern-day territory of Uzbekistan, Afghanistan, Pakistan and Northern India. The Sasanian or Sassanid Empire officially known as Eranshahr  was the last Iranian empire before the early Muslim conquests of the 7th–8th centuries AD. Named after the House of Sasan, it endured for over four centuries, from 224 to 651 AD, making it the longest-lived Persian imperial dynasty.[
Answer: Kushano-Sasanian Kingdom (also called Kushanshahs,  or Koshano Shao in Bactrian, or Indo-Sasanians) is a historiographic term used by modern scholars to refer to a branch of the Sasanian Persians who established their rule in Bactria during the 3rd and 4th centuries CE at the expense of the declining Kushans. They captured the provinces of Sogdiana, Bactria and Gandhara from the Kushans in 225 CE. The Sasanians established governors for the Sasanian Empire, who minted their own coinage and took the title of Kushanshas, i.e. "Kings of the Kushans". They are sometimes considered as forming a "sub-kingdom" inside the Sassanid Empire.
Question: Write a story about getting stuck on a train
Answer: The subway suddenly stops. I groan in frustration. I have been waiting for months to go to this particular art exhibit that happens once a year and now I’m going to be late. I tap my foot out of impatience. Minutes pass by and the train becomes loud with angry passengers. 
          I slip away to the emptier part of the train. I need to call my friends stating I will be late. I search for my phone in my bag but I could not find it. I think I forgot it at home. I look around to see if I could borrow anyone's phone. I walk up to the person in the seat across from me.
                 “Excuse me is there any possible chance I could borrow your phone?” I question
                          “There's no use calling anyone down here. There’s no signal,” She responds
               “Thank you anyway,” I say and walk back to my seat in defeat. I look out the window to see that the conductor's part of the subway is gone. I start to panic, my mind looking at all the worst possible scenarios. Did anyone else notice? I sit in silence for a few minutes not knowing what will happen next.
                   “Where’s the conductor's car?” Someone yells out. The entire subway goes crazy. I continue to look out the window to block everyone out. I am lost in my daydreaming until I see a crack appear on the wall. I jump up. The crack begins to spread and spread. 
                     Water starts to pour out, suddenly I’m not the only one who noticed it anymore. Everyone is stunned. Clearly thinking of the same thing I’m thinking of. We are going to drown.
                   I watch in fear as the water is spreading more quickly and it soon covers the subway floor. I managed to hop on a chair in time but others weren't as lucky as me. 
                It begins to reach up to the chairs. The water seems to never stop pouring in. I then see another light, it’s firemen in a special train car. I am close to the exit door so I was one of the first ones evacuated.
Question: What are the differences with previous applications of neural networks for this task?
Evidence: One common point in all the approaches yet has been the use of only textual features available in the dataset. Our model not only incorporates textual features, modeled using BiLSTM and augmented with an attention mechanism, but also considers related images for the task.
One common point in all the approaches yet has been the use of only textual features available in the dataset. Our model not only incorporates textual features, modeled using BiLSTM and augmented with an attention mechanism, but also considers related images for the task.
Answer: This approach considers related images
Question: How many documents do the authors provide to invest the impact of more training data?
Evidence: In this work, we propose a new evaluation framework for highly multilingual document classification which significantly extends the current state. We continue to use Reuters Corpus Volume 2, but based on the above mentioned limitations of the current subset of RCV2, we propose new tasks for cross-lingual document classification. The design choices are as follow:
Uniform class coverage: we sample from RCV2 the same number of examples for each class and language;
Split the data into train, development and test corpus: for each languages, we provide training data of different sizes (1k, 2k, 5k and 10k stories), a development (1k) and a test corpus (4k);
Support more languages: German (DE), English (EN), Spanish (ES), French (FR), Italian (IT), Japanese (JA), Russian (RU) and Chinese (ZH). Reference baseline results are available for all languages.
Most works in the literature use only 1 000 examples to train the document classifier. To invest the impact of more training data, we also provide training corpora of 2 000, 5 000 and 10 000 documents. The development corpus for each language is composed of 1 000 and the test set of 4 000 documents respectively. All have uniform class distributions. An important aspect of this work is to provide a framework to study and evaluate cross-lingual document classification for many language pairs. In that spirit, we will name this corpus “Multilingual Document Classification Corpus”, abbreviated as MLDoc. The full Reuters Corpus Volume 2 has a special license and we can not distribute it ourselves. Instead, we provide tools to extract all the subsets of MLDoc at https://github.com/facebookresearch/MLDoc.
Most works in the literature use only 1 000 examples to train the document classifier. To invest the impact of more training data, we also provide training corpora of 2 000, 5 000 and 10 000 documents.
Answer: The authors also provide training corpora of 2 000, 5 000 and 10 000 documents.
Question: Given this paragraph about blockchain, how did blockchain influence bitcoin cryptocurrency?
Evidence: Blockchains are typically managed by a peer-to-peer (P2P) computer network for use as a public distributed ledger, where nodes collectively adhere to a consensus algorithm protocol to add and validate new transaction blocks. Although blockchain records are not unalterable, since blockchain forks are possible, blockchains may be considered secure by design and exemplify a distributed computing system with high Byzantine fault tolerance. A blockchain was created by a person (or group of people) using the name (or pseudonym) Satoshi Nakamoto in 2008 to serve as the public distributed ledger for bitcoin cryptocurrency transactions, based on previous work by Stuart Haber, W. Scott Stornetta, and Dave Bayer. The implementation of the blockchain within bitcoin made it the first digital currency to solve the double-spending problem without the need of a trusted authority or central server. The bitcoin design has inspired other applications and blockchains that are readable by the public and are widely used by cryptocurrencies. The blockchain may be considered a type of payment rail.
Answer: Blockchain is the technology that enables the existence of cryptocurrency and bitcoin is the name of the best-known cryptocurrency. The implementation of bitcoin using blockchain made it the first digital currency to solve the double-spending problem without the need of a trusted authority or central server. Bitcoin has inspired other applications and blockchains that are highly available, public, and are widely used by cryptocurrencies.
Question: What kind of species is the Epophthalmia
Answer: Epophthalmia a genus of dragonflies in the family Macromiidae.