DataHammer/scimrc huggingface dataset:
Question: Over which datasets/corpora is this work evaluated?
Evidence: We conduct our experiments on $\sim $ 8.7M annotated anonymised user utterances. They are annotated and derived from requests across 23 domains.
We conduct our experiments on $\sim $ 8.7M annotated anonymised user utterances. They are annotated and derived from requests across 23 domains.
Answer: The answers are shown as follows:
* on $\sim $ 8.7M annotated anonymised user utterances
Question: How do their train their embeddings?
Evidence: For each variable in encoding set, learn the new embeddings using the embeddings train set . This should be done simultaneously (all variable embeddings estimated at once, as explained in the next section).
Since there is stochasticity in the embeddings training model, we will repeat the above multiple times, for the different experiments in the paper (and report the respective mean and standard deviation statistics). Whenever we want to analyse a particular model (e.g. to check the coefficients of a choice model), we select the one with the highest likelihood at the development set (i.e. in practice, its out-of-sample generalization performance), and report its performance on the test set.
For each variable in encoding set, learn the new embeddings using the embeddings train set .
Since there is stochasticity in the embeddings training model, we will repeat the above multiple times, for the different experiments in the paper (and report the respective mean and standard deviation statistics).
Answer: The embeddings are learned several times using the training set, then the average is taken.
Question: What are the most common types of streets in the United States?
Answer: A street in the US is commonly referred to as an avenue, boulevard, lane, place, or street.
Question: what is a dog?
Answer: The dog is a domesticated descendant of the wolf. Dogs were the first species to be domesticated by hunter-gatherers over 15,000 years ago before the development of agriculture. They are super cute and are preferred over cats by a majority of people. They most commonly respond to "Good Boy" and "Good Girl" and love to nap and play.
Question: How many official languages are spoken in India?
Answer: Republic of India as a nation has over 122 major languages and 1599 other languages but the latest constitution records officially 22 languages. These are classified as scheduled languages which has recognition and status.
Question: Given this paragraph about Pittsford, New York, what year was the town settled?
Evidence: The Town of Pittsford (formerly part of the town of Northfield) was settled in 1789 and incorporated in 1796. The Village of Pittsford was incorporated in 1827. It was named by Colonel Caleb Hopkins, War of 1812 hero and subsequently Pittsford Town Supervisor, for the town of his birth, Pittsford, Vermont.
Answer: The town of Pittsford was settled in 1789.
Question: After returning home after the Boer War, where did Wade work as a clinical tutor?
Evidence: In 1900, in response to a national call for volunteer doctors to serve in the Boer War, Wade enlisted as a civilian surgeon and was posted to join the Royal Scots Fusiliers at the 1st General Hospital in Wynberg. He went on to serve at the 1st General Hospital, Pretoria, South Africa. He was awarded the Queen's Medal with four clasps. On his return home after two years, Wade became clinical tutor under Professor Francis Mitchell Caird, Professor of Clinical Surgery at Edinburgh University. At the same time, he was appointed as a demonstrator in anatomy and then pathology at the University of Edinburgh. In 1903, he was appointed museum conservator at the Royal College of Surgeons of Edinburgh and became a fellow of the college.
Answer: After returning home, Wade worked as a clinical tutor at Edinburgh University.
Question: Name the Hindi novel that has become the 1st novel to win the International Booker Prize
Answer: Tomb of Sand
Question: How do they measure grammaticality?
Evidence: Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase): DISPLAYFORM0
Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase):
Answer: by calculating log ratio of grammatical phrase over ungrammatical phrase
Question: What is positive cash flow?
Answer: Positive cash flow is when a business has more money coming in than going out. It is a measure of profitability and can be calculated by subtracting expenses from revenues.
Question: Given this paragraph about the first tour de france, who won the first race
Evidence: The first Tour de France was staged in 1903. The plan was a five-stage race from 31 May to 5 July, starting in Paris and stopping in Lyon, Marseille, Bordeaux, and Nantes before returning to Paris. Toulouse was added later to break the long haul across southern France from the Mediterranean to the Atlantic. Stages would go through the night and finish next afternoon, with rest days before riders set off again. But this proved too daunting and the costs too great for most and only 15 competitors had entered. Desgrange had never been wholly convinced and he came close to dropping the idea. Instead, he cut the length to 19 days, changed the dates to 1 to 19 July, and offered a daily allowance to those who averaged at least 20 kilometres per hour (12 mph) on all the stages, equivalent to what a rider would have expected to earn each day had he worked in a factory. He also cut the entry fee from 20 to 10 francs and set the first prize at 12,000 francs and the prize for each day's winner at 3,000 francs. The winner would thereby win six times what most workers earned in a year. That attracted between 60 and 80 entrants – the higher number may have included serious inquiries and some who dropped out – among them not just professionals but amateurs, some unemployed, and some simply adventurous.

Desgrange seems not to have forgotten the Dreyfus Affair that launched his race and raised the passions of his backers. He announced his new race on 1 July 1903 by citing the writer Émile Zola, whose open letter J'Accuse…! led to Dreyfus's acquittal, establishing the florid style he used henceforth.

The first Tour de France started almost outside the Café Reveil-Matin at the junction of the Melun and Corbeil roads in the village of Montgeron. It was waved away by the starter, Georges Abran, at 3:16 p.m. on 1 July 1903. L'Auto hadn't featured the race on its front page that morning.[n 3]

Among the competitors were the eventual winner, Maurice Garin, his well-built rival Hippolyte Aucouturier, the German favourite Josef Fischer, and a collection of adventurers, including one competing as "Samson".[n 4]

Many riders dropped out of the race after completing the initial stages, as the physical effort the tour required was just too much. Only a mere 24 entrants remained at the end of the fourth stage. The race finished on the edge of Paris at Ville d'Avray, outside the Restaurant du Père Auto, before a ceremonial ride into Paris and several laps of the Parc des Princes. Garin dominated the race, winning the first and last two stages, at 25.68 kilometres per hour (15.96 mph). The last rider, Millocheau, finished 64h 47m 22s behind him.

L'Auto's mission was accomplished, as circulation of the publication doubled throughout the race, making the race something much larger than Desgrange had ever hoped for.
Answer: The first race in 1903 was won by Maurice Garin, over his rival Hippolyte Aucouturier
Question: What are the benefits of building a large language model specific to your organization rather than using a third party vendor's model?
Answer: Building your own large language model (LLM) reduces security risks from data leakage, which could allow others to access your proprietary information. Building an in-house LLM also creates value internal to your organization rather than delivering that value (in the form of your data) to a third party. The answers from a custom LLM will also be more valid and specific to your organization. Finally, building your own LLM can be more cost effective than paying for each API request to a third party solution.
Question: Which author wrote the ‘Winnie-the-Pooh’ books?
Answer: A. A. Milne
Question: Why 2 is greater than 3?
Answer: This is a trick question. 2 is not greater than 3. As a matter of fact, 3 is greater than 2. That being said, you could potentially define a mathematical space in which 2 is greater than 3. However, in real life we refer to real number domain in which 3 is greater than 2.
Question: What is the domain of their collected corpus?
Evidence: We collect utterances from the $\mathbf {C}$hinese $\mathbf {A}$rtificial $\mathbf {I}$ntelligence $\mathbf {S}$peakers (CAIS), and annotate them with slot tags and intent labels. The training, validation and test sets are split by the distribution of intents, where detailed statistics are provided in the supplementary material. Since the utterances are collected from speaker systems in the real world, intent labels are partial to the PlayMusic option. We adopt the BIOES tagging scheme for slots instead of the BIO2 used in the ATIS, since previous studies have highlighted meaningful improvements with this scheme BIBREF30 in the sequence labeling field.
We collect utterances from the $\mathbf {C}$hinese $\mathbf {A}$rtificial $\mathbf {I}$ntelligence $\mathbf {S}$peakers (CAIS), and annotate them with slot tags and intent labels. The training, validation and test sets are split by the distribution of intents, where detailed statistics are provided in the supplementary material. Since the utterances are collected from speaker systems in the real world, intent labels are partial to the PlayMusic option. We adopt the BIOES tagging scheme for slots instead of the BIO2 used in the ATIS, since previous studies have highlighted meaningful improvements with this scheme BIBREF30 in the sequence labeling field.
Answer: The answers are shown as follows:
* speaker systems in the real world
Question: As a second baseline, how does the two-channel CADec model used by the authors work?
Evidence: The baseline model, the model used for back-translation, and the DocRepair model are all Transformer base models BIBREF15. More precisely, the number of layers is $N=6$ with $h = 8$ parallel attention layers, or heads. The dimensionality of input and output is $d_{model} = 512$, and the inner-layer of a feed-forward networks has dimensionality $d_{ff}=2048$. We use regularization as described in BIBREF15.
As a second baseline, we use the two-pass CADec model BIBREF11. The first pass produces sentence-level translations. The second pass takes both the first-pass translation and representations of the context sentences as input and returns contextualized translations. CADec requires document-level parallel training data, while DocRepair only needs monolingual training data.
As a second baseline, we use the two-pass CADec model. The first pass produces sentence-level translations. The second pass takes both the first-pass translation and representations of the context sentences as input and returns contextualized translations.
Answer: The first pass produces sentence-level translations. The second pass takes both the first-pass translation and representations of the context sentences as input and returns contextualized translations.
Question: What are some of the most common vegetables in the broccoli family?
Answer: The broccoli family includes many cruciferous vegetables like cabbage, bok choi, cauliflower, collard greens, brussel sprouts, and arugula.
Question: Which datasets were used in the experiment?
Evidence: Contribution: We propose a preliminary study of a generic approach allowing any model to benefit from document-level information while translating sentence pairs. The core idea is to augment source data by adding document information to each sentence of a source corpus. This document information corresponds to the belonging document of a sentence and is computed prior to training, it takes every document word into account. Our approach focuses on pre-processing and consider whole documents as long as they have defined boundaries. We conduct experiments using the Transformer base model BIBREF1. For the English-German language pair we use the full WMT 2019 parallel dataset. For the English-French language pair we use a restricted dataset containing the full TED corpus from MUST-C BIBREF10 and sampled sentences from WMT 2019 dataset. We obtain important improvements over the baseline and present evidences that this approach helps to resolve cross-sentence ambiguities.
For the English-German language pair we use the full WMT 2019 parallel dataset. For the English-French language pair we use a restricted dataset containing the full TED corpus from MUST-C BIBREF10 and sampled sentences from WMT 2019 dataset. 
Answer: The answers are shown as follows:
* WMT 2019 parallel dataset
* a restricted dataset containing the full TED corpus from MUST-C BIBREF10
* sampled sentences from WMT 2019 dataset
Question: In the series A Song of Ice and Fire, who is the founder of House Bar Emmon?
Answer: Togarion Bar Emmon, also known as Togarion the Terrible
Question: How to modify the method of Universal Language Model Fine-tuning for Text Classification (ULMFiT)?
Evidence: We modify the method of Universal Langage Model Fine-tuning for Text Classification (ULMFiT) presented in Howard and Ruder BIBREF3 . The primary steps are:
Below we will give more detail on each step and the parameters used to generate our system.
The primary steps are: 1. Train a language model (LM) on a large corpus of data 2. Fine-tune the LM based on the target task language data 3. Replace the final layer of the LM with a softmax or linear output layer and then fine-tune on the particular task at hand (classification or regression).
Answer: The primary steps are: 1. Train a language model (LM) on a large corpus of data 2. Fine-tune the LM based on the target task language data 3. Replace the final layer of the LM with a softmax or linear output layer and then fine-tune on the particular task at hand (classification or regression).
Question: Can someone drive on Black Bear Road during the winter?
Evidence: Black Bear Road is open a few months of the year, from late summer (usually the last week of July) to early fall. The road is traveled only downhill from Red Mountain Pass — except for the annual Jeeper's Jamboree in which travel is reversed for one day only. The start of the trail was formerly marked along U.S. 550 with a sign that read:
Answer: No, it is only open from late summer to early fall.
Question: Name three beaches in Pinellas County, Florida
Answer: Three beaches in Pinellas County, Florida are Clearwater Beach, Indian Rocks Beach, and St. Pete Beach.
Question: What is the Baur au Lac
Evidence: Baur au Lac is a luxury hotel at Talstrasse, Zürich, Switzerland.

The hotel was founded in 1844 by Johannes Baur, and is still owned by the Baur family. It was completely renovated between 2008 and 2010. It is a member of the Leading Hotels of the World marketing organisation.
Answer: The Baur au Lac is a luxury hotel at Talstrasse, Zurich, Switzerland.

The hotel was founded in 1844 by Johannes Baur, and is still owned by the Baur family. It was completely renovated between 2008 and 2010. It is a member of the Leading Hotels of the World marketing organisation.

It is beautifully located, directly at the wonderful lake of Zurich in Switzerland
Question: Compare middle eastern music and western music
Answer: If you have only listened to Western music, the first time you hear o a middle eastern melody it may sound very different and odd. Melodies in middle eastern music are generally long, lack harmony, and have unfamiliar pitches.

Middle Eastern music typically sounds monophonic, meaning it's solely based on one or more instruments that play a single melody with a different variation. Western music typically sounds polyphonic, meaning more than one layer of melody is being played simultaneously.

Western music has twelve notes per octave including seven whole notes and five half-notes. However middle eastern music is based on different scale systems. It has quarter notes in addition and various regions have a different number of notes in their octave system.

Western music has harmony (ie. chords) while middle eastern music does not. In middle eastern music, there is only one melodic line with rise and fall. So the purity of the melodic line and the complexity of the rhythm is what make two different songs apart. In contrast, western music has a structure with a beginning, middle, and end.
Question: Write a tweet from the perspective of a dog that’s angry about the squirrels in its yard
Answer: GET OFF MY LAWN SQUIRRELS!!! I’ve marked this place a thousand times, it’s not yours #boundaries
Question: Identify which instrument is string or percussion: Tsuzumi, Auto-harp
Answer: Auto-harp is string, Tsuzumi is percussion.
Question: Rank American Presidents
Answer: Abraham Lincoln is mostly regarded as the greatest president for his leadership during the Civil War and the abolition of slavery. Franklin D. Roosevelt for leading the country out of the Great Depression and during World War II. Founding Father and first president George Washington for setting several enduring and important precedents for the office of the presidency, including the peaceful transition of power.
Question: Did they compare against other systems?
Evidence: The slot extraction and intent keywords extraction results are given in Table TABREF1 and Table TABREF2 , respectively. Table TABREF3 summarizes the results of various approaches we investigated for utterance-level intent understanding. Table TABREF4 shows the intent-wise detection results for our AMIE scenarios with the best performing utterance-level intent recognizer.
FLOAT SELECTED: Table 3: Utterance-level Intent Recognition Results (10-fold CV)
The slot extraction and intent keywords extraction results are given in Table TABREF1 and Table TABREF2 , respectively. Table TABREF3 summarizes the results of various approaches we investigated for utterance-level intent understanding. Table TABREF4 shows the intent-wise detection results for our AMIE scenarios with the best performing utterance-level intent recognizer.
FLOAT SELECTED: Table 3: Utterance-level Intent Recognition Results (10-fold CV)
Answer: Yes.
Question: How to implement EGL in this paper?
Evidence: We empirically validate EGL on speech recognition tasks. In our experiments, the RNN takes in spectrograms of utterances, passing them through two 2D-convolutional layers, followed by seven bi-directional recurrent layers and a fully-connected layer with softmax activation. All recurrent layers are batch normalized. At each timestep, the softmax activations give a probability distribution over the characters. CTC loss BIBREF8 is then computed from the timestep-wise probabilities.
A base model, INLINEFORM0 , is trained on 190 hours ( INLINEFORM1 100K instances) of transcribed speech data. Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset. We query labels for the selected subset and incorporate them into training. Learning rates are tuned on a small validation set of 2048 instances. The trained model is then tested on a 156-hour ( INLINEFORM3 100K instances) test set and we report CTC loss, Character Error Rate (CER) and Word Error Rate (WER).
The confidence score methods BIBREF1 , BIBREF2 can be easily extended to our setup. Specifically, from the probabilities over the characters, we can compute an entropy per timestep and then average them. This method is denoted as entropy. We could also take the most likely prediction and calculate its CTC loss, normalized by number of timesteps. This method is denoted as pCTC (predicted CTC) in the following sections.
We implement EGL by marginalizing over the most likely 100 labels, and compare it with: 1) a random selection baseline, 2) entropy, and 3) pCTC. Using the same base model, each method queries a variable percentage of the unlabeled dataset. The queries are then included into training set, and the model continues training until convergence. Fig. FIGREF9 reports the metrics (Exact values are reported in Table TABREF12 in the Appendix) on the test set as the query percentage varies. All the active learning methods outperform the random baseline. Moreover, EGL shows a steeper, more rapid reduction in error than all other approaches. Specifically, when querying 20% of the unlabeled dataset, EGL has 11.58% lower CER and 11.09% lower WER relative to random. The performance of EGL at querying 20% is on par with random at 40%, suggesting that using EGL can lead to an approximate 50% decrease in data labeling.
We implement EGL by marginalizing over the most likely 100 labels, and compare it with: 1) a random selection baseline, 2) entropy, and 3) pCTC.
Answer: The authors implement EGL by marginalizing over the most likely 100 labels, and compare it with: 1) a random selection baseline, 2) entropy, and 3) pCTC.
Question: Are spicy foods healthy?
Answer: Eating spicy foods has several health benefits, as various studies had shown that spicy foods can help boosting metabolism, lowering mortality rate,  decreasing  blood pressure, and even fighting cancer cells. However, overeating spicy food or eating super spicy food could lead to side effects, such as acne and stomach pain, to some people.
Question: Please extract the characteristics of a desert from this passage and list in bullet format.
Evidence: A desert is a region of land that is very dry because it receives low amounts of precipitation (usually in the form of rain, but it may be snow, mist or fog), often has little coverage by plants, and in which streams dry up unless they are supplied by water from outside the area. Deserts generally receive less than 250 mm (10 in) of precipitation each year. The potential evapotranspiration may be large but (in the absence of available water) the actual evapotranspiration may be close to zero. Semi-deserts are regions which receive between 250 and 500 mm (10 and 20 in) and when clad in grass, these are known as steppes.
Answer: -Very dry 
-Little coverage by plants
Question: How many CNNs and LSTMs were ensembled?
Evidence: To reduce variance and boost accuracy, we ensemble 10 CNNs and 10 LSTMs together through soft voting. The models ensembled have different random weight initializations, different number of epochs (from 4 to 20 in total), different set of filter sizes (either INLINEFORM0 , INLINEFORM1 or INLINEFORM2 ) and different embedding pre-training algorithms (either Word2vec or FastText).
To reduce variance and boost accuracy, we ensemble 10 CNNs and 10 LSTMs together through soft voting. The models ensembled have different random weight initializations, different number of epochs (from 4 to 20 in total), different set of filter sizes (either INLINEFORM0 , INLINEFORM1 or INLINEFORM2 ) and different embedding pre-training algorithms (either Word2vec or FastText).
Answer: The answers are shown as follows:
* 10 CNNs and 10 LSTMs