DataHammer/scimrc huggingface dataset:
Question: From the passage provided, extract the official language of Andhra Pradesh
Evidence: Andhra Pradesh (English: /ˌɑːndrə prəˈdɛʃ/, Telugu: [ãːndʱrɐ prɐdeːʃ] (listen) abbr. AP) is a state in the south-eastern coastal region of India. It is the seventh-largest state by area covering an area of 162,975 km2 (62,925 sq mi) and tenth-most-populous state, with 49,386,799 inhabitants. It is bordered by Telangana to the north-west, Chhattisgarh to the north, Odisha to the north-east, Tamil Nadu to the south, Karnataka to the west and the Bay of Bengal to the east. It has the second longest coastline in India after Gujarat, of about 974 km (605 mi). Andhra State was the first state to be formed on a linguistic basis in India on 1 October 1953. On 1 November 1956, Andhra State was merged with the Telugu-speaking areas (ten districts) of the Hyderabad State to form United Andhra Pradesh. In 2014, these merged areas of Hyderabad State were bifurcated from United Andhra Pradesh to form the new state Telangana. The present form of Andhra is similar to Andhra state, but some mandalas like Bhadrachalam are still included in Telangana. Amaravati serves as the capital of the state with the largest city being Visakhapatnam.

Andhra Pradesh was once a major Buddhist pilgrimage site in the country and a Buddhist learning center which can be seen in many sites in the state in the form of monastery ruins, chaityas, and stupas. It is also known for being the land of Koh-i-Noor and other globally known diamonds from Kollur Mine. It is also a major producer of rice known as the "Rice bowl of India". Its official language is Telugu; one of the classical languages of India, the fourth most spoken language in India, and the 13th-most spoken language in the world. Andhra Pradesh's second official language is Urdu.
Answer: Telugu is the official language of Andhra Pradesh
Question: Who won the World Chess Championship in 2021?
Answer: Magnus Carlsen defeated Ian Nepomniachtchi 7.5 - 3.5 to become the 2021 World Chess Champion.
Question: What is the performance of EGL?
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.
Moreover, EGL shows a steeper, more rapid reduction in error than all other approaches.
Answer: EGL shows a steeper, more rapid reduction in error than all other approaches.
Question: What is Lorraine
Evidence: Lorraine[Note 1] is a cultural and historical region in Northeastern France, now located in the administrative region of Grand Est. Its name stems from the medieval kingdom of Lotharingia, which in turn was named after either Emperor Lothair I or King Lothair II. Lorraine later was ruled as the Duchy of Lorraine before the Kingdom of France annexed it in 1766.

From 1982 until January 2016, Lorraine was an administrative region of France. In 2016, under a reorganisation, it became part of the new region Grand Est. As a region in modern France, Lorraine consisted of the four departments Meurthe-et-Moselle, Meuse, Moselle and Vosges (from a historical point of view the Haute-Marne department is located in the region), containing 2,337 communes. Metz is the regional prefecture. The largest metropolitan area of Lorraine is Nancy, which had developed for centuries as the seat of the duchy.

Lorraine borders Germany, Belgium, and Luxembourg. Its inhabitants are called Lorrains and Lorraines in French and number about 2,356,000.
Answer: Lorraine is a region located in Northeaster France, called Grand Est. It is consisted of the four departments Meuthe-et-Moselle, Meuse, Moselle and Vosges, containing 2,337 communes. Metz is the regional prefecture, and the largest metropolitan area of Lorraine is Nancy. 
The region Lorraine borders Germany, Belgium and Luxembourg. Its inhabitants are called Lorrains and Lorraines in French and number about 2,356,000.

Lorraine is also a common name given to female individuals, in France but also in english english speaking countries such as Canada, United States or United Kingdom.
Question: Where are do the Los Angeles Lakers play their home games?
Answer: The Los Angeles Lakers play their home games at the Staples Center.
Question: Do they predict the sentiment of the review summary?
Evidence: To address this issue, we further investigate a joint encoder for review and summary, which is demonstrated in Figure FIGREF4. The model works by jointly encoding the review and the summary in a multi-layer structure, incrementally updating the representation of the review by consulting the summary representation at each layer. As shown in Figure FIGREF5, our model consists of a summary encoder, a hierarchically-refined review encoder and an output layer. The review encoder is composed of multiple attention layers, each consisting of a sequence encoding layer and an attention inference layer. Summary information is integrated into the representation of the review content at each attention layer, thus, a more abstract review representation is learned in subsequent layers based on a lower-layer representation. This mechanism allows the summary to better guide the representation of the review in a bottom-up manner for improved sentiment classification.
FLOAT SELECTED: Figure 2: Three model structures for incorporating summary into sentiment classification
To address this issue, we further investigate a joint encoder for review and summary, which is demonstrated in Figure FIGREF4. The model works by jointly encoding the review and the summary in a multi-layer structure, incrementally updating the representation of the review by consulting the summary representation at each layer.
FLOAT SELECTED: Figure 2: Three model structures for incorporating summary into sentiment classification
Answer: No.
Question: What is a commonly used evaluation metric for language models?
Evidence: Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 .
recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 .
Answer: The answers are shown as follows:
* perplexity
Question: How was lexical diversity measured?
Evidence: To study the lexical and semantic diversities of responses, we performed three analyses. First, we aggregated all worker responses to a particular question into a single list corresponding to that question. Across all questions, we found that the number of unique responses was higher for the AUI than for the Control (Fig. FIGREF19 A), implying higher diversity for AUI than for Control.
Second, we compared the diversity of individual responses between Control and AUI for each question. To measure diversity for a question, we computed the number of responses divided by the number of unique responses to that question. We call this the response density. A set of responses has a response density of 1 when every response is unique but when every response is the same, the response density is equal to the number of responses. Across the ten questions, response density was significantly lower for AUI than for Control (Wilcoxon signed rank test paired on questions: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ) (Fig. FIGREF19 B).
To study the lexical and semantic diversities of responses, we performed three analyses. First, we aggregated all worker responses to a particular question into a single list corresponding to that question.
Second, we compared the diversity of individual responses between Control and AUI for each question. To measure diversity for a question, we computed the number of responses divided by the number of unique responses to that question. We call this the response density. 
Answer: By computing number of unique responses and number of responses divided by the number of unique responses to that question for each of the questions
Question: Classify each of the following as either birds or mammals: Eagle, Bear, Deer, Finch, Owl, Cat, Dog, Rat, Human, Whale, Raven
Answer: From the list that you provided: Eagle, Finch, Owl, Raven are birds. 

The rest of the list:  Bear, Deer, Cat, Dog, Rat, Human, and Whal are all mammals.
Question: Give me a list of classic English poems with nice message
Answer: 1. "If" by Rudyard Kipling: This poem is highlighting the importance to lead a life with integrity and gaining a clearer perspective about life.
2. "Requiescat" by Oscar Wilde: There is a lot of grief, loss and mourning. Poet dedicated to his younger sister who unfortunately died unexpectedly.
3. "And Still I Rise" by Maya Angelou: This poem is all about hope, courage and undying attitude to move forward in life by winning over all the challenges that life or society throws at you.
4. "Do not go gentle into that good night" by Dylan Thomas: One of the most known piece of the Welsh poet. In short, this poem is a gentle reminder about preciousness of life.
Question: What is a Maiestas trifasciatus
Evidence: Maiestas trifasciatus (formerly Recilia trifasciatus) is a species of bug from the Cicadellidae family that can be found on Canary Islands and countries including Iran and Burkina Faso. It was formerly placed within Recilia, but a 2009 revision moved it to Maiestas.
Answer: Maiestas trifasciatus (formerly Recilia trifasciatus) is a species of bug from the Cicadellidae family that can be found on Canary Islands and countries including Iran and Burkina Faso.

It was formerly placed within Recilia, but a 2009 revision moved it to Maiestas.
Question: Given a reference text about Konstantin Konstantinovich Sluchevsky, tell me where he was born and where he studied.
Evidence: Konstantin Konstantinovich Sluchevsky (Russian: Константин Константинович Случевский), (1837–1904) was a Russian poet.

Sluchevsky was born in St. Peterburg into a Russian noble family. He graduated from the First Cadet Corps, served in the Imperial Russian Guard, then entered the Academy of the General Staff, but in 1861 he quit the military service and went abroad. For a number of years he studied in Paris, Berlin, and Heidelberg, where he got the degree of Doctor of Philosophy in 1865. On his return to Russia he worked for the Ministry of Internal Affairs and State Property. From 1891 to 1902 he was the Chief Editor of the journal Pravitelstvenny Vestnik (Government Bulletin). He was also a member of the Council of the Chief Printing Office and had a title of Hofmeister.
Answer: Konstantin Konstantinovich Sluchevsky was born in St. Petersburg, Russia and studied in Paris, Berlin, and Heidelberg.
Question: What approach do the authors propose in the paper?
Evidence: Question answering (QA) has drawn a lot of attention in the past few years. QA tasks on images BIBREF0 have been widely studied, but most focused on understanding text documents BIBREF1 . A representative dataset in text QA is SQuAD BIBREF1 , in which several end-to-end neural models have accomplished promising performance BIBREF2 . Although there is a significant progress in machine comprehension (MC) on text documents, MC on spoken content is a much less investigated field. In spoken question answering (SQA), after transcribing spoken content into text by automatic speech recognition (ASR), typical approaches use information retrieval (IR) techniques BIBREF3 to find the proper answer from the ASR hypotheses. One attempt towards QA of spoken content is TOEFL listening comprehension by machine BIBREF4 . TOEFL is an English examination that tests the knowledge and skills of academic English for English learners whose native languages are not English. Another SQA corpus is Spoken-SQuAD BIBREF5 , which is automatically generated from SQuAD dataset through Google Text-to-Speech (TTS) system. Recently ODSQA, a SQA corpus recorded by real speakers, is released BIBREF6 .
To mitigate the impact of speech recognition errors, using sub-word units is a popular approach for speech-related downstream tasks. It has been applied to spoken document retrieval BIBREF7 and spoken term detection BIBREF8 The prior work showed that, using phonectic sub-word units brought improvements for both Spoken-SQuAD and ODSQA BIBREF5 .
Instead of considering sub-word features, this paper proposes a novel approach to mitigate the impact of ASR errors. We consider reference transcriptions and ASR hypotheses as two domains, and adapt the source domain data (reference transcriptions) to the target domain data (ASR hypotheses) by projecting these two domains in the shared common space. Therefore, it can effectively benefit the SQA model by improving the robustness to ASR errors in the SQA model.
Domain adaptation has been successfully applied on computer vision BIBREF9 and speech recognition BIBREF10 . It is also widely studied on NLP tasks such as sequence tagging and parsing BIBREF11 , BIBREF12 , BIBREF13 . Recently, adversarial domain adaptation has already been explored on spoken language understanding (SLU). Liu and Lane learned domain-general features to benefit from multiple dialogue datasets BIBREF14 ; Zhu et al. learned to transfer the model from the transcripts side to the ASR hypotheses side BIBREF15 ; Lan et al. constructed a shared space for slot tagging and language model BIBREF16 . This paper extends the capability of adversarial domain adaptation for SQA, which has not been explored yet.
Instead of considering sub-word features, this paper proposes a novel approach to mitigate the impact of ASR errors.
Answer: A novel approach to mitigate the impact of ASR errors.
Question: Do they use dropout?
Evidence: Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign). Some examples from the dataset are shown in Table TABREF4 . We use a LSTM based classifier model for training our model to classify these tweets into these three categories. An overview of the model is given in the Figure FIGREF12 . The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. Categorical crossentropy loss was used for the last layer due to the presence of multiple classes. We use Adam optimizer along with L2 regularisation to prevent overfitting. As indicated by the Figure FIGREF12 , the model was initially trained on the dataset provided by BIBREF1 , and then re-trained on the HEOT dataset so as to benefit from the transfer of learned features in the last stage. The model hyperparameters were experimentally selected by trying out a large number of combinations through grid search.
The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. 
Answer: Yes.
Question: Why does it get hotter in the summer?
Answer: During summer, the northern hemisphere tilts towards the Sun. As a result, the Sun shines longer in the summer, and the longer it shines, the hotter it gets. At this time southern hemisphere has winter. Then things swap six months later when the bottom half is closer to the Sun.
Question: What is the difference between oxfords and derbys?
Answer: Oxfords traditionally have closed lacing and are seen as more formal. Derbys on the other hand have an open lacing system and are thus more informal
Question: How is the dataset created?
Evidence: We collected Japanese fictional stories from the Web to construct the dataset. The dataset contains approximately 30M utterances of fictional characters. We separated the data into a 99%–1% split for training and testing. In Japanese, the function words at the end of the sentence often exhibit style (e.g., desu+wa, desu+ze;) therefore, we used an existing lexicon of multi-word functional expressions BIBREF14 . Overall, the vocabulary size $\vert \mathcal {V} \vert $ was 100K.
We collected Japanese fictional stories from the Web to construct the dataset.
Answer: The answers are shown as follows:
* We collected Japanese fictional stories from the Web
Question: How do I make Aioli?
Answer: First, blend combine 1 egg plus 1 egg yolk, two grated garlic cloves, juice from half a lemon, a tablespoon of dijon mustard, a small pinch of saffron, a pinch of salt and pinch of ground black pepper.

Then, mix 1/4 cup of olive oil and 1 cup of grapeseed oil together then very slowly stream in the oils whilst continuously whisking hard. If you add the oil in too quickly, it will not emulsify so do it slowly. Once it’s all incorporated, you’ll be rewarded with thick, creamy aioli!

Finally, taste it and if it needs further acid, add a teaspoon of white wine vinegar. 

Enjoy!
Question: What is man's best friend?
Answer: Man's best friends usually refers to a dog or it is a way to refer to a domestic dog.  This phrase refers to many traits that dogs exhibit such as: loyalty, companionship, and friendship.  They never stop loving, and do not have an ability to feel hate or show hate as people interpret it.  Any behavior that looks opposite of "Man's Best Friend" could be as a result of neglect or animal abuse.
Question: List down some important facts about Indigo airlines
Evidence: InterGlobe Aviation Limited, doing business as IndiGo, is an Indian low-cost airline headquartered in Gurgaon, Haryana, India. It is the largest airline in India by passengers carried and fleet size, with a ~57% domestic market share as of October 2022.[non-primary source needed] It is also the largest individual Asian low-cost carrier in terms of jet fleet size and passengers carried, and the fourth largest carrier in Asia. The airline has carried over 300+ million passengers as of November 2022.
Answer: 1.	InterGlobe Aviation Limited is doing the business as Indigo in India.
2.	Indigo is a low-cost Indian airline, whose headquarter is in Gurgaon, Haryana, India.
3.	It is the largest airline in India with a ~57% domestic market share as of October 2022.
4.	It has the record for largest individual Asian low-cost carrier in terms of jet fleet size and passengers carried, and it is also the fourth largest carrier in Asia.
5.	As of November 2022 Indigo airline has carried over 300+ million passengers.
Question: How do I improve my leadership skills?
Answer: Leadership is a journey and improves with practice over a long period of time. The key ingredients to improve leadership starts with self awareness and practicing empathy. 

One of the best way to develop your leadership skills is to observe and absorb from other good leaders. A leader should also regularly solicit honest feedback from people in their organization, preferably a 360 degree review. This feedback should be the starting point and a good guide to improving your leadership skills. New ideas to add and improve your leadership can be done through a constant study of articles, books and learning from leadership forums.

Just like any other skill, leadership should be continuously developed through reading, learning, feedback and observing.
Question: Given a reference text about David Moses ben Meir Dyte, tell me what he is known for.
Evidence: David Moses ben Meir Dyte (c. 1770–1830) was an English Jewish quill merchant who distinguished himself by preventing the assassination attempt on George III by James Hadfield on 15 May 1800.

Dyte was attending Colley Cibber's play She Would and She Would Not at the Theatre Royal, Drury Lane when Hadfield fired a horsepistol at the King. Dyte, seated directly behind the shooter, struck the assailant and deflected the shot. It is said that Dyte asked as his sole reward the right to sell opera tickets, then a monopoly at the royal disposal. In 1802 he was appointed 'Purveyor of Pens and Quills to the Royal Household'. The incident was immortalised in the play Jew Dyte by Harold Rubinstein.

Dyte was the father of Henry Dyte, who served as Honorary Secretary to the Blind Society; and the grandfather of D. H. Dyte, Surgeon to the Jewish Board of Guardians, and Charles Dyte, a parliamentarian in the colony of Victoria.
Answer: David Moses ben Meir Dyte is known for preventing an assassination attempt by James Hadfield on George III.
Question: How many times Fernando Alonso won the F1 championship ?
Answer: 2 times
Question: Classify the below natural satellites based on the whether they are moon of Mars or Jupiter. 
Phobos, Deimos, Europa, Ganymede
Answer: Mars - Phobos, Deimos
Jupiter - Europa, Ganymede
Question: How do you thicken bolognese sauce?
Answer: You can use flour or cornstarch to thicken your bolognese or tomato-based spaghetti sauce using starch (flour or cornstarch). Additionally, you can reduce your sauce's liquid content — this is also a practical option for thickening any sauce.
Question: Identify which instrument is string or percussion: Chime bar, Pinaka vina
Answer: Pinaka vina is string, Chime bar is percussion.