DataHammer/scimrc huggingface dataset:
Question: List the principles that are used in chemical engineering where each new line starts with "- ". Do not say anything else.
Evidence: Chemical engineering is an engineering field which deals with the study of operation and design of chemical plants as well as methods of improving production. Chemical engineers develop economical commercial processes to convert raw materials into useful products. Chemical engineering uses principles of chemistry, physics, mathematics, biology, and economics to efficiently use, produce, design, transport and transform energy and materials. The work of chemical engineers can range from the utilization of nanotechnology and nanomaterials in the laboratory to large-scale industrial processes that convert chemicals, raw materials, living cells, microorganisms, and energy into useful forms and products. Chemical engineers are involved in many aspects of plant design and operation, including safety and hazard assessments, process design and analysis, modeling, control engineering, chemical reaction engineering, nuclear engineering, biological engineering, construction specification, and operating instructions.
Answer: - chemistry
- physics
- mathematics
- biology
- economics
Question: Can you please describe the game of golf?
Answer: Golf originated in Scotland in the 15th century. It is mostly regarded as an "upper-class" sport reserved for members of private country clubs, but has gained a lot of popularity over the last few decades. As a result, golf has become more broadly accessible to the general public, and today the sport is enjoyed by tens of millions of people around the world. 

In golf, there are a total of eighteen stretches of grass, known as holes. Each hole is comprised of a tee box, where the golfer hits his or her initial shot, the fairway, and the green. Most holes also include deep grass that runs along the fairways, known as "the rough". For added difficulty, holes can also contain hazards like sand bunkers or bodies of water like ponds and creeks. A small circular hole is located on every green and is marked with a flag so that golfers can see it from far away. The objective is to get your golf ball from the tee box into that small hole in as few shots (known as "strokes") as possible. Golfers advance their ball by striking it with a golf club, and each ball strike is counted as a stroke. Golfers are allowed to use up to 14 golf clubs over the course of their round, each of which is used for different purposes, from different distances. Some clubs, like the driver, are used to hit the ball very long distances. In fact, most professional golfers are able to drive the ball over 300 yards. That's the length of three football fields! A combination of clubs called irons make up the majority of a set of golf clubs. They are are generally used to advance the ball to the green after the golfer hits his or her initial tee shot. The club face of each iron is designed with a different loft angle. Higher loft angles enable golfers to hit the ball higher in the air and shorter distances, whereas low loft angles produce lower trajectory shots that travel longer distances. The most frequently used club in the set is the putter, which is used to tap the ball from wherever it lies on the green into the hole. On average, most good golfers will putt the ball twice per hole, or approximately 36 times per round. 

Golf holes are designated as par 3, par 4, or par 5, depending on their length. These numbers are an indication of how many strokes it should take for a professional golfer to complete the hole. Par 3 holes are shorter, and should take a single stroke to get the ball from tee to green. Par 5 holes are longer, and should typically take three strokes to get from tee to green. Most golf courses have four par 3 holes, four par 5 holes, and ten par 4 holes, yielding an aggregate of par 72. At the end of a round of golf, players calculate their final score by aggregating their total number of strokes across all eighteen holes. Over time, the golfer's skill level is assessed by his or her "handicap", which is a system that reflects the average number of strokes over par that the player typically shoots. For example, if one's average round score is 79, their handicap is 7 because it's 7 strokes above par. The lower the handicap, the better the golfer.
Question: How do you brew beer?
Answer: The steps to make beer is as following: 1. Mashing 2. Separation 3. Boiling 4. Fermentation. The ingredients are brought together through these 4 steps. You can create many beer styles with this process.
Question: What is a simple recipe for a margarita?
Answer: A simple margarita can be made using just tequila, lime juice, and simple syrup. Alternatively, margarita sweet and sour mix can be bought from the store and will need to be mixed with tequila.
Question: What datasets were used?
Evidence: JESSI is trained using only the datasets given on the shared task, without using any additional external data. Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%.
JESSI is trained using only the datasets given on the shared task, without using any additional external data.
Answer: The answers are shown as follows:
* datasets given on the shared task, without using any additional external data
Question: Who was the first computer programmer?
Evidence: Augusta Ada King, Countess of Lovelace (née Byron; 10 December 1815 – 27 November 1852) was an English mathematician and writer, chiefly known for her work on Charles Babbage's proposed mechanical general-purpose computer, the Analytical Engine. She was the first to recognise that the machine had applications beyond pure calculation, and to have published the first algorithm intended to be carried out by such a machine. As a result, she is often regarded as the first computer programmer
Answer: Augusta Ada King, Countess of Lovelace (née Byron; 10 December 1815 – 27 November 1852) is often regarded as the first computer programmer for her work on the Analytical Engine with Charles Babbage.
Question: What is the best way to live life
Answer: 1. Be helpful
2.Be honest
3. Be Kind
4. Be positive
Question: What did Gertrude Townend do
Answer: Gertrude Catherine Townend was a British nurse and suffragette. She provided, with Nurse Catherine Pine, a care home for suffragettes recovering from imprisonment and force-feeding, and participated in suffragette gatherings.
Question: Give me a list of ways to lose weight
Answer: * Get more active
* Drink plenty of water
* Use a smaller plate
* Cut down on alcohol
Question: Identify which animal species is alive or extinct: Cave Bear, Saola
Answer: Saola is alive, Cave Bear is extinct.
Question: How to do you convert from inches to cm?
Answer: You can convert inches to centimeters using the following:
cm = 2.54 * inches, for example, 5 inches = 5 * 2.54 = 12.7 cm
Question: What dataset is used?
Evidence: We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:
We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.
Answer: The answers are shown as follows:
* HEOT 
* A labelled dataset for a corresponding english tweets
Question: What datasets did they use?
Evidence: Our experiments on a goal-oriented dialog corpus, the personalized bAbI dialog dataset, show that leveraging personal information can significantly improve the performance of dialog systems. The Personalized MemN2N outperforms current state-of-the-art methods with over 7% improvement in terms of per-response accuracy. A test with real human users also illustrates that the proposed model leads to better outcomes, including higher task completion rate and user satisfaction.
Our experiments on a goal-oriented dialog corpus, the personalized bAbI dialog dataset, show that leveraging personal information can significantly improve the performance of dialog systems. 
Answer: The answers are shown as follows:
* the personalized bAbI dialog dataset
Question: Which dataset was used to construct the context-gloss pairs?
Evidence: Word Sense Disambiguation (WSD) is a fundamental task and long-standing challenge in Natural Language Processing (NLP), which aims to find the exact sense of an ambiguous word in a particular context BIBREF0. Previous WSD approaches can be grouped into two main categories: knowledge-based and supervised methods.
Knowledge-based WSD methods rely on lexical resources like WordNet BIBREF1 and usually exploit two kinds of lexical knowledge. The gloss, which defines a word sense meaning, is first utilized in Lesk algorithm BIBREF2 and then widely taken into account in many other approaches BIBREF3, BIBREF4. Besides, structural properties of semantic graphs are mainly used in graph-based algorithms BIBREF5, BIBREF6.
Traditional supervised WSD methods BIBREF7, BIBREF8, BIBREF9 focus on extracting manually designed features and then train a dedicated classifier (word expert) for every target lemma.
Although word expert supervised WSD methods perform better, they are less flexible than knowledge-based methods in the all-words WSD task BIBREF10. Recent neural-based methods are devoted to dealing with this problem. BIBREF11 present a supervised classifier based on Bi-LSTM, which shares parameters among all word types except the last layer. BIBREF10 convert WSD task to a sequence labeling task, thus building a unified model for all polysemous words. However, neither of them can totally beat the best word expert supervised methods.
More recently, BIBREF12 propose to leverage the gloss information from WordNet and model the semantic relationship between the context and gloss in an improved memory network. Similarly, BIBREF13 introduce a (hierarchical) co-attention mechanism to generate co-dependent representations for the context and gloss. Their attempts prove that incorporating gloss knowledge into supervised WSD approach is helpful, but they still have not achieved much improvement, because they may not make full use of gloss knowledge.
In this paper, we focus on how to better leverage gloss information in a supervised neural WSD system. Recently, the pre-trained language models, such as ELMo BIBREF14 and BERT BIBREF15, have shown their effectiveness to alleviate the effort of feature engineering. Especially, BERT has achieved excellent results in question answering (QA) and natural language inference (NLI). We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem. We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task. In particular, our contribution is two-fold:
1. We construct context-gloss pairs and propose three BERT-based models for WSD.
2. We fine-tune the pre-trained BERT model, and the experimental results on several English all-words WSD benchmark datasets show that our approach significantly outperforms the state-of-the-art systems.
We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem.
Answer: WordNet
Question: What is the effect of the system combination?
Evidence: To further improve the performance, we adopt system combination on the decoding lattice level. By combining systems, we can take advantage of the strength of each model that is optimized for different domains. The results for 2 test sets is showed on Table TABREF17 and TABREF18.
As we can see, for both test sets, system combination significantly reduce the WER. The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%.
As we can see, for both test sets, system combination significantly reduce the WER. The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%.
Answer: The WER is reduced.
Question: To improve performance in low resource domains, what do the authors explore?
Evidence: Recently, with the emergence of neural seq2seq models, abstractive summarization methods have seen great performance strides BIBREF0, BIBREF1, BIBREF2. However, complex neural summarization models with thousands of parameters usually require a large amount of training data. In fact, much of the neural summarization work has been trained and tested in news domains where numerous large datasets exist. For example, the CNN/DailyMail (CNN/DM) BIBREF3, BIBREF4 and New York Times (NYT) datasets are in the magnitude of 300k and 700k documents, respectively. In contrast, in other domains such as student reflections, summarization datasets are only in the magnitude of tens or hundreds of documents (e.g., BIBREF5). We hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models, and we will indeed later show that this is the case for student reflections.
To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, we combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies (Hua and Wang 2017; Keneshloo, Ramakrishnan, and Reddy 2019), our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, we combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
Answer: The authors explore three directions. First, they explore domain transfer for abstractive summarization. Second, they propose a template-based synthesis method to create synthesized summaries, and then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, they combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
Question: What is yt standing for?
Evidence: The objective function was the negative log-likelihood and computed by: 
$$\textbf {F}(\theta ) = -\sum _{t=1}^{T}\textbf {y}_{t}^{\top }\log {\textbf {p}_{t}}$$   (Eq. 27) 
where: $\textbf {y}_{t}$ is the ground truth token distribution, $\textbf {p}_{t}$ is the predicted token distribution, $T$ is length of the input sentence. The proposed generators were trained by treating each sentence as a mini-batch with $l_{2}$ regularization added to the objective function for every 5 training examples. The models were initialized with a pretrained Glove word embedding vectors BIBREF28 and optimized by using stochastic gradient descent and back propagation through time BIBREF29 . Early stopping mechanism was implemented to prevent over-fitting by using a validation set as suggested in BIBREF30 .
yt is the ground truth token distribution.
Answer: yt is the ground truth token distribution.
Question: Which datasets do they evaluate on?
Evidence: Recently, neural models pre-trained on a language modeling task, such as ELMo BIBREF0 , OpenAI GPT BIBREF1 , and BERT BIBREF2 , have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference. The success of BERT can largely be associated to the notion of context-aware word embeddings, which differentiate it from common approaches such as word2vec BIBREF3 that establish a static semantic embedding. Since the introduction of BERT, the NLP community continues to be impressed by the amount of ideas produced on top of this powerful language representation model. However, despite its success, it remains unclear whether the representations produced by BERT can be utilized for tasks such as commonsense reasoning. Particularly, it is not clear whether BERT shed light on solving tasks such as the Pronoun Disambiguation Problem (PDP) and Winograd Schema Challenge (WSC). These tasks have been proposed as potential alternatives to the Turing Test, because they are formulated to be robust to statistics of word co-occurrence BIBREF4 .
In this paper, we show that the attention maps created by an out-of-the-box BERT can be directly exploited to resolve coreferences in long sentences. As such, they can be simply repurposed for the sake of commonsense reasoning tasks while achieving state-of-the-art results on the multiple task. On both PDP and WSC, our method outperforms previous state-of-the-art methods, without using expensive annotated knowledge bases or hand-engineered features. On a Pronoun Disambiguation dataset, PDP-60, our method achieves 68.3% accuracy, which is better than the state-of-art accuracy of 66.7%. On a WSC dataset, WSC-273, our method achieves 60.3%. As of today, state-of-the-art accuracy on the WSC-273 for single model performance is around 57%, BIBREF14 and BIBREF13 . These results suggest that BERT implicitly learns to establish complex relationships between entities such as coreference resolution. Although this helps in commonsense reasoning, solving this task requires more than employing a language model learned from large text corpora.
Particularly, it is not clear whether BERT shed light on solving tasks such as the Pronoun Disambiguation Problem (PDP) and Winograd Schema Challenge (WSC).
On a Pronoun Disambiguation dataset, PDP-60, our method achieves 68.3% accuracy, which is better than the state-of-art accuracy of 66.7%. On a WSC dataset, WSC-273, our method achieves 60.3%. 
Answer: The answers are shown as follows:
* PDP-60
* WSC-273
Question: What are some of the uses of gilding metal?
Evidence: Gilding metal is a form of brass (an alloy of copper and zinc) with a much higher copper content than zinc content. Exact figures range from 95% copper and 5% zinc to “8 parts copper to 1 of zinc” (11% zinc) in British Army Dress Regulations.

Gilding metal is used for various purposes, including the jackets of bullets, driving bands on some artillery shells, as well as enameled badges and other jewellery. The sheet is widely used for craft metalworking by hammer working. It is also used particularly as a lower-cost training material for silversmiths. Starting in 1944, shell casings made of gilding metal were melted down by the United States Mint to be made into pennies. These pennies replaced the less popular steel cent of 1943, and the pennies of this composition were produced until 1946.

Gilding metal was also used for the "bronze" Olympic medal at the 2020 Summer Olympics held in Tokyo, Japan in 2021.

Gilding metal may be annealed by heating to between 800–1,450 °F (427–788 °C). It should be cooled slowly afterwards, to reduce risk of cracking.
Answer: Army Dress, weapons, metalworking, penny production, and Olympic medals
Question: List the basis of contemporary national legal systems from the passage. Display the results in comma separated format.
Evidence: The contemporary national legal systems are generally based on one of four basic systems: civil law, common law, statutory law, religious law or combinations of these. However, the legal system of each country is shaped by its unique history and so incorporates individual variations. The science that studies law at the level of legal systems is called comparative law.

Both civil (also known as Roman) and common law systems can be considered the most widespread in the world: civil law because it is the most widespread by landmass and by population overall, and common law because it is employed by the greatest number of people compared to any single civil law system.
Answer: civil law, common law, statutory law, religious law, comparative law
Question: What did Franz Beckenbauer call Bobby Moore?
Evidence: "My captain, my leader, my right-hand man. He was the spirit and the heartbeat of the team. A cool, calculating footballer I could trust with my life. He was the supreme professional, the best I ever worked with. Without him England would never have won the World Cup." Alf Ramsey*
"He was my friend as well as the greatest defender I ever played against. The world has lost one of its greatest football players and an honourable gentleman." Pelé
"Bobby Moore was a real gentleman and a true friend." Franz Beckenbauer
"Moore was the best defender I have ever seen." Sir Alex Ferguson
"Bobby Moore was the best defender in the history of the game" Franz Beckenbauer
"There should be a law against him. He knows what's happening 20 minutes before everyone else." Jock Stein
"Ask me to talk about Bobby Moore the footballer and I will talk for days. Ask me about the man and I will dry up in a minute." Ron Greenwood
"Immaculate footballer. Imperial defender. Immortal hero of 1966. First Englishman to raise the World Cup aloft. Favourite son of London's East End. Finest legend of West Ham United. National Treasure. Master of Wembley. Lord of the game. Captain extraordinary. Gentleman of all time." Inscription on the pedestal of the statue at Wembley Stadium.
Answer: Franz Beckenbauer described Bobby Moore as "a real gentleman and a true friend".
Question: Think up different types of places to go for vacation.
Answer: There are many different places you can go on vacation. For people who love the outdoors, camping is a great option, including hiking, kayaking, and bird-watching. For those who like relaxation, a hotel by the beach can be ideal. Beach-related vacations can include sunbathing, swimming, laying out by the pool, and having refreshing drinks. Traveling to a different country can be an enriching vacation if you enjoy sightseeing, learning new languages, and sampling unknown foods.
Question: List five titles by Suzanne Collins.
Answer: Catching Fire, Mockingjay, Gregor the Overlander, Year of the Jungle, The Ballad of Songbirds and Snakes