DataHammer/scimrc huggingface dataset:
Question: Jing is a 46-year-old male. He is an insurance executive who is married with two children. Jing is active in his church and plays golf on the weekends. He went to see his cardiologist because he experienced recent fatigue with chest pain on exertion. He has never smoked but he consumes 1 to 2 alcoholic drinks per day. His medical history reveals a blood cholesterol level of 260 mg%, a triglyceride level of 198 mg% and an HDL-C value of 43 mg%.  Considering his sex, age, symptoms, and risk factors, can you reason about the likelihood of Jing having coronary heart disease?
Answer: Considering his sex, Jing is male, and men are generally at greater risk of coronary artery disease. Age is also a risk factor, as getting older increases his risk of damaged arteries. Additionally, he has experienced angina, which can be a common symptom of coronary artery disease. He also has high cholesterol, and high triglyceride levels. He also consumes alcohol at high levels. All this in consideration, Jing has a good likelihood of having some degree of coronary heart disease.
Question: In 93rd Academy Awards(Oscar Awards)The Best Picture award was given to whom?
Answer: Nomadland
Question: What are the Wimbledon Championships?
Evidence: The Wimbledon Championships, commonly known simply as Wimbledon, is the oldest tennis tournament in the world and is widely regarded as the most prestigious. It has been held at the All England Club in Wimbledon, London, since 1877 and is played on outdoor grass courts, with retractable roofs over the two main courts since 2019.
Answer: The Wimbledon Championships, commonly known simply as Wimbledon, is the oldest tennis tournament in the world and is widely regarded as the most prestigious. It has been held at the All England Club in Wimbledon, London, since 1877 and is played on outdoor grass courts, with retractable roofs over the two main courts since 2019.

Wimbledon is one of the four Grand Slam tennis tournaments, the others being the Australian Open, the French Open, and the US Open. Wimbledon is the only major still played on grass, the traditional tennis playing surface. Also, it is the only Grand Slam that retains a night-time curfew, though matches can now continue until 11.00 pm under the lights.

The tournament traditionally takes place over two weeks in late June and early July, starting on the last Monday in June and culminating with the Ladies' and Gentlemen's Singles Finals, scheduled for the Saturday and Sunday at the end of the second week. Five major events are held each year, with additional junior and invitational competitions also taking place. In 2009, Wimbledon's Centre Court was fitted with a retractable roof to lessen the loss of playing time due to rain. A roof was operational over No. 1 Court from 2019, when a number of other improvements were made, including adding cushioned seating, a table and 10 independently operable cameras per court to capture the games.

Wimbledon traditions include a strict all-white dress code for competitors, and royal patronage. Strawberries and cream are traditionally consumed at the tournament. Unlike other tournaments, advertising is minimal and low key from official suppliers such as Slazenger and Rolex. The relationship with Slazenger is the world's longest-running sporting sponsorship, providing balls for the tournament since 1902.

Due to the COVID-19 pandemic, 2020 Wimbledon was cancelled, the first cancellation of the tournament since World War II. The rescheduled 134th edition was staged from 28 June 2021 to 11 July 2021, following from the 2020 cancellation. The 135th edition was played between 27 June 2022 and 10 July 2022, and regularly scheduled play occurred on the middle Sunday for the first time. It marks the centenary of the inaugural championships staged at the Centre Court. The ATP, ITF, and WTA did not award ranking points for the 2022 tournament, due to controversy over the tournament excluding players representing Russia and Belarus.

The 2023 Wimbledon Championships will be the 136th staging and will run from 3 July 2023 to 16 July 2023 and it will be the first event of King Charles III since the death of the former patron, Queen Elizabeth II on 8 September 2022.
Question: What will they do in the future?
Evidence: In this paper, we adopt a data-driven approach which includes data collection, data cleaning, data normalization, descriptive analysis and predictive analysis, to evaluate the quality on Zhihu Live platform. To the best of our knowledge, we are the first to research quality evaluation of voice-answering products. We publicize a dataset named ZhihuLive-DB, which contains 7242 records and 286,938 comments text for researchers to evaluate Zhihu Lives' quality. We also make a detailed analysis to reveal inner insights about Zhihu Live. In addition, we propose MTNet to accurately predict Zhihu Lives' quality. Our proposed method achieves best performance compared with the baselines.
As knowledge sharing and Q&A platforms continue to gain a greater popularity, the released dataset ZhihuLive-DB could greatly help researchers in related fields. However, current data and attributes are relatively unitary in ZhihuLive-DB. The malicious comment and assessment on SNS platforms are also very important issues to be taken into consideration. In our future work, we will gather richer dataset, and integrate malicious comments detector into our data-driven approach.
In our future work, we will gather richer dataset, and integrate malicious comments detector into our data-driven approach.
Answer: They will gather richer dataset, and integrate malicious comments detector into their data-driven approach.
Question: How are the best results for each domain obtained in most cases?
Evidence: Experimental Setup We follow BIBREF28 and train domain-specific models for all domains. We then evaluate each model across the different domain test sets, enabling us to understand the effect of different domains on the downstream MT performance and to set up strong baselines for data selection experiments. We also train a general-domain model using the available data from all domains, as it is also a common approach in multi-domain scenarios BIBREF29. In all experiments we use a similar Transformer BIBREF36 model, and only control for the training data. More details on the exact training and hyperparameter settings for the NMT models are available in the supplementary material.
Results The results for the cross-domain evaluation are available in Table TABREF28. In most cases, the best results for each domain are obtained by training on the in-domain data. Training on all the available data helped mostly for the Koran test set. This is expected as the training data for this domain is considerably smaller than the training data for rest of the domains (Table TABREF24). We can also see that more data is not necessarily better BIBREF37: while the subtitles corpus is the largest of all 5 and includes 500,000 sentence pairs, it is second to last in performance as measured by the average BLEU across all test sets.
Cross-Domain BLEU vs. Cluster Proximity An interesting observation can be made with respect to the visual analysis of the domain clusters as depicted in Figure FIGREF15: as the Medical cluster (in Yellow), Law cluster (in Purple) and IT cluster (in Red) are close to each other in the embedding space, their cross-domain BLEU scores are also higher. For example, note how in the results for the Medical domain-specific model (first row in Table TABREF28), the BLEU scores on the Law and IT test sets are much higher in comparison to those on the Koran and Subtitles test sets, which clusters are farther away in the visualized embedding space. Similarly, as the Subtitles cluster (Blue) is closer to the Koran cluster (Green), the highest cross-domain BLEU score on the Koran test set is from the Subtitles model. To further quantify this phenomenon, we plot and measure Pearson's correlation between the cosine similarity of the centroids for the English BERT-based dev sentence representations for each domain pair, and the cross-domain BLEU score for this domain pair. This is shown in Figure FIGREF29. We can see the general trend where the closer the domain centroids are (with a similarity of 1 for training and evaluating on the same domain), the higher the cross-domain BLEU is between those domains, resulting in a Pearson's correlation of 0.81 (strong correlation). This suggests that such preliminary visual analysis can be a useful tool for understanding the relationship between diverse datasets, and motivates the use of pre-trained language model representations for domain data selection in MT.
In most cases, the best results for each domain are obtained by training on the in-domain data.
Answer: They are obtained by training on the in-domain data.
Question: Name some of the highest-grossing film of all time from July 2019 until March 2021 based on the passage.
Evidence: Since 2008, Marvel Studios has released 31 films within the MCU, from Iron Man (2008) to Ant-Man and the Wasp: Quantumania (2023), eight television series since 2021, from WandaVision (2021) to She-Hulk: Attorney at Law (2022), and two television specials, Werewolf by Night (2022) and The Guardians of the Galaxy Holiday Special (2022). The television series What If...? (2021) is the studio's first animated property, created by its "mini-studio" Marvel Studios Animation. These films, television series, and television specials all share continuity with each other, along with the One-Shots short films produced by the studio. The television series produced by Marvel Television also acknowledge the continuity.The Avengers (2012), Iron Man 3 (2013), Avengers: Age of Ultron (2015), Captain America: Civil War (2016), Black Panther (2018), Avengers: Infinity War (2018), Captain Marvel (2019), Avengers: Endgame (2019), Spider-Man: Far From Home (2019) and Spider-Man: No Way Home (2021) are all among the 50 highest-grossing films of all time, with Avengers: Endgame becoming the highest-grossing film of all time from July 2019 until March 2021. In addition to the MCU, Marvel Studios was also involved with the production of other Marvel-character film franchises that have exceeded $1 billion in North American box office revenue, including the X-Men and Spider-Man multi-film franchises.
Answer: The Avengers (2012), Iron Man 3 (2013), Avengers: Age of Ultron (2015), Captain America: Civil War (2016), Black Panther (2018), Avengers: Infinity War (2018), Captain Marvel (2019), Avengers: Endgame (2019), Spider-Man: Far From Home (2019) and Spider-Man: No Way Home (2021).
Question: Why do we have a threefold repetition rule in chess?
Evidence: In chess, the threefold repetition rule states that a player may claim a draw if the same position occurs three times during the game. The rule is also known as repetition of position and, in the USCF rules, as triple occurrence of position. Two positions are by definition "the same" if the same types of pieces occupy the same squares, the same player has the move, the remaining castling rights are the same and the possibility to capture en passant is the same. The repeated positions need not occur in succession. The reasoning behind the rule is that if the position occurs three times, no real progress is being made and the game could hypothetically continue indefinitely.

The game is not automatically drawn if a position occurs for the third time – one of the players, on their turn, must claim the draw with the arbiter. The claim must be made either before making the move which will produce the third repetition, or after the opponent has made a move producing a third repetition. By contrast, the fivefold repetition rule requires the arbiter to intervene and declare the game drawn if the same position occurs five times, needing no claim by the players.
Answer: This rule is to ensure the game ends and does not continue indefinitely.
Question: Qui était Clovis?
Answer: Clovis Ier, en latin Chlodovechus, né vers 466 et mort à Paris le 27 novembre 511, est roi des Francs saliens, puis roi de tous les Francs de 481 à 511.

Issu de la dynastie des Mérovingiens, il est le fils de Childéric Ier, roi des Francs saliens de Tournai (en actuelle Belgique), et de la reine Basine de Thuringe. Chef militaire, il accroît considérablement le territoire du petit royaume des Francs saliens, dont il hérite à la mort de son père, pour finir par unifier une grande partie des royaumes francs, repousser Alamans et Burgondes et annexer les territoires des Wisigoths dans le Sud de la Gaule.

Le règne de Clovis est surtout connu par la description qu'en fit Grégoire de Tours, évêque gallo-romain dont l'Histoire des Francs est riche d'enseignements, mais dont la visée, essentiellement édifiante, s'accompagne d'un manque de précision et de cohérence historique. Les éléments de la vie de Clovis ne sont pas connus de manière certaine et leur « habillage » est le plus souvent suspect. Néanmoins, Clovis est considéré dans l'historiographie comme un des personnages les plus importants de l'histoire de France.
Question: What does the Meta-NLG base on?
Evidence: As an essential part of a task-oriented dialogue system BIBREF0 , the task of natural language generation (NLG) is to produce a natural language utterance containing the desired information given a semantic representation consisting of dialogue act types with a set of slot-value pairs. Conventional methods using hand-crafted rules often generates monotonic utterances and it requires substantial amount of human engineering work. Recently, various neural approaches BIBREF1 , BIBREF2 , BIBREF3 have been proposed to generate accurate, natural and diverse utterances. However, these methods are typically developed for particular domains. Moreover, they are often data-intensive to train. The high annotation cost prevents developers to build their own NLG component from scratch. Therefore, it is extremely useful to train a NLG model that can be generalized to other NLG domains or tasks with a reasonable amount of annotated data. This is referred to low-resource NLG task in this paper.
Recently, some methods have been proposed for low-resource NLG tasks. Apart from the simple data augmentation trick BIBREF4 , specialized model architectures, including conditional variational auto-encoders (CVAEs, BIBREF3 , BIBREF5 , BIBREF6 ) and adversarial domain adaptation critics BIBREF5 , have been proposed to learn domain-invariant representations. Although promising results were reported, we found that datasets used by these methods are simple which tend to enumerate many slots and values in an utterance without much linguistic variations. As a consequence, over-fitting the slots and values in the low-resource target domain could even outperform those versions trained with rich source domain examples BIBREF6 . Fortunately, there is a new large-scale dialog dataset (MultiWoz, BIBREF7 ) that contains a great variety of domains and linguistic patterns that allows us to conduct extensive and meaningful experimental analysis for low-resource NLG tasks.
In this paper, instead of casting the problem as model-based approaches, we propose a generalized optimization-based meta-learning approach to directly enhance the optimization procedure for the low-resource NLG task. We start by arguing that a recently proposed model-agnostic meta-learning algorithm (MAML, BIBREF8 ) is a nice fit to the low-resource NLG task. Then, we proposed a generalized NLG algorithm called Meta-NLG based on MAML by viewing languages in different domains or dialog act types as separate Meta NLG tasks. Following the essence of MAML, the goal of Meta-NLG is to learn a better initialization of model parameters that facilitates fast adaptation to new low-resource NLG scenarios. As Meta-NLG is model-agnostic as long as the model can be optimized by gradient descent, we could apply it to any existing NLG models to optimize them in a way that adapt better and faster to new low-resource tasks.
The main contribution of this paper is two-fold:
Then, we proposed a generalized NLG algorithm called Meta-NLG based on MAML by viewing languages in different domains or dialog act types as separate Meta NLG tasks.
Answer: It is based on MAML.
Question: Why is lacrosse considered the fastest game on two feet?
Answer: During both mens and women's lacrosse games, the ball moves very quickly over a large field, both by passing between players as well as the players running the ball themselves from the defensive to offense ends of the field.
Question: Extract the title of the game, the name of its developer and the name of the main character and separate them by a comma.
Evidence: Horizon Zero Dawn is a 2017 action role-playing game developed by Guerrilla Games and published by Sony Interactive Entertainment. It is the first game of the Horizon video game series. The plot follows Aloy, a young hunter in a world overrun by machines, who sets out to uncover her past. The player uses ranged weapons, a spear, and stealth to combat mechanical creatures and other enemy forces. A skill tree provides the player with new abilities and bonuses. The player can explore the open world to discover locations and take on side quests. It is the first game in the Horizon series and was released for the PlayStation 4 in 2017 and Windows in 2020.
Answer: Horizon Zero Dawn, Guerrilla Games, Aloy
Question: What two main modules their approach consists of?
Evidence: The Co-Attention mechanism inspires us to build part of our VQABQ model, illustrated by Figure 2 . In the VQABQ model, there are two main modules, the basic question generation module (Module 1) and co-attention visual question answering module (Module 2). We take the query question, called the main question (MQ), encoded by Skip-Thought Vectors BIBREF9 , as the input of Module 1. In the Module 1, we encode all of the questions, also by Skip-Thought Vectors, from the training and validation sets of VQA BIBREF0 dataset as a 4800 by 215623 dimension basic question (BQ) matrix, and then solve the LASSO optimization problem, with MQ, to find the 3 BQ of MQ. These BQ are the output of Module 1. Moreover, we take the MQ, BQ and the given image as the input of Module 2, the VQA module with co-attention mechanism, and then it can output the final answer of MQ. We claim that the BQ can help Module 2 get the correct answer to increase the VQA accuracy. In this work, our main contributions are summarized below:
In the VQABQ model, there are two main modules, the basic question generation module (Module 1) and co-attention visual question answering module (Module 2). 
Answer: The answers are shown as follows:
* the basic question generation module (Module 1) and co-attention visual question answering module (Module 2)
Question: What are the steps to perform breathing meditation?
Answer: The simplest, most elegant and accessible breathing meditation technique is called "Sohum Sadhana". The way to perform it is the following:
1. Sit in a comfortable posture. Any posture which you can maintain without unsure stress anywhere on the body is fine.
2. Close your eyes.
3. Breathe in and out as you normally would.
4. While inhaling, mentally recite the syllable "So", with a long "o", which stretches as long as inhalation action continues.
5. While exhaling, mentally recite the syllable "hum", with the "um" pronounced as in "rum". Let the ending "m" sound continue until the end of the exhalation action.
6. Keeps repeating steps 5 and 6 as long as you want.
Question: Who were fighting each other in this battle and what's the consequence of it
Evidence: The Battle of Kuningtou or Battle of Guningtou, also known as the Battle of Kinmen, was a battle fought over Kinmen in the Taiwan Strait during the Chinese Civil War in 1949. The failure of the Communists to take the island left it in the hands of the Kuomintang (Nationalists) and crushed their chances of taking Taiwan to destroy the Nationalists completely in the war.
Answer: Chinese Nationalist and Chinese Communists fought this battle during Chinese Civil War in 1949. Chinese Nationalist won the battle which deterred Chinese Communists from taking over Taiwan, the last stronghold of Chinese Nationalist government fled from mainland in 1949 until today.
Question: Please tell me whether these are fish or fowl: salmon, chicken, tuna, swordfish, turkey, and halibut.
Answer: The fish are: salmon, tuna, swordfish, and halibut. The fowl (i.e., birds) are chicken and turkey.
Question: Tell me about NGC 7314
Evidence: NGC 7314 is a spiral galaxy located in the southern constellation of Piscis Austrinus. It was discovered by English astronomer John Herschel on July 29, 1834. This is a nearby Seyfert (active) galaxy, located at a distance of approximately 54.6 megalight-years from the Milky Way. Since it appears to have detached spiral arm segments (either from dust lanes or bright star clusters), it was listed in Halton Arp's Atlas of Peculiar Galaxies.

Walter Scott Houston describes its appearance in small telescopes:

Do not let its photographic magnitude of 11.6 scare you off, for it can be seen in a 6-inch telescope as a curiously fuzzy object. But it is small, appearing only 4' by 2'.

The morphological classification of this galaxy is SAB(rs)bc, indicating a spiral galaxy with a weak central bar (SAB), an incomplete ring structure around the bar (rs), and moderately–wound arms (bc). The plane of the galactic disk is inclined by 64° to the line of sight from the Earth, with the major axis aligned along a position angle of 178°. Within the galaxy's core is an active galactic nucleus tentatively classified as a type I Seyfert. The central supermassive black hole has a relatively low mass, estimated as (0.87±0.45)×106 M☉. The core is a source for X-ray emission that is seen to vary dramatically on time scales as low as hours.
Answer: NGC 7314 is a spiral galaxy located in the southern constellation of Piscis Austrinus. It was discovered by English astronomer John Herschel on July 29, 1834
Question: What is a good kids snack for school?
Answer: You have many different options when considering a good snack for kids?  Typically if the snack is for school you will want to choose a healthy snack to keep your child alert during class and paying attention.  Healthy snack options are fruits and vegetables, cheese and crackers or goldfish.  Other unhealthier snack options would be sugary snacks like gummy bears and chocolate.
Question: What are the disadvantages of the state-of-the-art?
Evidence: The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.
Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well.
Answer: Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well.
Question: In the series A Song of Ice and Fire, who is the founder of House Frey?
Answer: Emmon Frey