DataHammer/scimrc huggingface dataset:
Question: How do the authors define exemplars?
Evidence: Exemplars aim to provide appropriate context. To better understand the context, we experimented by analysing the questions generated through an exemplar. We observed that indeed a supporting exemplar could identify relevant tags (cows in Figure FIGREF3 ) for generating questions.
We improve use of exemplars by using a triplet network. This network ensures that the joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption and vice-versa. We empirically evaluated whether an explicit approach that uses the differential set of tags as a one-hot encoding improves the question generation, or the implicit embedding obtained based on the triplet network. We observed that the implicit multimodal differential network empirically provided better context for generating questions. Our understanding of this phenomenon is that both target and supporting exemplars generate similar questions whereas contrasting exemplars generate very different questions from the target question. The triplet network that enhances the joint embedding thus aids to improve the generation of target question. These are observed to be better than the explicitly obtained context tags as can be seen in Figure FIGREF2 . We now explain our method in detail.
Exemplars aim to provide appropriate context. To better understand the context, we experimented by analysing the questions generated through an exemplar. We observed that indeed a supporting exemplar could identify relevant tags (cows in Figure FIGREF3 ) for generating questions.

We improve use of exemplars by using a triplet network. This network ensures that the joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption and vice-versa.
Answer: The answers are shown as follows:
* Exemplars aim to provide appropriate context.
* joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption
Question: Provide a bulleted list of ways to spend less money
Answer: The following are ways to spend less money
1. Make a budget and track spending
2. Cook your own food
3. Don't drink alcohol
4. Delete the Amazon application
5. Have get togethers at home
6. Find a cheaper place to live
7. Negotiate insurance rates
Question: What is the best location for an all-season vacation home with proximity to Philadelphia?
Answer: There are many options with lakes, mountains and beaches within 150 miles of Philadelphia. Rehoboth Beach, Dewey Beach and Bethany Beach Delaware are accessible and family-friendly destinations that have something for everyone; with beautiful parks, free beaches and tax-free outlet shopping. There is also a vibrant restaurant scene with top-notch seafood and highly-rated breweries that remain open year-round.
Question: What generalizability issue did they address?
Evidence: Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an"apparently positive"sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network's baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.
We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.
Answer: Classifying data that have not been seen by the models at learning phase.
Question: What is the novelty of their work?
Evidence: Multilingual language models is not a new idea BIBREF3 , the novelty of our work lies primarily in the use of language vectors and the empirical evaluation using nearly a thousand languages.
Concurrent with this work, Johnson2016zeroshot conducted a study using neural machine translation (NMT), where a sub-word decoder is told which language to generate by means of a special language identifier token in the source sentence. This is close to our model, although beyond a simple interpolation experiment (as in our sec:generating) they did not further explore the language vectors, which would have been challenging to do given the small number of languages used in their study.
Ammar2016manylanguages used one-hot language identifiers as input to a multilingual word-based dependency parser, based on multilingual word embeddings. Given that they report this resulting in higher accuracy than using features from a typological database, it is a reasonable guess that their system learned language vectors which were able to encode syntactic properties relevant to the task. Unfortunately, they also did not look closer at the language vector space, which would have been interesting given the relatively large and diverse sample of languages represented in the Universal Dependencies treebanks.
Our evaluation in sec:clustering calls to mind previous work on automatic language classification, by Wichmann2010evaluating among others. However, our purpose is not to detect genealogical relationships, even though we use the strong correlation between such classifications and our language vectors as evidence that the vector space captures sensible information about languages.
Multilingual language models is not a new idea (Fugen et al., 2003), the novelty of our work lies primarily in the use of language vectors and the empirical evaluation using nearly a thousand languages.
Answer: The use of language vectors and the empirical evaluation using nearly a thousand languages.
Question: Which six target languages were evaluated?
Evidence: We build our bilingual LMs, named RAMEN, starting from BERT$_{\textsc {base}}$, BERT$_{\textsc {large}}$, RoBERTa$_{\textsc {base}}$, and RoBERTa$_{\textsc {large}}$ pre-trained models. Using BERT$_{\textsc {base}}$ allows us to compare the results with mBERT model. Using BERT$_{\textsc {large}}$ and RoBERTa allows us to investigate whether the performance of the target LM correlates with the performance of the source LM. We evaluate our models on two cross-lingual zero-shot tasks: (1) Cross-lingual Natural Language Inference (XNLI) and (2) dependency parsing.
We evaluate our approach for six target languages: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).
Answer: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).
Question: Give me a comma separated list of the main attractions in Tomar.
Evidence: Tomar attracts many tourists because of its varied monuments. These include:

Castle and Convent of the Order of Christ – Unesco World Heritage Site: An ensemble of 12th to 16th century architecture and art, it is the main monument of the city and one of the most important in Portugal.
Aqueduct of Pegões – Built between 1503 and 1614 to bring water to the convent of Christ in Tomar under command of king Philip I, the aqueduct is 6 kilometers long and in places reaches a height of 30 meters. It is the biggest and most important construction of Philip I in Portugal.
Church of Santa Maria do Olival: This 13th century Gothic church was built as a burial ground for the Knights Templar and their treasure.
Synagogue of Tomar: the best preserved mediaeval synagogue of Portugal (and one of two pre-expulsion Synagogues in the country), built in the mid-15th century the Jewish community of Tomar. Since 1939 it houses the Jewish Museum Abraão Zacuto, with pieces related to Jewish history in Portugal.
Church of Saint John the Baptist (São João Baptista): The main church of Tomar is located in the main square of the town, in front of the Municipality (17th century) and a modern statue of Gualdim Pais. The church was built between the 15th and 16th centuries. In addition to its architectural interest it is noted for several panels painted in the 1530s by one of Portugal's most renowned Renaissance artists, Gregório Lopes.
Chapel of Our Lady of the Conception (Nossa Senhora da Conceição): Chapel built between 1532 and 1540 in pure Renaissance style, begun by João de Castilho and finished by Diogo de Torralva. It was intended to be the burial chapel of King John III.
Church and Convent of Saint Iria: An early 16th century building located near the Nabão river.
Museu dos Fosforos (Matchbox Museum) – The biggest private matchbox collection in Europe.
Museu de Arte Moderna – Colecção José Augusto França (art gallery)
Casa Museu Fernando Lopes Graça
Casa dos Cubos (art gallery)
The streets and squares of the picturesque centre of Tomar are organised following a chessboard pattern, a rare feature for a mediaeval city, instituted by Prince Henry the Navigator, which later inspired the pattern used for the rebuilding of Lisbon after the earthquake in 1755. Scattered throughout the town there are many interesting houses with Renaissance, Baroque and Romantic façades. By the river Nabão, near the bridge, there is a park and garden that offer views of the city and surroundings.
Answer: Castle and Convent of the Order of Christ, Aqueduct of Pegões, Church of Santa Maria do Olival, Synagogue of Tomar, Church of Saint John the Baptist, Chapel of Our Lady of the Conception, Church and Convent of Saint Iria, Museu dos Fosforos (Matchbox Museum), Museu de Arte Moderna, Casa Museu Fernando Lopes Graça, Casa dos Cubos (art gallery)
Question: What is the benefit of cinnamon
Answer: Helps control blood sugar levels and prevent heart disease
Question: Please give me a brief summary of this paragraph about Tom Brady's NFL accomplishments.
Evidence: The winningest quarterback in NFL history, Brady won 251 regular season games and 35 postseason games for a combined 286 wins. Brady holds a .754 winning percentage, which is the highest among NFL quarterbacks who have started 100 games. He is the only NFL quarterback to win 200 regular season games and his 35 postseason victories are more than twice those of any other quarterback. Brady is also the only NFL quarterback to win all 16 regular season games, which he accomplished in 2007. In 2021, Brady became one of four quarterbacks to defeat all 32 NFL teams. Brady has the most Pro Bowl selections in NFL history at 15 and was named league MVP three times in 2007, 2010, and 2017. His 2010 MVP was awarded in an unanimous decision; the first time this distinction was achieved. Brady was also named Male Athlete of the Year in 2007, making him third NFL player to receive the award, joining Joe Montana. Brady, Montana, and Patrick Mahomes are the only players to win multiple NFL MVP and Super Bowl MVP awards.
Answer: Brady is the winningest quarterback in NFL history. He holds numerous records in the regular season and postseason. He also holds distinctions as the only quarterback to accomplish certain feats, such as winning all 16 regular season games in 2007.
Question: What is a safe temperature for a hot bath?
Answer: Its advisable to stay between 101 and 105 degrees fahrenheit for a hot bath to ensure you don't burn your skin.
Question: In 1961 which Henry Mancini record won Grammy record of year
Answer: Moon River
Question: My cat is constantly meowing by the door after I let her out once, but it's dangerous for cats to be outdoors unsupervised. What are some ways I can train her to stop meowing?
Answer: There are a few different ways you can train your cat to stop meowing:
- Get a clicker, and whenever she pauses her meowing, click the clicker and give her a treat. The clicker trains her to associate her behavior with the reward.
- Find something else to entertain her, such as a cat toy, or a video of wildlife.
- Build an outdoor enclosure for her, accessible from a cat door. This will allow her to go outside, but in a safe and controller environment.
Question: How many combinations of pre-trained seq-to-seq and token-level saliency models did the authors investigate?
Evidence: Pre-trained language models such as BERT BIBREF0 have significantly improved the accuracy of various language processing tasks. However, we cannot apply BERT to language generation tasks as is because its model structure is not suitable for language generation. Several pre-trained seq-to-seq models for language generation BIBREF1, BIBREF2 based on an encoder-decoder Transformer model, which is a standard model for language generation, have recently been proposed. These models have achieved blackstate-of-the-art results in various language generation tasks, including abstractive summarization.
However, when generating a summary, it is essential to correctly predict which part of the source text should be included in the summary. Some previous studies without pre-training have examined combining extractive summarization with abstractive summarization BIBREF3, BIBREF4. Although pre-trained seq-to-seq models have achieved higher accuracy compared to previous models, it is not clear whether modeling “Which part of the source text is important?” can be learned through pre-training.
blackThe purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task. Our main contributions are as follows:
We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder.
We proposed a new combination model, the conditional summarization model with important tokens (CIT), in which a token sequence extracted by a saliency model is explicitly given to a seq-to-seq model as an additional input text.
We evaluated the combination models on the CNN/DM BIBREF5 and XSum BIBREF6 datasets. Our CIT model outperformed a simple fine-tuned model in terms of ROUGE scores on both datasets.
We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder.
Answer: Nine.
Question: Imagine you are the last person on Mars. Write a diary entry describing your thoughts and feelings.
Answer: Today was a beautiful day on Mars. The sun was shining brightly and the air was crisp and cool. I was able to take in the beauty of the planet and the sky, which I had never seen before. I felt like I was on another world, but I knew I was still in the same place. I took a walk around the dome and marveled at the beauty of the planet. It was so peaceful and I felt like I was in a dream. I smiled as I thought about how much I was learning about Mars and its inhabitants. It was a great day. Pity it could not last.
Question: Is Mount Saint Helens still active?
Evidence: Mount St. Helens (known as Lawetlat'la to the indigenous Cowlitz people, and Loowit or Louwala-Clough to the Klickitat) is an active stratovolcano located in Skamania County, Washington, in the Pacific Northwest region of the United States. It lies 52 miles (83 km) northeast of Portland, Oregon, and 98 miles (158 km) south of Seattle. Mount St. Helens takes its English name from that of the British diplomat Lord St Helens, a friend of explorer George Vancouver who surveyed the area in the late 18th century. The volcano is part of the Cascade Volcanic Arc, a segment of the Pacific Ring of Fire.

The Mount St. Helens major eruption of May 18, 1980 remains the deadliest and most economically destructive volcanic event in U.S. history. Fifty-seven people were killed; 200 homes, 47 bridges, 15 miles (24 km) of railways, and 185 miles (298 km) of highway were destroyed. A massive debris avalanche, triggered by a magnitude 5.1 earthquake, caused a lateral eruption that reduced the elevation of the mountain's summit from 9,677 ft (2,950 m) to 8,363 ft (2,549 m), leaving a 1 mile (1.6 km) wide horseshoe-shaped crater. The debris avalanche was 0.6 cubic miles (2.5 km3) in volume. The 1980 eruption disrupted terrestrial ecosystems near the volcano. By contrast, aquatic ecosystems in the area greatly benefited from the amounts of ash, allowing life to multiply rapidly. Six years after the eruption, most lakes in the area had returned to their normal state.

After its 1980 eruption, the volcano experienced continuous volcanic activity until 2008. Geologists predict that future eruptions will be more destructive, as the configuration of the lava domes requires more pressure to erupt. However, Mount St. Helens is a popular hiking spot and it is climbed year-round. In 1982, the Mount St. Helens National Volcanic Monument was established by President Ronald Reagan and the U.S. Congress.
Answer: Mount St. Helens, located in the sate of Washington is considered an active volcano.
Question: Is Bristol a large city
Evidence: Bristol is the largest city in South West England, and as such is a centre for culture, research and higher education in the region. The city is home to a prestigious "red brick university" (University of Bristol) and a high-ranking "new university" (University of the West of England). The city is also noted for its investment in the sciences and engineering, particularly its ties to the aerospace industry.
Answer: Indeed. Bristol is the largest city in South West England, and as such is a centre for culture, research and higher education in the region. The city is home to a prestigious "red brick university" (University of Bristol) and a high-ranking "new university" (University of the West of England). The city is also noted for its investment in the sciences and engineering, particularly its ties to the aerospace industry.
Question: Given the lineage of this historical military aircraft, when was the Avro Arrow first flown and what were the operating characteristics of that flight?
Evidence: The Avro Canada CF-105 Arrow was a delta-winged interceptor aircraft designed and built by Avro Canada. The CF-105 held the promise of Mach 2 speeds at altitudes exceeding 50,000 feet (15,000 m) and was intended to serve as the Royal Canadian Air Force's (RCAF) primary interceptor into the 1960s and beyond.

The Arrow was the culmination of a series of design studies begun in 1953 that examined improved versions of the Avro Canada CF-100 Canuck. After considerable study, the RCAF selected a dramatically more powerful design, and serious development began in March 1955. The aircraft was intended to be built directly from the production line, skipping the traditional hand-built prototype phase. The first Arrow Mk. 1, RL-201, was rolled out to the public on 4 October 1957, the same day as the launch of Sputnik I.

Flight testing began with RL-201 on 25 March 1958, and the design quickly demonstrated excellent handling and overall performance, reaching Mach 1.9 in level flight. Powered by the Pratt & Whitney J75, another four Mk. 1s were completed, RL-202, RL-203, RL-204 and RL-205. The lighter and more powerful Orenda Iroquois engine was soon ready for testing, and the first Mk 2 with the Iroquois, RL-206, was ready for taxi testing in preparation for flight and acceptance tests by RCAF pilots by early 1959.
Answer: The Avro Canada RL-201 Arrow was first flown in a test flight on March 25, 1958. During that flight, handling and performance proved excellent and the aircraft reached speeds of up to Mach 1.9 in level flight.
Question: Identify which car manufacturer is Japanese or American: Isuzu, Cadillac
Answer: Isuzu is Japanese, Cadillac is American
Question: Given this paragraph, what are the potential advantages or disadvantages of using XGBoost versus a single decision tree?
Evidence: While the XGBoost model often achieves higher accuracy than a single decision tree, it sacrifices the intrinsic interpretability of decision trees.  For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder. To achieve both performance and interpretability, some model compression techniques allow transforming an XGBoost into a single "born-again" decision tree that approximates the same decision function
Answer: One advantage of using XGBoost over a single decision tree is that it frequently achieves higher accuracy. One disadvantage is that it sacrifices interpretability because it is much more difficult to follow the path of hundreds or thousands of trees.
Question: Which positions did Kamerion Wimbley play in High school?
Evidence: Kamerion Wimbley (born October 13, 1983) is a former American football outside linebacker and defensive end in the National Football League (NFL). He was drafted by the Cleveland Browns in the first round of the 2006 NFL Draft with the 13th overall pick. He also played for the Oakland Raiders and Tennessee Titans before retiring in 2015. He played college football at Florida State University.

Early years
Attending Wichita Northwest High School in Kansas, Wimbley played defensive end, linebacker, quarterback, receiver and punter. He earned Parade and SuperPrep All-America honors during his senior season and was considered to be one of the top high school athletes in the nation. He played in the 2002 U.S. Army All-American Bowl.

Considered a four-star recruit by Rivals.com, Wimbley was ranked 58th overall among football prospects of the class of 2002. He selected Florida State over Notre Dame, Nebraska, Oklahoma and Tennessee. Wimbley graduated from Northwest High School in December 2001 (a semester early) and enrolled at Florida State in the spring of 2002.
Answer: Kamerion Wimbley played Football in multiple positions in Wichita Northwest High School in Kansas, including: defensive end, linebacker, quarterback, receiver and punter.