DataHammer/scimrc huggingface dataset:
Question: Did you consider cases where there are rare relations?
Evidence: We systematically investigate the effect of three shapes of patterns used in GraphANGEL. For each dataset, we evaluate the effect of the patterns in 3-cycle and 4-cycle shapes by the performance of GraphANGEL 3−cycle and GraphANGEL 4−cycle . Since different shapes of patterns represent different composition logical rules, as shown in Table, patterns in Pair shape are the most general but include the least structure information, while those in 4-cycle shape are rich in the structure but less common. Hence, these patterns have their unique power in representing logics. Although it is hard to determine whether 3-cycle or 4-cycle shaped patterns is more powerful, as shown in Tables and A4, GraphANGEL with patterns in all shapes achieves the best performance. Besides the pattern type, we also investigate how the number of sampled subgraphs affects the performance. Taking Amazon and Douban Book datasets as examples, we show the performance of GraphANGEL under different K and Q in terms of ACC and AUC in Figure. One explanation is that the subgraphs following target patterns are constricted within the neighborhood of source and target nodes, the number of which is much smaller than subgraphs following supporting and refuting patterns.
Table 5: Result comparisons with baselines on generalization setting by randomly removing 20% relations. See Appendix A6.2 for full version and Appendix A6.2 for results of dropping 5%, 10%, 15%. The numbers in brackets show the descent degree.
Answer: We show the experimental results for GraphANGEL and baselines on the 20% least frequent relations in Table 5, where each model is trained with all the relations but tested only with the 20% least frequent relations. Results show that our method can significantly outperform the existing methods in terms of MRR, Hit@1, Hit@3, Hit@10.
Question: Given this paragraph on the Persian language, which are the 2 sub groups of the Western Iranian language?
Evidence: The Western Iranian languages themselves are divided into two subgroups: Southwestern Iranian languages, of which Persian is the most widely spoken, and Northwestern Iranian languages, of which Kurdish and Balochi are the most widely spoken
Answer: Southwestern and Northwestern Iranian language
Question: Why is it a good idea to walk every day?
Answer: Walking is a good exercise for burning calories without causing any damage to joints. People of any age can do it and a few miles a day can result in positive effects on cardiovascular health and general fitness.
Question: Why would a person from Switzerland immigrate to the California?
Answer: The warm climate, the nice weather, the proximity to the ocean in California are all things that Switzerland misses. People are usually more relaxed and friendlier to strangers than in Switzerland. In California has a huge variety of restaurants and grocery stores from all over the world.
But of course a person from Switzerland missed the mountains, excellent public transportation, cheese and chocolate.
Question: What is a homonym?
Answer: Homonyms are words that are pronounced the same but have different meanings. Although the words sound the same when pronounced, they are not spelled the same. An example is the word deer, also spelled dear. These words have very different meanings in the english language. Homonyms can be found in all languages around the world.
Question: What are some of the most popular tourist attractions in Rome?
Answer: Rome, Italy is of the most popular travel destinations in the world. People travel to Rome in particular to visit the Roman Colosseum and the Forum, which are ancient ruins. Tourists often make a quick visit to Vatican City to visit the Vatican Museum, which houses world renowned  pieces of art. The Trevi Fountain and the Piazza Venezia are also quite popular.
Question: What are examples of plants that can successfully grow in the tundra?
Answer: Arctic Moss, Arctic Poppy, Lichens, Labrador Tea, Arctic Willow.
Question: Provide some of the examples of regional governments.
Evidence: Federalism is a combined and compound mode of government that combines a general government (the central or "federal" government) with regional governments (provincial, state, cantonal, territorial, or other sub-unit governments) in a single political system, dividing the powers between the two. Federalism in the modern era was first adopted in the unions of states during the Old Swiss Confederacy.Federalism differs from confederalism, in which the general level of government is subordinate to the regional level, and from devolution within a unitary state, in which the regional level of government is subordinate to the general level. It represents the central form in the pathway of regional integration or separation, bounded on the less integrated side by confederalism and on the more integrated side by devolution within a unitary state.
Answer: provincial, state, cantonal, territorial, or other sub-unit governments
Question: What do people paint?
Answer: Painting is an activity that is for everyone.  Artist paint as a career but regular people also paint because it can be therapeutic, relaxing and a great way to relieve stress.  You don't even have to be good at painting to get all the benefits that come from painting.  Painting is great way to learn to appreciate what is in front of you and what you have created and can even give you a better outlook on life and improve your own spirituality.
Question: What metric do they propose?
Evidence: The task of generating natural language descriptions of structured data (such as tables) BIBREF2 , BIBREF3 , BIBREF4 has seen a growth in interest with the rise of sequence to sequence models that provide an easy way of encoding tables and generating text from them BIBREF0 , BIBREF1 , BIBREF5 , BIBREF6 .
For text generation tasks, the only gold standard metric is to show the output to humans for judging its quality, but this is too expensive to apply repeatedly anytime small modifications are made to a system. Hence, automatic metrics that compare the generated text to one or more reference texts are routinely used to compare models BIBREF7 . For table-to-text generation, automatic evaluation has largely relied on BLEU BIBREF8 and ROUGE BIBREF9 . The underlying assumption behind these metrics is that the reference text is gold-standard, i.e., it is the ideal target text that a system should generate. In practice, however, when datasets are collected automatically and heuristically, the reference texts are often not ideal. Figure FIGREF2 shows an example from the WikiBio dataset BIBREF0 . Here the reference contains extra information which no system can be expected to produce given only the associated table. We call such reference texts divergent from the table.
We show that existing automatic metrics, including BLEU, correlate poorly with human judgments when the evaluation sets contain divergent references (§ SECREF36 ). For many table-to-text generation tasks, the tables themselves are in a pseudo-natural language format (e.g., WikiBio, WebNLG BIBREF6 , and E2E-NLG BIBREF10 ). In such cases we propose to compare the generated text to the underlying table as well to improve evaluation. We develop a new metric, PARENT (Precision And Recall of Entailed N-grams from the Table) (§ SECREF3 ). When computing precision, PARENT effectively uses a union of the reference and the table, to reward correct information missing from the reference. When computing recall, it uses an intersection of the reference and the table, to ignore extra incorrect information in the reference. The union and intersection are computed with the help of an entailment model to decide if a text n-gram is entailed by the table. We show that this method is more effective than using the table as an additional reference. Our main contributions are:
We develop a new metric, PARENT (Precision And Recall of Entailed Ngrams from the Table) (§3).
Answer: PARENT.
Question: From the passage provided find the names of the creators of Kafka. Separate them with a comma.
Evidence: Kafka was originally developed at LinkedIn, and was subsequently open sourced in early 2011. Jay Kreps, Neha Narkhede and Jun Rao helped co-create Kafka. Graduation from the Apache Incubator occurred on 23 October 2012. Jay Kreps chose to name the software after the author Franz Kafka because it is "a system optimized for writing", and he liked Kafka's work.
Answer: Jay Kreps, Neha Narkhede, Jun Rao
Question: What are some common bases for tacos?
Answer: Tacos are typically built on top of a soft wheat tortilla, soft corn tortilla, hard taco shell, lettuce, or a baked taco shell.
Question: Is the model evaluated against any baseline?
Evidence: The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. For the former, we reach our best result by using as aligned information the French, the original aligned language for this dataset. Languages closely related to French (Spanish and Portuguese) ranked better, while our worst result used German. English also performs notably well in our experiments. We believe this is due to the statistics features of the resulting text. We observe in Table that the English portion of the dataset contains the smallest vocabulary among all languages. Since we train our systems in very low-resource settings, vocabulary-related features can impact greatly the system's capacity to language-model, and consequently the final quality of the produced alignments. Even in high-resource settings, it was already attested that some languages are more difficult to model than others BIBREF9.
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. For the former, we reach our best result by using as aligned information the French, the original aligned language for this dataset. Languages closely related to French (Spanish and Portuguese) ranked better, while our worst result used German. English also performs notably well in our experiments. We believe this is due to the statistics features of the resulting text. We observe in Table that the English portion of the dataset contains the smallest vocabulary among all languages. Since we train our systems in very low-resource settings, vocabulary-related features can impact greatly the system's capacity to language-model, and consequently the final quality of the produced alignments. Even in high-resource settings, it was already attested that some languages are more difficult to model than others BIBREF9.
Answer: No.
Question: In the series A Song of Ice and Fire, who is the founder of House Crakehall?
Answer: Crake the Boarkiller
Question: Why do some countries outperform other countries in certain sports despite having a smaller population?
Answer: A country's success in a given sport at the international level will depend on a number of factors other than population alone. While a country's population is a good identifier for the total selection pool for high performing athletes to come from, it is one of many factors. A country's founding history, climate zone, and GDP are other notable factors in sport performance. 

A nations history plays factor as many sports are baked in centuries of tradition. For example, Cricket, which is believed to have originated in England is most popular in countries that were originally British Colonies. Long traditions such as this set specific sports into the culture of a nation, increasing the percentage of the athlete pool that it can pull from and can increase the level at which that nation is competitive. 

Climate zone can also influence the competitiveness of a country in given sports. Norway, fore example, earned the most olympic medals of any country in the 2022 olympic games with a total of 37 medals. In the summer games of 2020 (held in 2021), Norway ranked 20th overall with just 8 medals. Norways Climate and Topography have led winter sports to be extremely popular, and Norway has over time developed elite programs in cross country/nordic skiing, speed skating, curling, biathlon, and ski jumping. 

A nations wealth will also influence performance in sport at the international level. This occurs predominantly for two reasons. First is that more money enables larger budgets on equipment, coaching, player development, participation in international circuits, data insights, medical staff etc. that over time create a performance separation from those who can not afford these things. Second is access to sports, many sports are quite expensive some examples include snow sports, gymnastics, swimming, hockey, and polo. Nations which can not afford to provide the facilities or support to develop athletes will often fail to draw a large talent pool and not be competitive at the international level.
Question: Who founded The Heart Institute (InCor) in São Paulo?
Evidence: The Heart Institute, University of São Paulo (Instituto do Coração da Universidade de São Paulo, or InCor) is one of the clinical institutes of the central University's teaching hospital (Hospital das Clínicas da Universidade de São Paulo) and recognized as one of the world's academic and clinical excellence centers in cardiology, cardiovascular medicine and cardiovascular surgery.

The Heart Institute was founded in 1963 (60 years ago) by the noted cardiovascular surgeon and professor of the Faculty of Medicine of the University of São Paulo, Dr. Euryclides de Jesus Zerbini.
Answer: The Heart Institute (InCor) was founded by Dr. Euryclides de Jesus Zerbini.
Question: What do Catholics believe?
Answer: "Our profession of faith begins with God, for God is the First and the Last, the beginning and the end of everything. The Credo begins with God the Father, for the Father is the first divine person of the Most Holy Trinity; our Creed begins with the creation of heaven and earth, for creation is the beginning and the foundation of all God's works."

---the Catechism of the Catholic Church, no. 198

Catholic belief is succinctly expressed in the profession of faith or credo called the Nicene Creed:

The Nicene Creed
I believe in one God,
the Father almighty,
maker of heaven and earth, of all things visible and invisible.

I believe in one Lord Jesus Christ, the Only Begotten Son of God, born of the Father before all ages. God from God, Light from Light, true God from true God, begotten, not made, consubstantial with the Father; through him all things were made. For us men and for our salvation he came down from heaven, and by the Holy Spirit was incarnate of the Virgin Mary, and became man. For our sake he was crucified under Pontius Pilate, he suffered death and was buried, and rose again on the third day in accordance with the Scriptures. He ascended into heaven and is seated at the right hand of the Father. He will come again in glory to judge the living and the dead and his kingdom will have no end.

I believe in the Holy Spirit, the Lord, the giver of life, who proceeds from the Father and the Son, who with the Father and the Son is adored and glorified, who has spoken through the prophets.

I believe in one, holy, catholic and apostolic Church. I confess one Baptism for the forgiveness of sins and I look forward to the resurrection of the dead and the life of the world to come. Amen.
Question: What are the two major bacterial pathogens of Anaplasma bacteria?
Evidence: Anaplasmosis is a tick-borne disease affecting ruminants, dogs, and horses, and is caused by Anaplasma bacteria. Anaplasmosis is an infectious but not contagious disease. Anaplasmosis can be transmitted through mechanical and biological vector processes. Anaplasmosis can also be referred to as "yellow bag" or "yellow fever" because the infected animal can develop a jaundiced look. Other signs of infection include weight loss, diarrhea, paleness of the skin, aggressive behavior, and high fever.

Many different tick species can carry the bacteria that cause anaplasmosis. The two major bacterial pathogens are Anaplasma marginale and Anaplasma phagocytophilum. These microorganisms are Gram-negative, and infect red blood cells. Once the host is infected with anaplasmosis, the immune system will try to fight off and kill the infected red blood cells, but will also kill healthy red blood cells. The Anaplasma sparouinense species is responsible for a rare zoonosis, the Sparouine anaplasmosis, detected only in French Guiana, South America. This disease was described from a clandestine gold miner working deep in rainforest. Infection of his red blood cells led to a severe deterioration of his health and required his hospitalization. Molecular typing showed that Anaplasma sparouinense is distinct to all known species and more genetically related to recently described Anaplasma species causing infections in rainforest wild fauna of Brazil.

While there are no current live or inactivated vaccines effective for all strains of A. marginale approved by the USDA for anaplasmosis, there are other means of prevention. Tick and fly control for herds of ruminants can be effective but also labor intensive. Chemical methods can also be used, including sanitizing surgical equipment after each use. Tetracycline drugs are the most common treatment for anaplasmosis, and can provide the animal with immunity for a period of time. The disease is more common in the South and West parts of the United States, but is no longer considered a major problem since the use of tetracycline drugs.
Answer: The two major bacterial pathogens are Anaplasma marginale and Anaplasma phagocytophilum. These microorganisms are Gram-negative, and infect red blood cells.
Question: Where can I find a bristly bellflower in the wild?
Evidence: Campanula cervicaria, also known as the bristly bellflower, is a species of flowering plant in the bellflower family, Campanulaceae. Native to Scandinavia and central Europe, C. cervicaria has also become naturalised in parts of the U.S. state of Minnesota. Its natural habitat is woodland edges, hillside meadows, dry meadows and banks, and it also flourishes where soil has been disturbed, for example after slash-and-burn or forest clearance. The plant has a hairy stem, with light blue flowers about 1 to 2 centimetres (0.4 to 0.8 in) long and grouped together. This C. cervicaria flower was photographed in Keila, Estonia.
Answer: The bristly bellflower (Campanula cervicaria) is native to Scandinavia and Central Europe. You can find it in hillside meadows and woodland edges. It flourishes in places where soil has been upturned.
Question: What is new combination model proposed by authors?
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 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.
Answer: The authors 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.
Question: How do we get rainbows?
Answer: The electromagnetic spectrum is made of light with many different wavelengths consisting of a band of colors that include red, orange, yellow, green, blue, indigo and violet. White light is how our eyes perceive all the colors of the rainbow mixed together. Sunlight appears white. When sunlight hits a rain droplet, some of the light is reflected. They are reflected at a different angles separating the spectrum and producing a rainbow.
Question: What are their baselines?
Evidence: We train our MTNet with Adam optimizer for 20 epochs. We set batch size as 8, and weight decay as 1e-5, we adopt 3 branched layers in MTNet. Detailed configuration is shown in Table TABREF21 . We use ReLU in shared layers, and relu6 in branched layers to prevent information loss. Our proposed MTNet achieves 0.2250 on MAE and 0.3216 on RMSE, respectively.
We compare MTNet with other mainstream regression algorithms BIBREF14 (linear regression, KNN, SVR, Random Forest and MLP). The architecture of MLP is 15-16-8-8-1, where each number represents the number of neurons in each layer. We try three kinds of kernels (RBF kernel, linear kernel, and poly kernel) with SVR in our experiments for fair comparison.
The results are listed in Table TABREF37 . Our method achieves the best performance in contrast to the compared baseline regressors.
We compare MTNet with other mainstream regression algorithms [1] (linear regression, KNN, SVR, Random Forest and MLP).
Answer: Linear regression, KNN, SVR, Random Forest and MLP.