DataHammer/scimrc huggingface dataset:
Question: Since InfoLOOB is an upper bound, a natural question is that why not also try using CLUB (Cheng et al., ICML 2020) as the objective?
Evidence: The “Leave one out upper bound” (Poole et al., 2019) on the mutual information was called “L1Out” in Cheng et al. (2020), while we call it “InfoLOOB” (LOOB for “Leave One Out Bound”). InfoLOOB is the same as InfoNCE (Eq. (3)), but without the positive sample x1 in the denominator. Contrastive Log-ratio Upper Bound (CLUB), another upper bound on the mutual information, was only used for minimizing it (Cheng et al., 2020). Maximizing CLUB failed in experiments, because the embedding
distribution was not uniform as known for similar objectives (Wang & Liu, 2021). Uniform embedding distributions are required for successful contrastive learning (Wang & Isola, 2020).
Answer: Again a valid point. In Cheng et al. (2020) CLUB has been used for minimizing the mutual information as usual for an upper bound. However, learning fails when maximizing the CLUB objective. There is a collapse to a single mode both without and with modern Hopfield networks. After extensive experimentation, we were not able to solve this problem. While CLUB averages over the negatives, InfoLOOB uses a log-sum-exponential (LSE). The LSE avoids a collapse since the sample that is most similar to the anchor is strongly pushed away from the anchor and even away from the average. The LSE leads to a more uniform distribution of features on the hypersphere. Our observations are in agreement with the findings in “Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere” (https://arxiv.org/abs/2005.10242).
We now write in the paper: “Contrastive Log-ratio Upper Bound (CLUB), another upper bound on the mutual information, was only used for minimizing it (Cheng et al., 2020). Maximizing CLUB failed in experiments, because the embedding distribution was not uniform as known for similar objectives (Wang & Liu, 2021). Uniform embedding distributions are required for successful contrastive learning (Wang & Isola, 2020).”
Question: Why does the LD2NL build upon SPARQL2NL?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL.
Answer: Because it is open-source and the paradigm it follows can be reused and ported to RDF and OWL.
Question: Give a comma separated list of all the people listed in this passage about Jesse LaFollette
Evidence: LaFollette was born near Morristown, New Jersey in 1781. His father, Joseph, and grandfather, Jean, were Huguenots who had escaped the persecution in France, traveling first to Jersey and then to the colonies where they operated a small farm near the Wallkill River in northern New Jersey. Jean was killed during the French and Indian Wars. Joseph married Phoebe Gobel of Morristown, New Jersey, whose father's farm along with other neighboring farms in Jockey Hollow was used by George Washington and his troops during the winter of 1780. After serving with Count Casimir Pulaski during the Revolutionary War, Joseph and his family joined the pioneers who trekked westward through the Cumberland Gap.
Answer: Jesse LaFollette, Joseph LaFollette, Jean LaFollette, Phoebe Gobel, George Washington, Casimir Pulaski
Question: What are the four false positive types in this paper?
Evidence: We inspected the tweets of one fold that were misclassified by the Mazajak/SVM model (36 false positives/121 false negatives) to determine the most common errors.
had four main types:
[leftmargin=*]
Gloating: ex. يا هبيده> (“yA hbydp” - “O you delusional”) referring to fans of rival sports team for thinking they could win.
Quoting: ex. لما حد يسب ويقول يا كلب> (“lmA Hd ysb wyqwl yA klb” – “when someone swears and says: O dog”).
Idioms: ex. يا فاطر رمضان يا خاسر دينك> (“yA fATr rmDAn yA xAsr dynk” – “o you who does not fast Ramadan, you who have lost your religion”), which is a colloquial idiom.
Implicit Sarcasm: ex. يا خاين انت عايز تشكك>
في حب الشعب للريس> (“yA KAyn Ant EAwz t$kk fy Hb Al$Eb llrys” – “O traitor, (you) want to question the love of the people to the president ”) where the author is mocking the president's popularity.

had two types:
[leftmargin=*]
Mixture of offensiveness and admiration: ex. calling a girl a puppy يا كلبوبة> (“yA klbwbp” – “O puppy”) in a flirtatious manner.
Implicit offensiveness: ex. calling for cure while implying sanity in: وتشفي حكام قطر من المرض> (“wt$fy HkAm qTr mn AlmrD” – “and cure Qatar rulers from illness”).
Many errors stem from heavy use of dialectal Arabic as well as ambiguity. Since BERT was trained on Wikipedia (MSA) and Google books, the model failed to classify tweets with dialectal cues. Conversely, Mazajak/SVM is more biased towards dialects, often failing to classify MSA tweets.
We inspected the tweets of one fold that were misclassified by the Mazajak/SVM model (36 false positives/121 false negatives) to determine the most common errors. They were as follows: Four false positive types: • Gloating: ex. èYJ J .ë AK (“yA hbydp” - “O you delusional”) referring to fans of rival sports team for thinking they could win. • Quoting: ex. I. Ê¿ AK Èñ_x0010_ ®K ð I. _x001d_ Yg AÖÏ (“lmA Hd ysb wyqwl yA klb” – “when some_x0002_one swears and says: O dog”). • Idioms: ex. ½ JK X QåA g AK àA ÓP Q£A ¯ AK (“yA fATr rmDAn yA xAsr dynk” – “o you who does not fast Ramadan, you have lost your faith”), which is a colloquial idiom.Implicit Sarcasm: ex. _x005f QÊË I. ªË@ I _x0011_ . k ú ¯ ½¾   QK A« I K@ áK A g AK (“yA xAyn Ant EAwz t$kk fy Hb Al$Eb llrys” – “O traitor, (you) want to question people’s love for the president ”) where the author is mocking the president’s popularity.
Answer: Four false positive types are the gloating, the quoting, the idioms and the implicit sarcasm.
Question: Why is the generation-based model designed in the approach？
Evidence: Our framework consists of a question answering model and a question generation model.
We implement two question answering models, which directly measure the semantic similarity between questions and candidate answers in the semantic space. First, to make the model's prediction more explainable, we implement a path based QA model PCNet. In this model, to get candidate answers from the triplets based document for a given question. We first retrieve the an “anchor” point $arg1_i$ or $arg2_i$ in the document fact $f_i$ . These anchors are selected based on edit distance between the words in the questions and the arguments. Then we regard all arguments in the 1-hops and 2-hops fact of the anchors as answers. However, the coverage of the candidate answers can be 100% in the model. We then implement a second end-to-end neural model KVMenNet which covers all the answers but with less interpretability. Both models generate a score $f_{qa}(q, a)$ of each candidate answer.
We then implement a generation-based model. The motivation to design this model is that we want to associate natural language phrases with knowledge based representation. It takes semantics of a candidate answer as the input and generates a question $\hat{q}$ . Then a paraphrasing model gives a score $f_{qg}(q,\hat{q})$ , which is computed between the generated question $\hat{q}$ and the original question $q$ , as the ranking score.
We get the final scores $S(q,a)$ used for ranking as follows. 
$$S(q,a) = \lambda f_{qa}(q, a) + (1-\lambda ) f_{qg}(q,\hat{q})$$   (Eq. 7) 
Moreover, we incorporate side information from external KBs into the three models. The details of how we use external KBs to enhance the representation of elements in the document KB will be described in section Incorporating External Knowledge.
We then implement a generation-based model. The motivation to design this model is that we want to associate natural language phrases with knowledge based representation.
Answer: The motivation to design this model is that they want to associate natural language phrases with knowledge based representation.
Question: What is the best way to get faster at running long distances?
Answer: To become faster at running requires practicing a lot of long slow distance running mixed in with occasional interval training. It's also important to avoid injuries by stretching, investing in good running shoes, and sleeping and eating well.
Question: Are fresh vegetable brought at a grocery store fresher than frozen vegetables?
Answer: It's quite common for a lot of people to assume that fresh vegetables are fresher than frozen vegetables. In fact, many wrongly assume that freezing destroys nutrients. Nothing could be farther from the truth. To survive transportation and storage, fresh vegetables are often harvested prematurely at sub-par nutrition levels. On the other hand, frozen vegetables are harvested close to peak maturity and nutrition levels.

Unless you have your own kitchen garden or have access to a farmers market located close to where the vegetables are actually grown, frozen is a better bet - it's fresher, more convenient and rather crucially, more easily accessible for you to improve the health quotient of your meals. It's better to eat frozen peas all through the year than be puritanical about enjoying it only during winter. Eating healthily is as much a behavior science problem as it is a nutrition science problem, and it's easier to keep frozen peas around and eat it regularly than to try and remember what is currently in season and go out of the way to procure them fresh all the time. Long term convenience always trumps short term will power improvements.
Question: Why are cats better than dogs in some people's opinion?
Answer: There are couple of reasons that people would prefer cats over dogs. The major one is that cats are lower maintenance than dogs. Most cats are happy indoors and doesn't need walks daily. This becomes a huge advantages as many younger generation people prefers more indoor activities today as well. There are couple other advances: 1. Cats are less likely to cause a true mess of your house than dogs due to their smaller size and weaker strength. 2. Cats are less smelly than dogs. 3. Cats are quieter than dogs as they don't bark or howl.
Question: What are the words of House Allyrion?
Answer: "No Foe May Pass"
Question: Is the killers a band
Evidence: The Killers are an American rock band formed in Las Vegas in 2001 by Brandon Flowers (lead vocals, keyboards, bass) and Dave Keuning (lead guitar, backing vocals). After going through a number of short-term bass players and drummers in their early days, both Mark Stoermer (bass, rhythm guitar, backing vocals) and Ronnie Vannucci Jr. (drums, percussion) joined the band in 2002. The band's name is derived from a logo on the bass drum of a fictitious band portrayed in the music video for the New Order song "Crystal".
Answer: The Killers are an American rock band formed in Las Vegas in 2001 by Brandon Flowers (lead vocals, keyboards, bass) and Dave Keuning (lead guitar, backing vocals). After going through a number of short-term bass players and drummers in their early days, both Mark Stoermer (bass, rhythm guitar, backing vocals) and Ronnie Vannucci Jr. (drums, percussion) joined the band in 2002. The band's name is derived from a logo on the bass drum of a fictitious band portrayed in the music video for the New Order song Crystal.
Question: Identify which instrument is string or percussion: Washboard, Schrammel gitarre
Answer: Schrammel gitarre is string, Washboard is percussion.
Question: How many workers were involved in the human judgment of each table and reference pair?
Evidence: In this section we further analyze the performance of PARENT-W under different conditions, and compare to the other best metrics from Table TABREF37 .
To study the correlation as we vary the number of divergent references, we also collected binary labels from workers for whether a reference is entailed by the corresponding table. We define a reference as entailed when it mentions only information which can be inferred from the table. Each table and reference pair was judged by 3 independent workers, and we used the majority vote as the label for that pair. Overall, only INLINEFORM0 of the references were labeled as entailed by the table. Fleiss' INLINEFORM1 was INLINEFORM2 , which indicates a fair agreement. We found the workers sometimes disagreed on what information can be reasonably entailed by the table.
Figure FIGREF40 shows the correlations as we vary the percent of entailed examples in the evaluation set of WikiBio. Each point is obtained by fixing the desired proportion of entailed examples, and sampling subsets from the full set which satisfy this proportion. PARENT and RG-F remain stable and show a high correlation across the entire range, whereas BLEU and BLEU-T vary a lot. In the hyperparams category, the latter two have the worst correlation when the evaluation set contains only entailed examples, which may seem surprising. However, on closer examination we found that this subset tends to omit a lot of information from the tables. Systems which produce more information than these references are penalized by BLEU, but not in the human evaluation. PARENT overcomes this issue by measuring recall against the table in addition to the reference.
We check how different components in the computation of PARENT contribute to its correlation to human judgments. Specifically, we remove the probability INLINEFORM0 of an n-gram INLINEFORM1 being entailed by the table from Eqs. EQREF19 and EQREF23 . The average correlation for PARENT-W drops to INLINEFORM5 in this case. We also try a variant of PARENT with INLINEFORM6 , which removes the contribution of Table Recall (Eq. EQREF22 ). The average correlation is INLINEFORM7 in this case. With these components, the correlation is INLINEFORM8 , showing that they are crucial to the performance of PARENT.
 BIBREF28 point out that hill-climbing on an automatic metric is meaningless if that metric has a low instance-level correlation to human judgments. In Table TABREF46 we show the average accuracy of the metrics in making the same judgments as humans between pairs of generated texts. Both variants of PARENT are significantly better than the other metrics, however the best accuracy is only INLINEFORM0 for the binary task. This is a challenging task, since there are typically only subtle differences between the texts. Achieving higher instance-level accuracies will require more sophisticated language understanding models for evaluation.
Each table and reference pair was judged by 3 independent workers, and we used the majority vote as the label for that pair.
Answer: 3 independent workers.
Question: Why do people visit the national parks?
Answer: The national parks are a system of diverse natural and historical locations that are preserved for people to enjoy. Because there is such variety in the national parks there's sure to be something that interests you. Some parks are made up of vast natural landscapes and wildernesses that can be explored. Others are sanctuaries for wildlife where you can see animals in their natural habitat. Still other locations are on islands or have amazing coastline and includes underwater habitats and wildlife. While some national parks are harder to access, most can be enjoyed by the majority of people. If you hate paying high entrance fees to destination locations the national parks are also a great option. You can get an annual pass for less than $100 which allows you to visit all national parks and many national forests and monuments at no additional charge. However, if you want to stay within the national parks make sure to book well in advance as campsites and lodging fills up quickly. For the most popular parks you'll want to book six months in advance when the reservations open up. The national parks can be enjoyed by people of all ages and have stunning vistas you can drive right up to or week long backpacking trips for adventure seekers.
Question: Why do little boys jump in a puddle of water or mud?
Answer: Because they can. Because it was there. Because they have a sense their mother or father will disapprove so they are naturally inclined to do so.
Question: Write a preface to book where the author feels the need to properly caveat his story about warfare in the middle east, in case someone were to get offended by the content.  Make sure the author conveys his deep respect for that region and all cultures represented therein.
Answer: Though the events described here may have some historic precedent, they are totally fictitious, as are equipment and its use. Similarly, any resemblance between the characters in this story and actual individuals would be totally coincidental. Finally, though the geographical locations are real, the characterization of certain countries is vaguely inspired by reality. One should not conclude that the author's views relative to any of these countries matches the views presented here. The entire Middle East has been a war cauldron for most of its history, and yet it remains both the birthplace of western civilization and the origin of wonderful scientific discoveries.  Additionally, its contribution to the world of art, through the ago has been unquestioned. In short, whoever is painted as a villain today could turn out to be a hero tomorrow.
Question: When was the french revolution
Evidence: The French Revolution (French: Révolution française [ʁevɔlysjɔ̃ fʁɑ̃sɛːz]) was a period of radical political and societal change in France that began with the Estates General of 1789 and ended with the formation of the French Consulate in November 1799.
Answer: The French revolution started in 1789 and ended in November 1799
Question: What is the Mapudungun?
Evidence: Mapudungun (iso 639-3: arn) is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina BIBREF0. It is an isolate language and is classified as threatened by Ethnologue, hence the critical importance of all documentary efforts. Although the morphology of nouns is relatively simple, Mapudungun verb morphology is highly agglutinative and complex. Some analyses provide as many as 36 verb suffix slots BIBREF1. A typical complex verb form occurring in our corpus of spoken Mapudungun consists of five or six morphemes.
Mapudungun has several interesting grammatical properties. It is a polysynthetic language in the sense of BIBREF2; see BIBREF3 for explicit argumentation. As with other polysynthetic languages, Mapudungun has Noun Incorporation; however, it is unique insofar as the Noun appears to the right of the Verb, instead of to the left, as in most polysynthetic languages BIBREF4. One further distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms; that is, while subordinate clauses in Mapudungun closely resemble possessed nominals and may occur with an analytic marker resembling possessor agreement, there is no agreement inflection on the verb itself. One further remarkable property of Mapudungun is its inverse voice system of agreement, whereby the highest agreement is with the argument highest in an animacy hierarchy regardless of thematic role BIBREF5.
Mapudungun (iso 639-3: arn) is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina (Z ´u ˜niga, 2006, 41–3) .
Answer: Mapudungun is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina.
Question: When are storms most common in the North Indian basin?
Evidence: Summer is traditionally associated with hot or warm weather. In Mediterranean climates, it is also associated with dry weather, while in other places (particularly in Eastern Asia because of the monsoon) it is associated with rainy weather. The wet season is the main period of vegetation growth within the savanna climate regime. Where the wet season is associated with a seasonal shift in the prevailing winds, it is known as a monsoon.
In the northern Atlantic Ocean, a distinct tropical cyclone season occurs from 1 June to 30 November. The statistical peak of the Atlantic hurricane season is 10 September. The Northeast Pacific Ocean has a broader period of activity, but in a similar timeframe to the Atlantic. The Northwest Pacific sees tropical cyclones year-round, with a minimum in February and March and a peak in early September. In the North Indian basin, storms are most common from April to December, with peaks in May and November. In the Southern Hemisphere, the tropical cyclone season runs from the start of November until the end of April with peaks in mid-February to early March.
Thunderstorm season in the United States and Canada runs in the spring through summer but sometimes can run as late as October or even November in the fall. These storms can produce hail, strong winds and tornadoes, usually during the afternoon and evening.
Answer: In the North Indian basin, storms are most common from April to December, with peaks in May and November.
Question: What are some board or card games that you recommend for ten year olds?
Answer: Some games that are popular among that demographic are "Uno", "Exploding Kittens", "War", and "Connect 4".
Question: Classify these animals as either mammals, reptiles, birds, or other: king cobra, honey badger, whale, black kite, salmon, and French poodle
Answer: mammals: honey badger, whale, french poodle
reptiles: king cobra
birds: black kite
other: salmon
Question: Give a list of proper nouns in the passage.
Evidence: Kendall Boiler and Tank Company Building is a one-story commercial edifice located on 275 Third Street in Kendall Square, Cambridge, Massachusetts. The brick building was formerly owned by the Kendall Boiler and Tank Company and is part of the Blake and Knowles Steam Pump Company National Register District, on the National Register of Historic Places.
Answer: Kendall Boiler and Tank Company Building
Third Street
Kendall Square
Cambridge
Massachusetts
Kendall Boiler and Tank Company
Blake and Knowles Steam Pump Company National Register District
National Register of Historic Places