DataHammer/scimrc huggingface dataset:
Question: What ability do flexible parameterised model families they specify have?
Evidence: The scientific process by which the deep learning research community operates is guided by empirical studies that evaluate the relative quality of models. Complicating matters, the measured performance of a model depends not only on its architecture (and data), but it can strongly depend on hyperparameter values that affect learning, regularisation, and capacity. This hyperparameter dependence is an often inadequately controlled source of variation in experiments, which creates a risk that empirically unsound claims will be reported.
In this paper, we use a black-box hyperparameter optimisation technique to control for hyperparameter effects while comparing the relative performance of language modelling architectures based on LSTMs, Recurrent Highway Networks BIBREF0 and NAS BIBREF1 . We specify flexible, parameterised model families with the ability to adjust embedding and recurrent cell sizes for a given parameter budget and with fine grain control over regularisation and learning hyperparameters.
Once hyperparameters have been properly controlled for, we find that LSTMs outperform the more recent models, contra the published claims. Our result is therefore a demonstration that replication failures can happen due to poorly controlled hyperparameter variation, and this paper joins other recent papers in warning of the under-acknowledged existence of replication failure in deep learning BIBREF2 , BIBREF3 . However, we do show that careful controls are possible, albeit at considerable computational cost.
Several remarks can be made in light of these results. First, as (conditional) language models serve as the central building block of many tasks, including machine translation, there is little reason to expect that the problem of unreliable evaluation is unique to the tasks discussed here. However, in machine translation, carefully controlling for hyperparameter effects would be substantially more expensive because standard datasets are much larger. Second, the research community should strive for more consensus about appropriate experimental methodology that balances costs of careful experimentation with the risks associated with false claims. Finally, more attention should be paid to hyperparameter sensitivity. Models that introduce many new hyperparameters or which perform well only in narrow ranges of hyperparameter settings should be identified as such as part of standard publication practice.
We specify flexible, parameterised model families with the ability to adjust embedding and recurrent cell sizes for a given parameter budget and with fine grain control over regularisation and learning hyperparameters.
Answer: The ability to adjust embedding and recurrent cell sizes for a given parameter budget and with fine grain control over regularisation and learning hyperparameters.
Question: How do sailplanes (gliders) stay aloft?
Answer: Gliders are usually towed to altitude by a motorized airplane and then use rising air from natural weather phenomenas such as thermals, mountain lee waves or ridge lift to stay aloft.
Question: Classify each of the following as a penalty or scoring play in football: touchdown, holding, offsides, safety, field goal, false start
Answer: Touchdown: Scoring Play
Holding: Penalty
Offsides: Penalty
Safety: Scoring Play
Field Goal: Scoring Play
False Start: Penalty
Question: Why is a sink token added to the task?
Evidence: We experiment with three ways of setting the fertility of the source words: constant, guided, and predicted. With constant, we set the fertilities of all source words to a fixed integer value INLINEFORM0 . With guided, we train a word aligner based on IBM Model 2 (we used fast_align in our experiments, BIBREF17 ) and, for each word in the vocabulary, we set the fertilities to the maximal observed value in the training data (or 1 if no alignment was observed). With the predicted strategy, we train a separate fertility predictor model using a bi-LSTM tagger. At training time, we provide as supervision the fertility estimated by fast_align. Since our model works with fertility upper bounds and the word aligner may miss some word pairs, we found it beneficial to add a constant to this number (1 in our experiments). At test time, we use the expected fertilities according to our model.
The reason we add this token is the following: without the sink token, the length of the generated target sentence can never exceed P j fj words if we use constrained softmax/sparsemax. At training time this may be problematic, since the target length is fixed and the problems in Eqs. 4–6 can become infeasible. By adding the sink token we guarantee P j fj = ∞, eliminating the problem.
Answer: The reason they add this token is the following: without the sink token, the length of the generated target sentence can never exceed a limitation of words if they use constrained softmax/sparsemax.  By adding the sink token they eliminate the problem.
Question: What intrinsic evaluation metrics are used?
Evidence: Tests and Gold-Standard Data for Intrinsic Evaluation
Using the gold standard data (described below), we performed three types of tests:
Class Membership Tests: embeddings corresponding two member of the same semantic class (e.g. “Months of the Year", “Portuguese Cities", “Smileys") should be close, since they are supposed to be found in mostly the same contexts.
Class Distinction Test: this is the reciprocal of the previous Class Membership test. Embeddings of elements of different classes should be different, since words of different classes ere expected to be found in significantly different contexts.
Word Equivalence Test: embeddings corresponding to synonyms, antonyms, abbreviations (e.g. “porque" abbreviated by “pq") and partial references (e.g. “slb and benfica") should be almost equal, since both alternatives are supposed to be used be interchangeable in all contexts (either maintaining or inverting the meaning).
Therefore, in our tests, two words are considered:
distinct if the cosine of the corresponding embeddings is lower than 0.70 (or 0.80).
to belong to the same class if the cosine of their embeddings is higher than 0.70 (or 0.80).
equivalent if the cosine of the embeddings is higher that 0.85 (or 0.95).
Tests and Gold-Standard Data for Intrinsic Evaluation
Using the gold standard data (described below), we performed three types of tests:

Class Membership Tests: embeddings corresponding two member of the same semantic class (e.g. “Months of the Year", “Portuguese Cities", “Smileys") should be close, since they are supposed to be found in mostly the same contexts.

Class Distinction Test: this is the reciprocal of the previous Class Membership test. Embeddings of elements of different classes should be different, since words of different classes ere expected to be found in significantly different contexts.

Word Equivalence Test: embeddings corresponding to synonyms, antonyms, abbreviations (e.g. “porque" abbreviated by “pq") and partial references (e.g. “slb and benfica") should be almost equal, since both alternatives are supposed to be used be interchangeable in all contexts (either maintaining or inverting the meaning).

Therefore, in our tests, two words are considered:

distinct if the cosine of the corresponding embeddings is lower than 0.70 (or 0.80).

to belong to the same class if the cosine of their embeddings is higher than 0.70 (or 0.80).

equivalent if the cosine of the embeddings is higher that 0.85 (or 0.95).
Answer: The answers are shown as follows:
* Class Membership Tests
* Class Distinction Test
* Word Equivalence Test
Question: Extract the foreign nationalities living in the city of Lugo, Spain, and list them using bullets in the format {Nationality} - {Percentage} separated by a comma.
Evidence: Lugo (Galician pronunciation: [ˈluɣʊ], Spanish pronunciation: [ˈluɣo]; Latin: Lucus Augusti) is a city in northwestern Spain in the autonomous community of Galicia. It is the capital of the province of Lugo. The municipality had a population of 98,025 in 2018, making it the fourth most populous city in Galicia.

The population of the city in 2018 was 98,026 inhabitants, which has been growing constantly since the first census in 1842, despite the fact that the rest of the province is losing population dramatically. The population of the city in 2014 was 98,560 inhabitants (45,948 men and 52,612 women). From INE (Instituto Nacional de Estadística).

By April 2020, the population had grown to 99,638. Reaching 100,000 inhabitants is one of the goals of the city Mayor, Lara Méndez.

In 2010 there were 5,373 foreigners living in the city, representing 5.5% of the total population. The main nationalities are Colombians (18%), Moroccans (12%) and Brazilians (11%).
Answer: {Colombians} - {18%}, {Moroccans} - {12%}, {Brazilians} - {12%}
Question: What are the other models they compare to?
Evidence: In this subsubsection, each of the three raw data sets (associated with their labels) shown in Table 1 is used. The clause data are not used. In other words, the training data used in this subsubsection are the same as those used in previous studies. For each data corpus, 1000 raw data samples are used as the test data, and the rest are used as the training data. The involved algorithms are detailed as follows.
CNN-C denotes the CNN with (Chinese) character embedding.
CNN-W denotes the CNN with (Chinese) word embedding.
CNN-Lex-C denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) character embedding is used.
CNN-Lex-W denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) word embedding is used.
Bi-LSTM-C denotes the BI-LSTM with (Chinese) character embedding.
Bi-LSTM-W denotes the Bi-LSTM with (Chinese) word embedding.
Lex-rule denotes the rule-based approach shows in Fig. 1. This approach is unsupervised.
BOW denotes the conventional algorithm which is based of bag-of-words features.
The involved algorithms are detailed as follows.

CNN-C denotes the CNN with (Chinese) character embedding.

CNN-W denotes the CNN with (Chinese) word embedding.

CNN-Lex-C denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) character embedding is used.

CNN-Lex-W denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) word embedding is used.

Bi-LSTM-C denotes the BI-LSTM with (Chinese) character embedding.

Bi-LSTM-W denotes the Bi-LSTM with (Chinese) word embedding.

Lex-rule denotes the rule-based approach shows in Fig. 1. This approach is unsupervised.

BOW denotes the conventional algorithm which is based of bag-of-words features.
Answer: The answers are shown as follows:
* CNN-C
* CNN-W
* CNN-Lex-C
* CNN-Lex-W
* Bi-LSTM-C 
* Bi-LSTM-W
* Lex-rule
* BOW
Question: Given this paragraph about a Tree well, why is it so dangerous for hikers, snowboarder, skiers and any snow practitioners
Evidence: A tree well, also known as a spruce trap, is the space around a tree under its branches that does not get the same amount of snow as the surrounding open space. This creates a void or area of loose snow below the branches and around the trunk that is dangerous to any hikers, snowshoers, skiers, snowboarders, and snowmobilers who fall into them. If someone lands in such a well, often as a result of a fall, it can be too deep for them to climb up the surrounding loose snow before they are buried. Making the situation more dangerous, they often fall into the well head-first and as the result of an accident which could leave them injured or unconscious
Answer: It is dangerous because it creates an area of loose snow below the branches and around the trunk, and when someone will fall into this area it can be too deep for this person to climb up before they are buried. Another dangerous aspect is the fact that they could fall head-first which could leave them injured or unconscious.
Question: Classify each of the following as either a city or a country: venice, rome, venezuela, vatican city, india, brazil, mexico city, mexico, NYC, china, taiwan, london
Answer: Cities:venice, rome,mexico city,NYC,london
Countries: venezuela, vatican city, india, brazil,china, taiwan
Question: Do they analyze if their system has any bias?
Evidence: Last but not least, ethics and fairness are important considerations, that deserve to be studied. In that sense, detection of individual and global bias should be prioritized in order to give useful feedbacks to practitioners. Furthermore we are considering using adversarial learning as in BIBREF33 in order to ensure fairness during the training process.
Last but not least, ethics and fairness are important considerations, that deserve to be studied. In that sense, detection of individual and global bias should be prioritized in order to give useful feedbacks to practitioners. Furthermore we are considering using adversarial learning as in BIBREF33 in order to ensure fairness during the training process.
Answer: No.
Question: Is this a span-based (extractive) QA task?
Evidence: We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
Answer: Yes.
Question: What was the relationship of the person who became MD at Grandmaster Capital Management to Paypal?
Evidence: Individuals whom the media refers to as members of the PayPal Mafia include:

Peter Thiel, PayPal founder and former chief executive officer who is sometimes referred to as the "don" of the PayPal Mafia
Max Levchin, founder and chief technology officer at PayPal
Elon Musk, co-founder of Zip2, founder of X.com which merged with Confinity to form PayPal. Musk later founded SpaceX, co-founded Tesla, Inc., co-founded OpenAI, Neuralink, founded The Boring Company, and became owner of Twitter, Inc.
David O. Sacks, former PayPal COO who later founded Geni.com and Yammer
Scott Banister, early advisor and board member at PayPal.
Roelof Botha, former PayPal CFO who later became a partner and Senior Steward of venture capital firm Sequoia Capital
Steve Chen, former PayPal engineer who co-founded YouTube.
Reid Hoffman, former executive vice president who later founded LinkedIn and was an early investor in Facebook, Aviary
Ken Howery, former PayPal CFO who became a partner at Founders Fund
Chad Hurley, former PayPal web designer who co-founded YouTube
Eric M. Jackson, who wrote the book The PayPal Wars and became chief executive officer of WND Books and co-founded CapLinked
Jawed Karim, former PayPal engineer who co-founded YouTube
Jared Kopf, former PayPal (executive assistant to Peter Thiel) who co-founded Slide, HomeRun and NextRoll
Dave McClure, former PayPal marketing director, a super angel investor for start up companies
Andrew McCormack, co-founder of Valar Ventures
Luke Nosek, PayPal co-founder and former vice president of marketing and strategy, became a partner at Founders Fund with Peter Thiel and Ken Howery
Keith Rabois, former executive at PayPal who later worked at LinkedIn, Slide, Square, Khosla Ventures, and currently with Peter Thiel at Founders Fund, and personally invested in Tokbox, Xoom, Slide, LinkedIn, Geni, Room 9 Entertainment, YouTube, and Yelp
Jack Selby, former vice president of corporate and international development at PayPal who co-founded Clarium Capital with Peter Thiel, later becoming managing director of Grandmaster Capital Management
Premal Shah, former product manager at PayPal, became the founding president of Kiva.org
Russel Simmons, former PayPal engineer who co-founded Yelp Inc.
Jeremy Stoppelman, former vice president of technology at PayPal who later co-founded Yelp
Yishan Wong, former engineering manager at PayPal, later worked at Facebook and became the CEO of Reddit
Answer: Jack Selby was the former vice president of corporate and international development at PayPal and later became the managing director of Grandmaster Capital Management
Question: What dataset is used?
Evidence: We evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12. The interaction behavior collected in the dataset is consistent with our definition in SECREF3. As they did for their study, we only kept users who have at least three interactions (i.e., posting at least three fact-checking messages containing fact-checking URLs). We conducted additional preprocessing step by removing users whose posts are non-English, or their tweets were inaccessible, because some of our baselines require a fact-checker's tweets. Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset. And we further take available feature values of both user and URL into consideration. For instance, a category of referred fact-checking article and the name of corresponding fact-checking website reveals linguistic characteristics such as writing style and topical interest of each URL; while the number of followers and number of followees of each user indicates the credibility and influence of the fact-checker. Statistics of the final dataset is presented in Table TABREF65.
We evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12.
Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset.
Answer: The answers are shown as follows:
* Twitter dataset obtained from the authors of BIBREF12
Question: What are the characteristics of the city dialect?
Evidence: After averaging over all concepts, we lose information on the lexical variation that each concept presents but on the other hand one can now investigate which regions show similar geolectal variation, yielding well defined linguistic varieties. Those cells that have similar colors in either figure FIGREF16 or figure FIGREF17 are expected to be ascribed to the same dialect zone. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. The character of its vocabulary is more uniform as compared with the purple group. While the purple cluster prefer particular utterances, the lexicon of the urban group includes most of the keywords. Importantly, we emphasize that both distance measures (cosine similarity and Jensen-Shanon) give rise to the same result, with little discrepancies on the numerical values that are not significant. The presence of two Twitter superdialects (urban and rural) has been recently suggested BIBREF10 based on a machine learning approach. Here, we arrive at the same conclusion but with a totally distinct model and corpus. The advantage of our proposal is that it may serve as a useful tool for dialectometric purposes.
After averaging over all concepts, we lose information on the lexical variation that each concept presents but on the other hand one can now investigate which regions show similar geolectal variation, yielding well defined linguistic varieties. Those cells that have similar colors in either figure FIGREF16 or figure FIGREF17 are expected to be ascribed to the same dialect zone. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. The character of its vocabulary is more uniform as compared with the purple group. While the purple cluster prefer particular utterances, the lexicon of the urban group includes most of the keywords.
Answer: Lexicon of the cities tend to use most forms of a particular concept
Question: What type of baseline are established for the two datasets?
Evidence: The series of TempEval competitions BIBREF21 , BIBREF22 , BIBREF23 have attracted many research interests in predicting event temporal relations. Early attempts by BIBREF24 , BIBREF21 , BIBREF25 , BIBREF26 only use pair-wise classification models. State-of-the-art local methods, such as ClearTK BIBREF27 , UTTime BIBREF28 , and NavyTime BIBREF29 improve on earlier work by feature engineering with linguistic and syntactic rules. As we mention in the Section 2, CAEVO is the current state-of-the-art system for feature-based temporal event relation extraction BIBREF10 . It's widely used as the baseline for evaluating TB-Dense data. We adopt it as our baseline for evaluating CaTeRS and RED datasets. Additionally, several models BramsenDLB2006, ChambersJ2008, DoLuRo12, NingWuRo18, P18-1212 have successfully incorporated global inference to impose global prediction consistency such as temporal transitivity.
As we mention in the Section 2, CAEVO is the current state-of-the-art system for feature-based temporal event relation extraction BIBREF10 . It's widely used as the baseline for evaluating TB-Dense data. We adopt it as our baseline for evaluating CaTeRS and RED datasets.
Answer: The answers are shown as follows:
* CAEVO
Question: Which is the mighty river that begins at Lake Itasca in Northern Minnesota
Answer: The Mississippi River
Question: Name at least four different types of freshwater waterways
Answer: Rivers, creeks, canals and lakes
Question: Who sings the song "Riptide"?
Answer: Vance Joy sings "Riptide"
Question: Give me a list of suggestions on how I can reduce cat allergens in my home.
Answer: - Vacuum your house every day
- Use a MERV 11 (or higher) filter in your HVAC system and/or portable air purifier
- Minimize the use of carpet so that dander and fur won't linger longer
- Take an antihistamine every day
- Use pet-safe hypoallergenic wipes on your cat every day
- Brush your cat at least three times a week
- Wash your bed sheets and pillowcases weekly
- Don't let your cat sleep in your bedroom