DataHammer/scimrc huggingface dataset:
Question: Is the proposed layer smaller in parameters than a Transformer?
Evidence: FLOAT SELECTED: Table 3: Tokenized case-sensitive BLEU (in parentheses: sacreBLEU) on WMT14 En-De translation task. #Param: number of model parameters. 4Dec: decoding time (seconds)/speedup on newstest2014 dataset with a batch size of 32. 4Train: training time (seconds)/speedup per training step evaluated on 0.5K steps with a batch size of 1K target tokens. Time is averaged over 3 runs using Tensorflow on a single TITAN X (Pascal). “-”: optimization failed and no result. “?”: the same as model 1©. † and ‡: comparison against 11© and 14© respectively rather than 1©. Base: the baseline Transformer with base setting. Bold indicates best BLEU score. dpa and dpr: dropout rate on attention weights and residual connection. bs: batch size in tokens.
FLOAT SELECTED: Table 5: Translation results on different tasks. Settings for BLEU score is given in Section 7.1. Numbers in bracket denote chrF score. Our model outperforms the vanilla base Transformer on all tasks. “Ours”: DS-Init+MAtt.
FLOAT SELECTED: Table 3: Tokenized case-sensitive BLEU (in parentheses: sacreBLEU) on WMT14 En-De translation task. #Param: number of model parameters. 4Dec: decoding time (seconds)/speedup on newstest2014 dataset with a batch size of 32. 4Train: training time (seconds)/speedup per training step evaluated on 0.5K steps with a batch size of 1K target tokens. Time is averaged over 3 runs using Tensorflow on a single TITAN X (Pascal). “-”: optimization failed and no result. “?”: the same as model 1©. † and ‡: comparison against 11© and 14© respectively rather than 1©. Base: the baseline Transformer with base setting. Bold indicates best BLEU score. dpa and dpr: dropout rate on attention weights and residual connection. bs: batch size in tokens.
FLOAT SELECTED: Table 5: Translation results on different tasks. Settings for BLEU score is given in Section 7.1. Numbers in bracket denote chrF score. Our model outperforms the vanilla base Transformer on all tasks. “Ours”: DS-Init+MAtt.
Answer: No.
Question: Tell me about the history of Data Warehousing. Make it entertaining
Answer: Data Warehousing is an evolution of data storage that began in the 1960s. The original goal was to store data in a centralized location for easier retrieval and analysis. This was achieved through the development of databases and the introduction of data warehouses. In the early 2000s, data warehousing evolved into a more advanced process that enables organizations to analyze data in real-time and make better decisions. Today, data warehousing is used to store, manage, and analyze data to help organizations make better decisions and improve their business processes.
Question: How can I be more productive?
Answer: The best way to be productive is to manage time. Try to assign time to all your tasks. Complete the tasks on high priority first. Do not procrastinate.
Question: On which dataset is model trained?
Evidence: For evaluating the proposed model on behavior related data, we employ the Couples Therapy Corpus (CoupTher) BIBREF21 and Cancer Couples Interaction Dataset (Cancer) BIBREF22. These are the targeted conditions under which a behavior-gated language model can offer improved performance.
We utilize the Couple's Therapy Corpus as an in-domain experimental corpus since our behavior classification model is also trained on the same. The RNNLM architecture is similar to BIBREF1, but with hyperparameters optimized for the couple's corpus. The results are tabulated in Table TABREF16 in terms of perplexity. We find that the behavior gated language models yield lower perplexity compared to vanilla LSTM language model. A relative improvement of 2.43% is obtained with behavior gating on the couple's data.
For evaluating the proposed model on behavior related data, we employ the Couples Therapy Corpus (CoupTher) BIBREF21 and Cancer Couples Interaction Dataset (Cancer) BIBREF22. These are the targeted conditions under which a behavior-gated language model can offer improved performance.
We utilize the Couple's Therapy Corpus as an in-domain experimental corpus since our behavior classification model is also trained on the same. 
Answer: The answers are shown as follows:
* Couples Therapy Corpus (CoupTher) BIBREF21
Question: What are calibration frames in astrophotography?
Answer: Calibration frames are additional exposures used to improve the quality of astrophotography images and reduce noise from outside sources. There are three common types of calibration frames used, Dark Frames, Bias Frames, and Flat Frames. From thermal noise and hot pixels to sensor readout noise and uneven field illumination, each type of calibration frame tries to reduce a specific type of noise from the initial images. These calibration frames are normally taken alongside your images while the camera is in the same configuration and temperature and are then subtracted from the initial images reducing the noise and improving the quality.
Question: Why do firms advertise? Even when goods are interchangeable?
Answer: Many firms advertise their goods or services, but are they wasting economic resources?
Some economists reckon that advertising merely manipulates consumer tastes and
creates desires that would not otherwise exist. By increasing product differentiation and
encouraging brand loyalty advertising may make consumers less price sensitive,
moving the market further from perfect competition towards imperfect competition
(see monopolistic competition) and increasing the ability of firms to charge more than
marginal cost. Heavy spending on advertising may also create a barrier to entry, as a
firm entering the market would have to spend a lot on advertising too.
Question: Are there any concerns related to the quality of NMT?
Evidence: Neural machine translation (NMT) emerged in the last few years as a very successful paradigm BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . While NMT is generally more fluent than previous statistical systems, adequacy is still a major concern BIBREF4 : common mistakes include dropping source words and repeating words in the generated translation.
Previous work has attempted to mitigate this problem in various ways. BIBREF5 incorporate coverage and length penalties during beam search—a simple yet limited solution, since it only affects the scores of translation hypotheses that are already in the beam. Other approaches involve architectural changes: providing coverage vectors to track the attention history BIBREF6 , BIBREF7 , using gating architectures and adaptive attention to control the amount of source context provided BIBREF8 , BIBREF9 , or adding a reconstruction loss BIBREF10 . BIBREF11 also use the notion of fertility implicitly in their proposed model. Their “fertility conditioned decoder” uses a coverage vector and an “extract gate” which are incorporated in the decoding recurrent unit, increasing the number of parameters.
In this paper, we propose a different solution that does not change the overall architecture, but only the attention transformation. Namely, we replace the traditional softmax by other recently proposed transformations that either promote attention sparsity BIBREF12 or upper bound the amount of attention a word can receive BIBREF13 . The bounds are determined by the fertility values of the source words. While these transformations have given encouraging results in various NLP problems, they have never been applied to NMT, to the best of our knowledge. Furthermore, we combine these two ideas and propose a novel attention transformation, constrained sparsemax, which produces both sparse and bounded attention weights, yielding a compact and interpretable set of alignments. While being in-between soft and hard alignments (Figure FIGREF20 ), the constrained sparsemax transformation is end-to-end differentiable, hence amenable for training with gradient backpropagation.
To sum up, our contributions are as follows:
While NMT is generally more fluent than previous sta-tistical systems, adequacy is still a major concern (Koehn and Knowles, 2017): common mis-takes include dropping source words and repeating words in the generated translation.
Answer: Yes. There are common mis-takes include dropping source words and repeating words in the generated translation.
Question: How easy and safe is it to replace the brakes on your car by yourself?
Answer: Brakes on a car are an essential system that requires the work to be done with confidence.  Unlike other systems in a car that can fail and not adversely affect safety of a car, the brakes are a critical safety system that must be done correctly.  Failure to repair brakes correctly can result in accidents and damage to property or loss of life in extreme cases.  With this said, however, the work required to replace brakes on a car is not that technically complicated.  In most cases specialized tools are not needed and most cars have a very similar mechanism to engage brakes making repairs easier.  You can consult other sources online to find detailed descriptions for your make and model.  Several tips include picking quality parts that are designed for your vehicle, always replacing brake rotors along with brake pads and double checking the tightness of caliper pins.  With some basic skills and proper preparation you can learn to perform this work yourself and save money.  It is always good to work with or consult with someone with experience the first time you attempt such a repair.
Question: Write a poem about a cat owner who gets transported to a parallel universe where highly advanced cats keep humans as pets.
Answer: I’d always been a cat person
But ne’er it crossed my mind
That one day I’d be whisked off to
A world run by felines

Though stunned by what I saw at first
(Imagine cats in hats!)
It wasn’t long before I found
I’d have to face some facts

For one, the cats were civilized
Far more than you or I
There was free fish both night and day
And many warm spots to lie

They’d music made of mewing notes
With basso purring too
They had sculpture, architecture,
(Though furballs were the glue)

But one night in my wanderings
I heard a fearful cry
A half-forgotten human sound
The wailing of a child

With unease growing in my heart
I sought the baleful noise
And through a window brightly lit
I saw a group of boys

The cats were playing with them, yes
The way you’d treat a pet
But the boys were crying nonetheless
Eyes shining big and wet

At once I knew what had them down,
What necessity they lacked
They cried and cried “Mama! Papa!”
To home they wanted back

When I returned from that strange land
And heard my own cat whine
The sound of those poor children
Echoed deep inside my mind
Question: What are the advantages of a propane grill?
Answer: The most often cited advantage of a propane grill is that it takes very little time to get the grill up to temperature for cooking.  Other fuel sources, such as wood pellets or charcoal briquets, do take some prep time before you can start cooking.

Also, cleanup on a propane grill is typically simpler as there is no wood or charcoal left over to clean up.

Many will argue that wood or charcoal gives more of a flavor to the food being cooked.  But, there are remedies to this for propane, including getting what is commonly known as a smoker box to generate smoke flavor.

Ultimately any style of grill can cook very tasty meals.  So it often comes down to personal preference on fuel source.
Question: How do you throw a cutter in baseball?
Answer: A cutter is a pitch in baseball with action and movement similar to a hybrid between a fastball and a slider.  Perhaps the most famous cutter pitcher in baseball history, a closer from the New York Yankees named, Mariano Rivera, used the cutter exclusively all the way to the hall of fame.  The cutter is thrown like a fastball, but with a "football" throwing arm action where the pitcher's palm faces their opposing arm as they release the pitch.  Grips for the cutter can vary, however, the keys are to hold the middle of the baseball, cross the seems at an angle similar to a slider and be sure not to turn or twist on release.  With this approach, the cutter is a pitch safe for youth baseball as young as age 9 and can be effective all the way to the World Series as demonstrated by Mariano Rivera.
Question: In geology, what factors determine the classification of rocks?  Place the results in a bullet list
Evidence: In geology, rock (or stone) is any naturally occurring solid mass or aggregate of minerals or mineraloid matter. It is categorized by the minerals included, its chemical composition, and the way in which it is formed. Rocks form the Earth's outer solid layer, the crust, and most of its interior, except for the liquid outer core and pockets of magma in the asthenosphere. The study of rocks involves multiple subdisciplines of geology, including petrology and mineralogy. It may be limited to rocks found on Earth, or it may include planetary geology that studies the rocks of other celestial objects.
Answer: - minerals included
- chemical composition
- way in which the rock is formed
Question: What should I do if my baby doesn't sleep in the night?
Answer: A few things you can check:
1, does your baby sleep a lot during the daytime? If so, you need to tell him/her when is daytime and when is night. Train him/her to sleep more during the night
2, Is your baby hungry? Baby will wake up if they are hungry. Make sure they have sufficient food before going to bed.
3, how is your baby's sleeping environment? Make sure the room temperature and humidity are good for sleep. Also, try to have a silent and dark environment for the baby to sleep.
Question: Are positively-labeled examples better?
Evidence: As can be seen in Table TABREF7, pre-trained language models are indeed highly capable of generating sentence representations that cluster by domains, resulting in up to 87.66%, 89.04% and 89.94% accuracy when using k=5, k=10 and k=15 clusters, respectively, across 10,000 sentences in 5 domains. We find these scores remarkably high given our straight-forward average-pooling strategy and that no domain-supervision was involved in the process of learning the pre-trained representations. Figure FIGREF15 also demonstrates the quality of the obtained clusters in 2D using the BERT-base model, where the ellipses describe the mean and variance parameters learned for each cluster by the GMM with $k=5$.
We note that some classes of models did better than others: while all vector-based models did far better than the random and LDA baselines, the MLM-based models dominated in all cases over word2vec and the auto-regressive models. This may be explained by the fact that the MLM-based models use the entire sentence context when generating the representations for each token, while the auto-regressive models only use the past context, and word2vec uses a limited window context. Using PCA improved performance in most cases and especially for the auto-regressive models, although the results for the MLMs remain high in both cases – suggesting that these models encode the information very differently.
The results are available in Table TABREF32. We can see that all selection methods performed much better in terms of BLEU than random selection. It is also nice to see that all selection methods performed better than using all the available data or the oracle-selected data when averaged across all domains, showing again that more data is not necessarily better in multi-domain scenarios and that data selection is a useful approach. Regarding a comparison of the data selection methods, Moore-Lewis performed better than Domain-Cosine, while Domain-Finetune performed best, showing the benefit of fine-tuning large pretrained models for the data selection task. Using the positively-labeled examples alone (Domain-Finetune-Positive) performed worse than using the top 500k examples but better than Domain-Cosine, while not requiring to determine the number of selected sentences.
Using the positively-labeled examples alone (Domain-Finetune-Positive) performed worse than using the top 500k examples but bet_x0002_ter than Domain-Cosine, while not requiring to determine the number of selected sentences.
Answer: No.
Question: Extract all the football competitions mentioned in the passage and present them using bullet points.
Evidence: Barcelona is one of three founding members of the top division that have never been relegated from the top division since its inception in 1929, along with Athletic Bilbao and Real Madrid. In 2009, Barcelona became the first Spanish club to win the continental treble consisting of La Liga, Copa del Rey, and the UEFA Champions League, and also became the first Spanish football club to win six out of six competitions in a single year, by also winning the Spanish Super Cup, UEFA Super Cup, and FIFA Club World Cup.
Answer: - La Liga
- Copa del Rey
- UEFA Champions League
- Spanish Super Cup
- UEFA Super Cup
- FIFA Club World Cup
Question: Who was the most popular musical artist in the 1980s?
Answer: Michael Jackson was the number one selling musical artist in the 1980s.
Question: Give me the TLDR on using llamas to guard my livestock.
Evidence: Using llamas as livestock guards in North America began in the early 1980s, and some sheep producers have used llamas successfully since then. Some would even use them to guard their smaller cousins, the alpaca. They are used most commonly in the western regions of the United States, where larger predators, such as coyotes and feral dogs, are prevalent. Typically, a single gelding (castrated male) is used.

Research suggests the use of multiple guard llamas is not as effective as one. Multiple males tend to bond with one another, rather than with the livestock, and may ignore the flock. A gelded male of two years of age bonds closely with its new charges and is instinctively very effective in preventing predation. Some llamas appear to bond more quickly to sheep or goats if they are introduced just prior to lambing. Many sheep and goat producers indicate a special bond quickly develops between lambs and their guard llama and the llama is particularly protective of the lambs.

Using llamas as guards has reduced the losses to predators for many producers. The value of the livestock saved each year more than exceeds the purchase cost and annual maintenance of a llama. Although not every llama is suited to the job, most are a viable, nonlethal alternative for reducing predation, requiring no training and little care.
Answer: TLDR: LLamas can be used as livestock guards. Typically a single castrated male is the best choice (multiple males will bond with each other instead of bonding with the livestock). Generally llamas are cost effective, require no training and little care.
Question: Who invented the radio?
Evidence: The invention of radio communication was preceded by many decades of establishing theoretical underpinnings, discovery and experimental investigation of radio waves, and engineering and technical developments related to their transmission and detection. These developments allowed Guglielmo Marconi to turn radio waves into a wireless communication system.
Answer: The radio was invented by Guglielmo Marconi, which allowed the harnessing of radio waves into a wireless communication system.  This invention did not happen in a vacuum.  It was enabled by investigating radio waves and understanding how to detect and transmit them.
Question: Identify which instrument is string or percussion: Suikinkutsu, Domra
Answer: Domra is string, Suikinkutsu is percussion.
Question: Categorize these paint colours as “primary”, “secondary”, and “tertiary” according to the Quiller color wheel.

Cadmium Yellow Light, Permanent Green Light, Viridian Green, Green Blue, Phthalocyanine Blue, Ultramarine Blue, Ultramarine Violet, Magenta, Permanent Rose, Cadmium Red, Cadmium Scarlet, Cadmium Orange
Answer: Primary: Cadmium Yellow Light, Phthalocyanine Blue, Permanent Rose
Secondary: Viridian Green, Ultramarine Violet, Cadmium Scarlet
Tertiary: Permanent Green Light, Green Blue, Ultramarine Blue, Magenta, Cadmium Red, Cadmium Orange
Question: Do the authors perform experiments using their proposed method?
Evidence: We illustrate the concept by discussing some instantiations that we have recently experimented with.
Unlike the Visual Dialogue setting discussed above, this setting ensures informational symmetry between the participants (both have access to the same type of information; but not the same information, as they can't “see” each other). More importantly, however, the constraint that the game only ends if they both agree ensures a “committment symmetry”, where the success of the game must be ensured by both participants. The design also provides for a clear “relevance place” at which an opportunity arises for semantic negotiation, namely, before the final decision is made. An example of this is shown in the example below. (The number in the parentheses indicate the time, relative to the beginning of the interaction, when the utterance was made.)
The MatchIt Game (Ilinykh et al., forthcoming) is a yet further simplified visual game. Here, the goal simply is to decide whether you and your partner are both looking at the same image (of the same genre as in MeetUp). In that sense, it is a reduction of the MeetUP game to the final stage, taking out the navigation aspect. As example SECREF12 shows, this can similarly lead to meta-semantic interaction, where classifications are revised. As SECREF12 shows, even in cases where a decision can be reached quickly, there can be an explicit mutual confirmation step, before the (silent) decision signal is sent.
A third setting that we have explored BIBREF19 brings conceptual negotiation more clearly into the foreground. In that game, the players are presented with images of birds of particular species and are tasked with coming up with a description of common properties. Again, the final answer has to be approved by both participants. As SECREF13 shows, this can lead to an explicit negotiation of conceptual content.
We illustrate the concept by discussing some instantiations that we have recently experimented with.
The design also provides for a clear “relevance place” at which an opportunity arises for semantic negotiation, namely, before the final decision is made. An example of this is shown in the example below. 
 As SECREF12 shows, even in cases where a decision can be reached quickly, there can be an explicit mutual confirmation step, before the (silent) decision signal is sent.
A third setting that we have explored BIBREF19 brings conceptual negotiation more clearly into the foreground. In that game, the players are presented with images of birds of particular species and are tasked with coming up with a description of common properties. Again, the final answer has to be approved by both participants. As SECREF13 shows, this can lead to an explicit negotiation of conceptual content.
Answer: Yes.
Question: What was the name of the character Steve Buscemi played in the HBO series BoardWalk Empire?
Evidence: Buscemi starred in the HBO drama series Boardwalk Empire created by Terence Winter. The series started in 2010, where Buscemi assumed the role as Enoch "Nucky" Thompson (based on Enoch L. Johnson), a corrupt Atlantic City politician who rules the town during the Prohibition era. Buscemi's performance garnered him a Golden Globe Award for Best Actor – Television Series Drama; he later received two more nominations for his work on the same show. In 2011 he hosted NBC's Saturday Night Live.
Answer: Enoch "Nucky" Thompson
Question: Where is Allauch
Evidence: Allauch (French pronunciation: ​; Occitan: Alaug) is a French commune situated east of Marseille in the department of Bouches-du-Rhône in the Provence-Alpes-Côte d'Azur region of France.

The inhabitants of the commune are known as Allaudiens or Allaudiennes in French.
Answer: Allauch is a French commune situated east of Marseille in the department of Bouches-du-Rhône in the Provence-Alpes-Côte d'Azur region of France.

The inhabitants of the commune are known as Allaudiens or Allaudiennes in French.