DataHammer/scimrc huggingface dataset:
Question: Why was the movie The Happytime Murders given such bad reviews if it was award winning?
Evidence: The Happytime Murders was released on August 24, 2018, by STXfilms. It received mostly negative reviews from critics for its humor, performances and story, though the puppetry received praise. The film was a box-office bomb, grossing $27.5 million worldwide against a $40–47 million budget. The film received six nominations at the 39th Golden Raspberry Awards, including Worst Picture, and Worst Actress for McCarthy who won that award.
Answer: The Golden Raspberry Awards, also known as the Razzies, is a parody award show. They celebrate the failures in cinema. Their tagline is "Own Your Bad" and often celebrities that are nominated for a Razzie take it in jest. Sometimes the roles or categories a movie is nominated in the Razzies are also nominated for Oscars or other acclaimed awards. The Happytime Murders film, however, was only nominated for parody awards such as the Razzies.
Question: What is Twenty20 or T20 in cricket?
Answer: Twenty20 or T20 for short is a shortened version of the game of cricket in which each side faces a maximum of 20 overs. The game typically lasts about 4 hours with a quick turn between innings unlike other forms of the game, thus increasing its viewership and popularity. 

This format was introduced by the England Cricket Board in an inter-county competition back in 2003. Since then, its become really popular with several international competitions and country wide leagues across all the major cricket playing nations world wide
Question: Why do babies cry?
Answer: Babies cry in order to tell us things about their needs, as it is their only form of communication. The most common reasons a baby may cry are that they are hungry, have a dirty diaper, or are tired. Babies may also cry if they are hot, cold, or uncomfortable (gas pains, itchy clothes, rash). Babies do not cry to manipulate their parents; they simply are not capable of such advanced thinking and behavior.
Question: what are some of the signs and symptoms of Dissociative identity disorder?
Evidence: According to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), symptoms of DID include "the presence of two or more distinct personality states" accompanied by the inability to recall personal information beyond what is expected through normal memory issues. Other DSM-5 symptoms include a loss of identity as related to individual distinct personality states, loss of one's subjective experience of the passage of time, and degradation of a sense of self and consciousness. In each individual, the clinical presentation varies and the level of functioning can change from severe impairment to minimal impairment. The symptoms of dissociative amnesia are subsumed under a DID diagnosis, and thus should not be diagnosed separately if DID criteria are met. Individuals with DID may experience distress from both the symptoms of DID (intrusive thoughts or emotions) and the consequences of the accompanying symptoms (dissociation rendering them unable to remember specific information). The majority of patients with DID report childhood sexual or physical abuse. Amnesia between identities may be asymmetrical; identities may or may not be aware of what is known by another. Individuals with DID may be reluctant to discuss symptoms due to associations with abuse, shame, and fear. DID patients may also frequently and intensely experience time disturbances. Around half of people with DID have fewer than 10 identities and most have fewer than 100; although as many as 4,500 have been reported. The average number of identities has increased over the past few decades, from two or three to now an average of approximately 16. However, it is unclear whether this is due to an actual increase in identities, or simply that the psychiatric community has become more accepting of a high number of compartmentalized memory components.
Answer: Dissociative identity Disorder, or DIS in short, causes many symptoms that range from intrusive thoughts, and emotions to Amnesia. The severeness of Amnesia will vary between identities. Other symptoms include time disturbances between identities. Some of the other speculative signs include the number of identities that can range from 10 to 4500.
Question: How is octave convolution concept extended to multiple resolutions and octaves?
Evidence: An octave convolutional layer BIBREF0 factorizes the output feature maps of a convolutional layer into two groups. The resolution of the low-frequency feature maps is reduced by an octave – height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves – dividing by $2^t$, where $t=1,2,3$ – and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer, and an example with three groups and reductions of one and two octaves is depicted in Fig. FIGREF1.
An octave convolutional layer BIBREF0 factorizes the output feature maps of a convolutional layer into two groups. The resolution of the low-frequency feature maps is reduced by an octave – height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves – dividing by $2^t$, where $t=1,2,3$ – and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer, and an example with three groups and reductions of one and two octaves is depicted in Fig. FIGREF1.
Answer: The answers are shown as follows:
* The resolution of the low-frequency feature maps is reduced by an octave – height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves – dividing by $2^t$, where $t=1,2,3$ – and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer,
Question: What is a confusion network or lattice?
Evidence: FLOAT SELECTED: Fig. 2: Standard ASR outputs
A number of important downstream and upstream applications rely on accurate confidence scores in graph-like structures, such as confusion networks (CN) in Fig. 2 and lattices in Fig. 2 , where arcs connected by nodes represent hypothesised words. This section describes an extension of BiRNNs to CNs and lattices.
FLOAT SELECTED: Fig. 2: Standard ASR outputs
A number of important downstream and upstream applications rely on accurate confidence scores in graph-like structures, such as confusion networks (CN) in Fig. 2 and lattices in Fig. 2 , where arcs connected by nodes represent hypothesised words. 
Fig. 2b shows that compared to 1-best sequences in Fig. 2a, each node in a CN may have multiple incoming arcs.
Answer: graph-like structures where arcs connect nodes representing multiple hypothesized words, thus allowing multiple incoming arcs unlike 1-best sequences
Question: Identify which animal species is alive or extinct: Purussaurus, Albacore Tuna
Answer: Albacore Tuna is alive, Purussaurus is extinct.
Question: From the given text, List some details about the song "Key West (Philosopher Pirate)"
Evidence: "Key West (Philosopher Pirate)" is a song written and performed by the American singer-songwriter Bob Dylan and released as the ninth track on his 2020 album Rough and Rowdy Ways. It is a mid-tempo, accordion-driven ballad that has been cited as a high point of the album by many critics.

It is the only song on the album to feature a traditional chorus and one of only two songs (the other being "I've Made Up My Mind to Give Myself to You") to feature backing vocals.
Answer: 1. Bob Dylan, an American singer-songwriter, wrote and recorded the song "Key West (Philosopher Pirate)," which was included as the ninth track on his 2020 album Rough and Rowdy Ways.
2. Many reviews have praised this mid-tempo, accordion-driven ballad as the album's best track.
3. It is one of just two songs on the album (the other being "I've Made Up My Mind to Give Myself to You") to have backup singers, and it is the only song on the album with a conventional chorus.
Question: What is the accuracy of Subtask A on the hashtag file #BreakUpIn5Words in the evaluation stage?
Evidence: We relied on bigram and trigram language models because tweets are short and concise, and often only consist of just a few words.
The performance of our system was not consistent when comparing the development to the evaluation results. During development language models trained on the tweet data performed better. However during the evaluation and post-evaluation stage, language models trained on the news data were significantly more effective. We also observed that bigram language models performed slightly better than trigram models on the evaluation data. This suggests that going forward we should also consider both the use of unigram and character–level language models.
These results suggest that there are only slight differences between bigram and trigram models, and that the type and quantity of corpora used to train the models is what really determines the results.
The task description paper BIBREF6 reported system by system results for each hashtag. We were surprised to find that our performance on the hashtag file #BreakUpIn5Words in the evaluation stage was significantly better than any other system on both Subtask A (with accuracy of 0.913) and Subtask B (with distance score of 0.636). While we still do not fully understand the cause of these results, there is clearly something about the language used in this hashtag that is distinct from the other hashtags, and is somehow better represented or captured by a language model. Reaching a better understanding of this result is a high priority for future work.
The tweet data was significantly smaller than the news data, and so certainly we believe that this was a factor in the performance during the evaluation stage, where the models built from the news data were significantly more effective. Going forward we plan to collect more tweet data, particularly those that participate in #HashtagWars. We also intend to do some experiments where we cut the amount of news data and then build models to see how those compare.
While our language models performed well, there is some evidence that neural network models can outperform standard back-off N-gram models BIBREF12 . We would like to experiment with deep learning methods such as recurrent neural networks, since these networks are capable of forming short term memory and may be better suited for dealing with sequence data.
We were surprised to find that our performance on the hashtag file #BreakUpIn5Words in the evaluation stage was significantly better than any other system on both Subtask A (with accuracy of 0.913) and Subtask B (with distance score of 0.636).
Answer: 0.913.
Question: What is the last source of information that the system considers for interactive learning?
Evidence: From the point of view of dialog systems providing general information from a knowledge base, the most limiting factor is that a large portion of the questions is understood poorly.
Current approaches BIBREF3 , BIBREF4 can only achieve around 50% accuracy on some question answering datasets. Therefore, we think that there is a room for improvements which can be achieved by interactively asking for additional information in conversational dialogs with users. This extra information can be used for improving policies of dialog systems. We call this approach the interactive learning from dialogs.
We can improve dialog systems in several aspects through interactive learning in a direct interaction with users. First, the most straightforward way obviously is getting the correct answer for questions that the system does not know. We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. We hypothesize that users are willing to give such explanations because it could help them to find answers for their own questions. The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording. This area is extensively studied for the purposes of information retrieval BIBREF5 , BIBREF6 .
The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.
The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording.
Answer: Rephrasing.
Question: Tell me whether these are shoes or clothing brands: Nike, Tommy Hilfiger, Adidas, Levi's, Reebok, Puma, Wrangler.
Answer: Nike is a shoe brand.
Tommy Hilfiger is a clothing brand.
Adidas is a shoe brand,
Levi's is a clothing brand.
Reebok is a shoe brand.
Puma is a shoe brand.
Wrangler is a clothing brand.
Question: Given this paragraph about ESPN's SportsCenter in 2009, in what west coast city did ESPN begin producing it's 1:00 a.m. Eastern Time edition of SportsCenter?
Evidence: On April 6, 2009 (starting with the 9:00 a.m. to 12:00 p.m. Eastern Time edition, which was anchored by Hannah Storm and Sage Steele), SportsCenter debuted a new graphics package that saw the "rundown" graphic – shown during the daytime editions – being shifted to the left side of the screen. On that same date, SportsCenter began producing its 1:00 a.m. Eastern Time edition of SportsCenter live from ESPN's production facilities in the newly constructed L.A. Live complex (just across from the Staples Center) in Los Angeles. The set is virtually identical to the setup at the main facilities in Bristol, and the late-night West Coast broadcast would be produced as simply another edition of the program. Neil Everett and Stan Verrett were appointed as the primary anchors for the Los Angeles-based editions of SportsCenter. A new BottomLine ticker was also unveiled that day on four of the five ESPN networks (ESPN, ESPN2, ESPN Classic and ESPNU); the redesigned ticker was quickly dropped, reverting to the old BottomLine design – which had been in use since April 2003 – due to an equipment failure (however, this ticker was operational for the 2009 NFL Draft and the 2009 NBA draft). After technical issues with the revamped BottomLine were fixed, the new BottomLine was reinstated on July 8.
Answer: Los Angeles is the west coast city that ESPN began producing it's 1:00 a.m. Eastern Time edition of SportsCenter.
Question: Name three creative ways to utilize plastic wrap other than sealing food to keep it fresh.
Answer: 1. If you have wounds in your body and want to take a shower while keeping the wounded area dry, you can wrap that area with plastic wrap closely to prevent water from coming in.
2. If you are moving and have lots of fragile items (like monitors, chairs, etc), you can wrap your furniture or electronics with plastic wrap to prevent damages.
3. If you have an outdoor garden mini greenhouse and want to prevent cold air from getting into it at night, you can wrap the bottom of the greenhouse with plastic wrap so that it is airtight.
Question: How many basketball teams are in NBA?
Answer: There are 30 teams in NBA, 15 in east and 15 in west. The most recent expand with the Charlotte Bobcats in 2004. There is rumor that there is a possible NBA expansion to 32 teams around 2025.
Question: Which movie won the Best Picture award at 95th Academy Awards?
Evidence: The 95th Academy Awards was a ceremony held by the Academy of Motion Picture Arts and Sciences (AMPAS) on March 12, 2023, at the Dolby Theatre in Los Angeles. It honored films released in 2022.

The event was televised in the U.S. by ABC and was produced by Ricky Kirshner and Glenn Weiss. Weiss was also the director. Comedian and late-night talk show host Jimmy Kimmel hosted the show for the third time, after emceeing the 89th and 90th editions of the ceremony in 2017 and 2018, respectively.

Everything Everywhere All at Once led the ceremony with eleven nominations and seven wins, including Best Picture, Best Director and Best Original Screenplay for Daniel Kwan and Daniel Scheinert, and three of the four acting awards. Other winners included All Quiet on the Western Front with four awards, The Whale with two, and Avatar: The Way of Water, Black Panther: Wakanda Forever, Guillermo del Toro's Pinocchio, Navalny, RRR, Top Gun: Maverick, and Women Talking each with one. Short film winners included The Boy, the Mole, the Fox and the Horse, The Elephant Whisperers, and An Irish Goodbye.
Answer: Everything Everywhere All at Once won the award for Best Picture at the 95th Academy Awards. Additionally, this movie also won Best Director and Best Original Screenplay for Daniel Kwan and Daniel Scheinert, and three of the four acting awards.
Question: Why was Antestor known as sorrow metal?
Evidence: Antestor (Norwegian: [ˈɑ̀ntəstuːɽ]) is a Norwegian Christian extreme metal band formed in 1990 in Jessheim. Credited for starting the northern European Christian black metal scene, Antestor is the only Christian band to have an album released by Cacophonous Records, which has also released records by bands such as Dimmu Borgir, Sigh, and Cradle of Filth. The band's only release on Cacophonous, The Return of the Black Death, proved influential for the Christian black metal movement, and has sold over 10,000 copies.

In the late 1990s they dubbed their musical style as "sorrow metal" rather than black metal because the black metal movement was publicly affiliated with Satanism in Norway. According to HM magazine, the progressive elements on the debut album, Martyrium, were possibly ahead of their time in the Norwegian extreme metal scene.

The group has gone through several line-up changes over the years, and currently Antestor consists of the founding guitarist Lars Stokstad (Vemod), vocalist Ronny Hansen (Vrede), bassist Erik Normann Aanonsen, guitarist Robert Bordevick, and drummer Henning Børven. The original vocalist Kjetil Molnes (Martyr) and drummer Svein Sander (Armoth) left the band around 2000, and the members of a fellow Norwegian black metal group, Vaakevandring, joined Antestor. The reputable Norwegian extreme metal drummer Jan Axel Blomberg (Hellhammer) played session drums for their 2005 album The Forsaken and Det tapte liv EP. A fourth studio album, Omen, was released in 2012.
Answer: Because black metal was associated with Satanism in Antestor's home country Norway, and they are a Christian extreme metal band.
Question: What are the different methods to open a pomegranate?
Answer: 1 - Vertical cutting: roll the pomegranate over the surface of the cutting board to loosen the seeds; position it in the middle; cut the top (flower) and pop it off; slice it in half vertically along the ridges; cut each of the ridges to create 5 slices and then tear it apart to remove the seeds.
2 - Submerging it in water to remove the seeds: fill a large bowl with cool water; make a shallow cut down the side and use your thumbs to tear it into 2 halves then place them in the water and use your fingers to nudge the seeds free and finally strain the seeds into a bowl.
3 - Removing seeds with a wooden spoon: make a shallow horizontal slice in the middle and tear it into 2 halves; for each half place it seeds side down over a bowl and hit the skin with a wooden spoon for seeds to fall out.
Question: Why is strength training important for distance runners?
Answer: Strength training is important for distance runners to get faster, and stay injury-free. Running is a high impact activity that puts a lot of stress on the body. Strength training can help the body to deal with this stress, and correct imbalances in the body that can lead to injury over time.
Question: What are the components of the black-box context injection system?
Evidence: Our goal is to supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences, in order to produce the desired target-side morphology when the information is not available in the source sentence. The approach we take in the current work is that of black-box injection, in which we attempt to inject knowledge to the input in order to influence the output of a trained NMT system, without having access to its internals or its training procedure as proposed by vanmassenhove-hardmeier-way:2018:EMNLP.
To verify this, we experiment with translating the sentences with the following variations: No Prefix—The baseline translation as returned by the GMT system. “He said:”—Signaling a male speaker. We expect to further skew the system towards masculine forms. “She said:”—Signaling a female speaker and unknown audience. As this matches the actual speaker's gender, we expect an improvement in translation of first-person pronouns and verbs with first-person pronouns as subjects. “I said to them:”—Signaling an unknown speaker and plural audience. “He said to them:”—Masculine speaker and plural audience. “She said to them:”—Female speaker and plural audience—the complete, correct condition. We expect the best translation accuracy on this setup. “He/she said to him/her”—Here we set an (incorrect) singular gender-marked audience, to investigate our ability to control the audience morphology.
Our goal is to supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences, in order to produce the desired target-side morphology when the information is not available in the source sentence. The approach we take in the current work is that of black-box injection, in which we attempt to inject knowledge to the input in order to influence the output of a trained NMT system, without having access to its internals or its training procedure as proposed by vanmassenhove-hardmeier-way:2018:EMNLP.
To verify this, we experiment with translating the sentences with the following variations: No Prefix—The baseline translation as returned by the GMT system. “He said:”—Signaling a male speaker. We expect to further skew the system towards masculine forms. “She said:”—Signaling a female speaker and unknown audience. As this matches the actual speaker's gender, we expect an improvement in translation of first-person pronouns and verbs with first-person pronouns as subjects. “I said to them:”—Signaling an unknown speaker and plural audience. “He said to them:”—Masculine speaker and plural audience. “She said to them:”—Female speaker and plural audience—the complete, correct condition. We expect the best translation accuracy on this setup. “He/she said to him/her”—Here we set an (incorrect) singular gender-marked audience, to investigate our ability to control the audience morphology.
Answer: The answers are shown as follows:
* supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences
Question: Are the Transformers masked?
Evidence: We calculate the loss in training according to a mean of the sparse categorical cross-entropy formula. Sparse categorical cross-entropy BIBREF23 is used for identifying classes from a feature set, which assumes a large target classification set. Evaluation between the possible translation classes (all vocabulary subword tokens) and the produced class (predicted token) is the metric of performance here. During each evaluation, target terms are masked, predicted, and then compared to the masked (known) value. We adjust the model's loss according to the mean of the translation accuracy after predicting every determined subword in a translation.
During each evaluation, target terms are masked, predicted, and then compared to the masked (known) value.
Answer: Yes.
Question: What is the size of the Eiffel Tower?
Answer: The Eiffel Tower is 330 meters high.