DataHammer/scimrc huggingface dataset:
Question: Since the main advantage of CoopFlow is a fast generation, the author should discuss such a cost perspective in detail. For example, how many iteration MCMC/SDE steps are required for other models, and how about generating samples with the same (and small) number of steps for all models?
Evidence: In this section, we compare our method with other baselines in generating examples with the same number of Langevin steps. We compare our methods with two closely related baselines, (1) the single EBM with short-run MCMC starting from noise distribution (Nijkamp et al., 2019), and (ii) Cooperative learning of an EBM and a generic generator (Xie et al., 2020a). The numbers of MCMC steps and FID reported in Table 9 are collected from the original papers. In Table 10, we also report the FID scores of different baseline methods using the different numbers of MCMC steps. We can see that with the same number of Langevin steps, the CoopFlow can generate much more realistic image patterns than the other two baselines. The results of the experiments show that the proposed CoopFlow can use less number of MCMC steps to achieve better results.
Answer: Thank you for your valuable suggestion. We have followed your suggestions and compared our method with closely related baselines in Table 4.1 from the perspective of computational cost in terms of the number of MCMC steps for EBM.
We compare our methods with two closely related baselines, (1) the single EBM with short-run MCMC starting from noise distribution [a], and (ii) Cooperative learning of an EBM and a generic generator [b]. The numbers of MCMC steps and FID reported in Table 4.1 are collected from the original papers.
Table 4.1: A comparison of computational costs in terms of number of MCMC steps.
Method number of MCMC steps FID
CoopFlow = EBM + flow (ours) 30 15.80
EBM with short-run MCMC [a] 100 48.21
CoopNet = EBM + generator [b] 10 33.61
In Table 4.2, we also report the FID performance of different related baseline methods under the different numbers of MCMC steps. We can see that with the same number of Langevin steps, the CoopFlow can generate much more realistic image patterns than the other two baselines. The quality of the generated images are evaluated by FID. The results of the experiments show that the proposed CoopFlow can use less number of MCMC steps to achieve better results.
Table 4.2: A comparison of FID scores under different numbers of MCMC steps.
Method 10 20 30 40 50 200
CoopFlow = EBM + flow (ours) 16.46 15.20 15.80 16.80 15.64 17.94
EBM with short-run MCMC 421.3 194.88 117.02 140.79 198.09 54.23
CoopNet = EBM + generator 33.74 33.48 34.12 33.85 42.99 38.88
We have followed you suggestion to include the above additional experiments in our revised paper. Please check a newly added subsection "A7. Analysis of the number of Langevin steps" in the Appendix.
Question: How big is new question answering dataset?
Evidence: CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data. (The data URL is not yet provided for anonymous review.) We include a list of MIDs such that their English names map unambiguously to a MID. Table TABREF17(a) summarizes the overall statistics of CFQ. Table TABREF17(b) uses numbers from BIBREF8 and from an analysis of WebQuestionsSP BIBREF17 and ComplexWebQuestions BIBREF18 to compare three key statistics of CFQ to other semantic parsing datasets (none of which provide annotations of their compositional structure). CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets. Note that it would be easy to boost the raw number of questions in CFQ almost arbitrarily by repeating the same question pattern with varying entities, but we use at most one entity substitution per question pattern. Appendix SECREF10 contains more detailed analyses of the data distribution.
CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data.
Answer: The answers are shown as follows:
* 239,357 English question-answer pairs
Question: Who is Bon Jovi
Evidence: Bon Jovi is an American rock band formed in 1983 in Sayreville, New Jersey. It consists of singer Jon Bon Jovi, keyboardist David Bryan, drummer Tico Torres, guitarist Phil X, and bassist Hugh McDonald. Original bassist Alec John Such quit the band in 1994, and longtime guitarist and co-songwriter Richie Sambora left in 2013. The band has been credited with " the gap between heavy metal and pop with style and ease".
Answer: Bon Jovi is an American rock band formed in 1983 in Sayreville, New Jersey. It consists of singer Jon Bon Jovi, keyboardist David Bryan, drummer Tico Torres, guitarist Phil X, and bassist Hugh McDonald. Original bassist Alec John Such quit the band in 1994, and longtime guitarist and co-songwriter Richie Sambora left in 2013. The band has been credited with bridging the gap between heavy metal and pop with style and ease.
Question: Given this paragraph, who wrote "Greenmantle?"
Evidence: Greenmantle is the second of five novels by John Buchan featuring the character Richard Hannay. It was first published in 1916 by Hodder & Stoughton, London. It is one of two Hannay novels set during the First World War, the other being Mr Standfast (1919); Hannay's first and best-known adventure, The Thirty-Nine Steps (1915), is set in the period immediately preceding the war.
Answer: John Buchan
Question: What is electricity?
Answer: Electricity is the flowing of electrons from a higher electric potential to a lower one (or alternatively, flowing of electron holes in the opposite direction). The flowing electrons can perform work, ranging from heating a resistive element in a light bulb to turning a motor through a magnetic field.
Question: write a story about the first contact of Alien would be
Answer: The first contact with Alien will be a big event that the entire earth would remember for generations. the first thing that will determine the nature of this first contact depends who get to who first. Alien arrived to earth, or human discovered another planet with other civilisation. it is high likely that in either way, the first contact will be between robots of the more advanced civilisation and the the people of the less advanced civilisation. this is due to the fact that the high advanced civilization tends to explore other worlds using their robots first.
Question: What name is Beijing also known by?
Evidence: Beijing (/beɪˈdʒɪŋ/ bay-JING; Chinese: 北京; pinyin: Běijīng; Mandarin pronunciation: [pèɪ.tɕíŋ] (listen)), alternatively romanized as Peking (/piːˈkɪŋ/ pee-KING), is the capital of the People's Republic of China. With over 21 million residents, Beijing is the world's most populous national capital city and is China's second largest city after Shanghai. It is located in Northern China, and is governed as a municipality under the direct administration of the State Council with 16 urban, suburban, and rural districts. Beijing is mostly surrounded by Hebei Province with the exception of neighboring Tianjin to the southeast; together, the three divisions form the Jingjinji megalopolis and the national capital region of China.
Answer: Běijīng is alternatively romanized as Peking and is the capital of the People's Republic of China
Question: Bike or Car?
Answer: Car is always better, but bike fans will deny it.

Car has more power then Bike.
Car is more safe
Car has more feature compare to Bike
Question: How are weather forecasts created?
Answer: Weather forecasts are created by combining data from many sources, such as satellites, weather balloons, and ground stations.  Those data points are then fed to large computer systems with sophisticated models to predict how the weather will change over time.  

Weather sites and even local weather forecasters use this information to provide a regional and short-term forecasts.
Question: Extract the amounts of federal funding provided to state and local governments for education in the past year
Evidence: Education in the United States is provided in public and private schools and by individuals through homeschooling. State governments set overall educational standards, often mandate standardized tests for K–12 public school systems and supervise, usually through a board of regents, state colleges, and universities. The bulk of the $1.3 trillion in funding comes from state and local governments, with federal funding accounting for about $260 billion in 2021 compared to around $200 billion in past years.

Private schools are free to determine their own curriculum and staffing policies, with voluntary accreditation available through independent regional accreditation authorities, although some state regulation can apply.

In 2013, about 87% of school-age children (those below higher education) attended state-funded public schools, about 10% attended tuition and foundation-funded private schools, and roughly 3% were home-schooled.
Answer: $260 Billion
Question: Provide a possible jurisdiction section for a federal civil rights complaint leaving blanks for the location.
Answer: This is a civil rights complaint arising out of the county of _______ within the state of _______. This complaint sets forth causes of action under Title 42 SS 1981, 1982, 1983, 1985, 1986. The Plaintiff will show that her ability to engage in a contractual agreement was infringed upon by a private corporation conspiring with state actors acting under color of state law in violation of the aforementioned federal statutes. These facts give this honorable court jurisdiction over the matter.
Question: What is the language model pre-trained on?
Evidence: This method is unique since it avoids needing a prior dataset by using the information found within the weights of a general language model. Word embedding models, and BERT in particular, contain vast amounts of information collected through the course of their training. BERT Base for instance, has 110 Million parameters and was trained on both Wikipedea Corpus and BooksCorpus BIBREF0, a combined collection of over 3 Billion words. The full potential of such vastly trained general language models is still unfolding. This paper demonstrates that by carefully prompting and analysing these models, it is possible to extract new information from them, and extend short-text analysis beyond the limitations posed by word count.
Word embedding models, and BERT in particular, contain vast amounts of information collected through the course of their training. BERT Base for instance, has 110 Million parameters and was trained on both Wikipedea Corpus and BooksCorpus BIBREF0, a combined collection of over 3 Billion words.
Answer: The answers are shown as follows:
* Wikipedea Corpus and BooksCorpus
Question: What is the legal drinking age in the USA?
Answer: The legal drinking age in the USA is 21.
Question: How do you play baseball?
Evidence: Baseball is a bat-and-ball sport played between two teams of nine players each, taking turns batting and fielding. The game occurs over the course of several plays, with each play generally beginning when a player on the fielding team, called the pitcher, throws a ball that a player on the batting team, called the batter, tries to hit with a bat. The objective of the offensive team (batting team) is to hit the ball into the field of play, away from the other team's players, allowing its players to run the bases, having them advance counter-clockwise around four bases to score what are called "runs". The objective of the defensive team (referred to as the fielding team) is to prevent batters from becoming runners, and to prevent runners' advance around the bases. A run is scored when a runner legally advances around the bases in order and touches home plate (the place where the player started as a batter).
Answer: Baseball is played with two teams of nine players each that take turns batting and fielding.  The objective of the offensive team is to hit the ball into the field of play away from the other team's players, allowing its players to run bases and having them advance around the bases and score runs. The objective of the defensive team is to prevent batters from becoming runners, and to prevent runners' advance around the bases.
Question: Give me a list of some of the best films where English is not the primary language spoken. Please indicate which language is the primary language spoken.
Answer: You might enjoy the following films:

- Spirited Away (Japanese)
- In the Mood for Love (Cantonese)
- City of God (Portuguese)
- Life is Beautiful (Italian)
- Cinema Paradiso (Italian)
- Amélie (French)
- La Haine (French)
Question: Based on this passage, what was the first designated space to be established in Roche Percee called? Who established it and in what year?
Evidence: In 1872 the Boundary Commission travelled through this area while surveying the border between Canada and the United States. During the 1874 March West trek of the North-West Mounted Police, the force established its first camp (called Short Creek Camp) at this location. The community began being served by the Soo Line in 1893, permitting the coal mines in the region to become operational. Roche Percée incorporated as a village on January 12, 1909.
Answer: The first established space was Short Creek Camp which was founded in 1874 by the North-West Mounted Police
Question: Give me the list of fortified wines.
Answer: Fortified wines:
- Port
- Madeira
- Marsala
- Sherry
- Vermouth
- Vinsanto
Question: What was Britain's first colony (annexed in 1583)
Answer: Newfoundland
Question: Who are the considered to be the best soccer players of all time, and what countries are they from?
Answer: Pelé (Brazil), Lionel Messi (Argentina), Cristiano Ronaldo (Portugal), Ronaldo (Brazil), Diego Maradona (Argentina), Franz Beckenbauer (Germany), George Best (Northern Ireland), Michel Platini (France), Johan Cruyff (The Netherlands), and David Beckham (England).
Question: You were abducted by aliens and experimented on, you were sent back to earth with the knowledge and ability to solve any problem on earth
Answer: Now that I have returned to earth with extreme knowledge and power, I have the ability to solve any problem in the world. I will start with antigravity enabling space travel for all. I will then discover the secret to longevity. I will answer the uncertainties of the world such as how the Pyramids came to be, lost civilizations, nuclear fusion and global warming. Finally, to make my millions, I will reverse male balding.
Question: i want to eat healthier. what kind of food in my diet should i cut out? Everyday I have a yogurt, burger, pizza, salad, and coffee.
Answer: It depends what your goals are for dieting. However, burgers and pizzas are generally not healthy due to a lot of trans fat and unhealthy carbs. Burgers and pizzas are also often fast food which means they are made with processed ingredients. Yogurt, salad, and coffee do not need to be cut out but in excess they can be unhealthy too. So, always eat or drink in moderation,
Question: Classify each of these as a toy for a dog or a cat: ball, laser, bone, mouse, frisbee, catnip
Answer: ball: dog, laser: cat, bone: dog, mouse: cat, frisbee: dog, catnip: cat
Question: Why do we have work meetings?
Answer: We have work meetings to give status updates on current projects.  These meetings can be virtual or in person.  In-person work meetings are great for building rapport, meeting new people, and building stronger relationships with people you already know.  Staying up to date on what everyone is working and aligning with company vision is how companies stay successful and have a competitive advantage over their competitors.  Regular meetings are the best way to achieve this.
Question: Why is football called soccer in the US?
Answer: It is originally a British name. “Assoccer” (association football) was later shortened to “soccer”. The American football is called rugby football. Later British changes the name to football due to anti-americanism. But in America, football is named as a more popular sports like rugby football. So now it cannot change the name as the world called.
Question: Classify the following bools as fiction or non-fiction: Harry Potter and the Sorcerer's Stone, Economics in One Lesson, The Cat in the Hat, A Brief History of Time, Democracy In America, and Goodnight Moon.
Answer: Harry Potter and the Sorcerer's Stone, The Cat in the Hat, and Goodnight Moon are all fiction books.  Economics in One Lesson, A Brief History of Time, and Democracy In America are all non-fiction.
Question: The paper misses relevant work: Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning,Could authors provide a legitimate reason for not including relevant work into this submission?
Evidence: In Table 5, we compare the computational complexity of action selection for CASEC and DCG, which is the bottleneck of both algorithms. CASEC is slightly faster than DCG by virtue of graph sparsity.****Table 5: Average time (millisecond) for 1000 action selection phases of CASEC/DCG. CASEC uses a graph with sparseness 0.2 while DCG uses the full graph. To ensure a fair comparison, both Max-Sum/Max-Plus algorithms pass messages for 8 iterations. The batch size is set to 10.
Answer: The main reason is that DICG [1] does not incorporate pairwise payoff functions. Graphs in DICG are used to mix observation of agents which serves as the input of fully-decomposed actors or centralized critics. Consequently, DICG still suffers from the relative generalization problem. In our ablation study, Attn. (described in Appendix B.2), we use the DICG-style attention mechanism to assign weights to payoff functions. We empirically compare against this ablation in Table 5 (page 20).
As suggested by the reviewer, in the revised version of our paper, we discuss and compare against DICG.
Question: What dataset is used for the experiments?
Evidence: Recent work on convolutional neural network architectures showed that they are competitive with recurrent architectures even on tasks where modeling long-range dependencies is critical, such as language modeling BIBREF0 , machine translation BIBREF1 , BIBREF2 and speech synthesis BIBREF3 . In end-to-end speech recognition however, recurrent architectures are still prevalent for acoustic and/or language modeling BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 .
There is a history of using convolutional networks in speech recognition, but only as part of an otherwise more traditional pipeline. They have been first introduced as TDNNs to predict phoneme classes BIBREF9 , and later to generate HMM posteriorgrams BIBREF10 . They have more recently been used in end-to-end frameworks, but only in combination with recurrent layers BIBREF6 , or n-gram language models BIBREF11 , or for phone recognition BIBREF12 , BIBREF13 . Nonetheless, convolutional architectures are prevalent when learning from the raw waveform BIBREF14 , BIBREF15 , BIBREF16 , BIBREF13 , BIBREF17 , because they naturally model the computation of standard features such as mel-filterbanks. Given the evidence that they are also suitable on long-range dependency tasks, we expect convolutional neural networks to be competitive at all levels of the speech recognition pipeline.
In this paper, we present a fully convolutional approach to end-to-end speech recognition. Building on recent advances in convolutional learnable front-ends for speech BIBREF13 , BIBREF17 , convolutional acoustic models BIBREF11 , and convolutional language models BIBREF0 , the paper has four main contributions:
More precisely, we perform experiments on the large vocabulary task of the Wall Street Journal dataset (WSJ) and on the 1000h Librispeech. Our overall pipeline improves the state- of-the-art of end-to-end systems on both datasets. In particular, we decrease by 2% (absolute) the Word Error Rate on the noisy test set of Librispeech compared to DeepSpeech 2 [7] and the best sequence-to-sequence model [9]. On clean speech, the improvement is about 0.5% on Librispeech compared to the best end-to-end systems; on WSJ, our results are competitive with the current state-of-the-art, a DNN-HMM system [19].
Answer: They performed experiments on the large vocabulary task of the Wall Street Journal dataset (WSJ) and on the 1000h Librispeech.
Question: List of the biggest oceans in the world
Answer: Pacific Ocean
Atlantic Ocean
Indian Ocean
Southern Ocean
Arctic Ocean
Coral Sea
Arabian Sea
Question: Can the EGL measure the expected change in a model's parameters intuitively?
Evidence: State-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize. Labeling thousands of hours of audio, however, is expensive and time-consuming. A natural question to ask is how to achieve better generalization with fewer training examples. Active learning studies this problem by identifying and labeling only the most informative data, potentially reducing sample complexity. How much active learning can help in large-scale, end-to-end ASR systems, however, is still an open question.
The speech recognition community has generally identified the informativeness of samples by calculating confidence scores. In particular, an utterance is considered informative if the most likely prediction has small probability BIBREF1 , or if the predictions are distributed very uniformly over the labels BIBREF2 . Though confidence-based measures work well in practice, less attention has been focused on gradient-based methods like Expected Gradient Length (EGL) BIBREF3 , where the informativeness is measured by the norm of the gradient incurred by the instance. EGL has previously been justified as intuitively measuring the expected change in a model's parameters BIBREF3 .We formalize this intuition from the perspective of asymptotic variance reduction, and experimentally, we show EGL to be superior to confidence-based methods on speech recognition tasks. Additionally, we observe that the ranking of samples scored by EGL is not correlated with that of confidence scoring, suggesting EGL identifies aspects of an instance that confidence scores cannot capture.
In BIBREF3 , EGL was applied to active learning on sequence labeling tasks, but our work is the first we know of to apply EGL to speech recognition in particular. Gradient-based methods have also found applications outside active learning. For example, BIBREF4 suggests that in stochastic gradient descent, sampling training instances with probabilities proportional to their gradient lengths can speed up convergence. From the perspective of variance reduction, this importance sampling problem shares many similarities to problems found in active learning.
EGL has previously been justified as intuitively measuring the expected change in a model’s parameters [4].
Answer: Yes, it can.