DataHammer/scimrc huggingface dataset:
Question: What is the purpose of this article?
Evidence: People's personality and motivations are manifest in their everyday language usage. With the emergence of social media, ample examples of such usage are procurable. In this paper, we aim to analyze the vocabulary used by close to 200,000 Blogger users in the U.S. with the purpose of geographically portraying various demographic, linguistic, and psychological dimensions at the state level. We give a description of a web-based tool for viewing maps that depict various characteristics of the social media users as derived from this large blog dataset of over two billion words.
In this paper, we aim to analyze the vocabulary used by close to 200,000 Blogger users in the U.S. with the purpose of geographically portraying various demographic, linguistic, and psychological dimensions at the state level.
Answer: In this paper, the author aims to analyze the vocabulary used by close to 200,000 Blogger users in the U.S. with the purpose of geographically portraying various demographic, linguistic, and psychological dimensions at the state level.
Question: How many intents were classified?
Evidence: In task 1, there are two top categories, namely, chit-chat and task-oriented dialogue. The task-oriented dialogue also includes 30 sub categories. In this evaluation, we only consider to classify the user intent in single utterance.
In task 1, there are two top categories, namely, chit-chat and task-oriented dialogue.
Answer: The answers are shown as follows:
* two
Question: Who are some of the best guitarists of all time?
Answer: Jimi Hendrix
Duane Allman
B.B. King
Eric Clapton
Robert Johnson
Chuck Berry
Stevie Ray Vaughan
Ry Cooder
Jimmy Page
Keith Richards
Kirk Hammett
Kurt Cobain
Jerry Garcia
Jeff Beck
Carlos Santana
Johnny Ramone
Jack White
John Frusciante
Richard Thompson
James Burton
George Harrison
Mike Bloomfield
Warren Haynes
The Edge
Freddie King
Tom Morello
Mark Knopfler
Stephen Stills
Ron Asheton
Buddy Guy
Dick Dale
John Cipollina
Lee Ranaldo
Thurston Moore
John Fahey
Steve Cropper
Bo Diddley
Peter Green
Brian May
John Fogerty
Clarence White
Robert Fripp
Eddie Hazel
Scotty Moore
Frank Zappa
Les Paul
T-Bone Walker
Joe Perry
John McLaughlin
Pete Townshend
Paul Kossoff
Lou Reed
Mickey Baker
Jorma Kaukonen
Ritchie Blackmore
Tom Verlaine
Roy Buchanan
Dickey Betts
Jonny Greenwood
Ed O'Brien
Ike Turner
Zoot Horn Rollo
Danny Gatton
Mick Ronson
Hubert Sumlin
Vernon Reid
Link Wray
Jerry Miller
Steve Howe
Eddie Van Halen
Lightnin' Hopkins
Joni Mitchell
Trey Anastasio
Johnny Winter
Adam Jones
Ali Farka Toure
Henry Vestine
Robbie Robertson
Cliff Gallup
Robert Quine
Derek Trucks
David Gilmour
Neil Young
Eddie Cochran
Randy Rhoads
Tony Iommi
Joan Jett
Dave Davies
D Boon
Glen Buxton
Robby Krieger
Fred "Sonic" Smith
Wayne Kramer
Bert Jansch
Kevin Shields
Angus Young
Robert Randolph
Leigh Stephens
Greg Ginn
Kim Thayil
Question: Write a haiku about cats
Answer: Whiskers fluttering
A silent leap, and then: crash
My coffee mug breaks.
Question: What is a Druid?
Evidence: A druid was a member of the high-ranking class in ancient Celtic cultures. Druids were religious leaders as well as legal authorities, adjudicators, lorekeepers, medical professionals and political advisors. Druids left no written accounts. While they were reported to have been literate, they are believed to have been prevented by doctrine from recording their knowledge in written form. Their beliefs and practices are attested in some detail by their contemporaries from other cultures, such as the Romans and the Greeks.
Answer: A druid was a member of the high-ranking class in ancient Celtic cultures.
Question: When was the Victorian Football League renamed to the Australian Football League?
Answer: 1990
Question: What data sets did the authors use to evaluate their model?
Evidence: The purpose of text classification is that, given a sentence $x$ , the model should predict labels $\hat{y}$ from a pre-defined label set $\mathcal {Y}$ . From the description in the previous section, we can compute the distributed representation $\mathbf {h}_j$ of the phrase at node $j$ of a tree: 
$$\mathbf {h}_{j} &= \operatornamewithlimits{DC-TreeNN}(\mathbf {x}_j, \mathbf {h}_{j}^l, \mathbf {h}_{j}^r,\theta )$$   (Eq. 18) 
 After this recursive process, the hidden state $\mathbf {h}_R$ at the root node is used as the sentential representation, which then followed by a softmax classifier to predict the probability distribution over classes. 
$${\hat{\mathbf {y}}} = \operatornamewithlimits{softmax}(\mathbf {W}_t \mathbf {h}_R + \mathbf {b}_t)$$   (Eq. 19) 
 where ${\hat{\mathbf {y}}}$ is prediction probabilities, $\mathbf {W}_t$ and $\mathbf {b}_t$ are the parameters of the classifier.
We evaluate our models on five different datasets. The detailed statistics about the five datasets are listed in Table 2 . Each dataset is briefly described as follows.
SST The movie reviews with two classes (negative, positive) in the Stanford Sentiment Treebank BIBREF23 .
MR The movie reviews with two classes BIBREF24 .
QC The TREC questions dataset involves six different question types. BIBREF25 .
SUBJ Subjectivity dataset where the goal is to classify each instance (snippet) as being subjective or objective. BIBREF26 
IE Idiom enhanced sentiment classification. BIBREF27 . Each sentence contains at least one idiom.
As shown in Table 3 , DC-TreeLSTM consistently outperforms RecNN, MV-RecNN, RNTN, and TreeLSTM by a large margin while achieving comparable results to the CNN and using much fewer parameters.(The number of parameters in our models is approximately 10K while in CNN the number of parameters is about 400K). Compared with RecNN, DC-RecNN performs better, indicating the effectiveness of the dynamic compositional function. Additionally, both DC-RecNN and DC-TreeLSTM achieve substantial improvement on IE dataset, which covers the richness of compositionality (idiomaticity). We attribute the success on IE to its power in modeling more complicated compositionality.
• SST The movie reviews with two classes (negative, positive) in the Stanford Sentiment Treebank [Socher et al., 2013b].  • MR The movie reviews with two classes [Pang and Lee, 2005].  • QC The TREC questions dataset involves six different question types. [Li and Roth, 2002].  • SUBJ Subjectivity dataset where the goal is to classify each instance (snippet) as being subjective or objective. [Pang and Lee, 2004]  • IE Idiom enhanced sentiment classification. [Williams et al., 2015]. Each sentence contains at least one idiom.
Answer: • SST The movie reviews with two classes (negative, positive) in the Stanford Sentiment Treebank. • MR The movie reviews with two classes. • QC The TREC questions dataset involves six different question types. • SUBJ Subjectivity dataset where the goal is to classify each instance (snippet) as being subjective or objective. • IE Idiom enhanced sentiment classification.
Question: Who was favored to win the 2023 NCAA Men's Collegiate Basketball Championship game?
Answer: The final odds from the primary online gambling sites in the 2023 NCAA Men's Collegiate Basketball Championship game gave the University of Connecticut (UCONN) a 7.5 point advantage over San Diego State University (SDSU).
Question: What are the four great inventions of Ancient China?
Answer: The four great inventions of ancient China are paper-making, printing, gunpowder and compass. Paper that was most similar to modern materials, was first invented by a court official named Cai Lun in the early 2nd century China. It was relatively cheap, light and suitable for brush writing, and was produced using bark, rags, wheat stalks and other materials. As for printing, the first book with a verifiable date of printing was a Buddhist sutra that was printed in the Tang Dynasty, around the year of 868. The use of gunpowder technique was first discovered by Chinese monks in the 9th century CE, during their quest for a life-extending elixir. Last but not least, one of the first books to describe the magnetic compass was written by Shen Kuo in the Song Dynasty, 11th Century CE.
Question: What college did David Gilhooly go to?
Evidence: He enrolled in University of California, Davis (UC Davis) initially studying biology, followed by anthropology, and ending with a focus on fine art. While attending UC Davis, Gilhooly served as artist Robert Arneson's assistant starting in 1963. He graduated from UC Davis with a BA degree in 1965, and an MA degree in 1967.
Answer: David Gilhooly went to the University of California, Davis (UC Davis).
Question: What is a vegetarian diet?
Answer: A vegetarian diet is one in which someone eats no meat, but will consume other animal products such as eggs or dairy
Question: What is the symbol layout tree introduced by Zanibbi et al.?
Evidence: Word embeddings were first introduced in BIBREF2 , BIBREF3 and there have been many variants BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . Common for all of them is the idea that words can be represented by latent feature vectors. These feature vectors are optimized to maximize the conditional probability of the dataset. Recently BIBREF1 extended the idea of word embeddings to other types of data. EqEmb expand the idea of word embeddings to a new type of data points – equations.
There have been different proposed approaches for representing mathematical equations. BIBREF8 introduced the symbol layout tree, a representation that encodes the spatial relationship of variables and operators for the purpose of indexing and retrieving mathematical equations. Our work also falls into the framework of mathematical language processing (MLP) BIBREF9 whose first step is converting mathematical solutions into a series of numerical features.
There have been different proposed approaches for representing mathematical equations. Zanibbi et al. (2016) introduced the symbol layout tree, a representation that encodes the spatial relationship of variables and operators for the purpose of indexing and retrieving mathematical equations. Our work also falls into the framework of mathematical language processing (MLP) (Lan et al., 2015) whose first step is converting mathematical solutions into a series of numerical features.
Answer: A representation that encodes the spatial relationship of variables and operators for the purpose of indexing and retrieving mathematical equations.
Question: FL clients should not be stateful, do you agree?
Evidence: Table 9: Comparison of FedBYOL without exponential moving average (EMA) and stop-gradient (sg) on the CIFAR datasets. FedBYOL w/o EMA and sg can hardly learn, but updating both Wk and Wtk with Wg (update-both) enables it to achieve comparable results.
Answer: Thank you for raising the concern. Actually, this paper focuses on cross-silo FL rather than cross-device FL and we have revised the manuscript to clarify this point. We have also refined Algorithm 1 to handle client sampling on every training round. Specifically, when clients are newly sampled, we update their online and target encoders with the global model; when clients are sampled in the previous round, we update only the online encoders with EMA. In definition, cross-device FL assumes there are millions of stateless clients that might participate in training just once. While due to the constraints of experimental settings, the majority of studies conduct experiments with at most hundreds of clients. FedEMA can work under such experimental settings by caching states of clients in the server. When the number of clients scales to millions, FedEMA degrades to FedBYOL that updates both encoders --- without keeping any local states. We have included the discussion on cross-silo FL and cross-device FL in Appendix B.3.
Question: What was previous state-of-the-art approach?
Evidence: The first five methods are the most common class (MCC), $KLD$, $LS \cup KLD$, $LS \cup KLD \cup CONN$, and $KLD \cup LS \cup LS_{inter}$. $KLD$, $LS$, and $CONN$ represent KL-divergence score, lexical semantic feature, and categorical feature respectively. These methods are used as baselines in Hidey et al.'s work. $KLD$ and $LS \cup KLD$ acquire the best accuracy and precision on the Training set. $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$ are the best systems with the highest recall and F1-score respectively. The next five are the most commonly used methods in text classification. They are TextCNN, TextRNN, SASE, DPCNN, and BERT. In our experiment, we reproduced all of them except BERT. For BERT, we use the public released pre-trained language model (base). and fine-tuned it on each dataset. The detailed information about these baselines is listed as follows:
$LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$ are the best systems with the highest recall and F1-score respectively.
Answer: The answers are shown as follows:
* TextCNN, TextRNN, SASE, DPCNN, and BERT
* $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$ are the best systems with the highest recall and F1-score respectively
Question: What are some examples of tasks performed by AI?
Evidence: Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to intelligence of humans and other animals. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.
Answer: Examples of tasks performed by AI include speech recognition, computer vision, translation between (natural) languages, and other mappings of inputs.
Question: "we report the max average score as performance", this is ambiguous. As I see it, we have different points along the x-axis and different seeds. What are taking the maximum over? What are we averaging over?"
Evidence: 4 methods, i.e., GCQL (Ours), CQL, REDQ and AWAC, are tested on 3 tasks, i.e., Walker, Hopper and HalfCheetah, and each task has 5 different kinds of offline dataset, which are random-v0, medium-v0, expert-v0, medium-expert-v0, and medium-replay-v0. As online interactions are available, we take the maximization over testing scores during the whole training process, and report the average of these max scores across five different seeds in Table. Please note that the results of AWAC are directly taken from their paper. As shown in Table, our method gains a better performance than the baselines in all tasks. Particularly, for the medium-replay and random dataset, our GCQL outperforms the baselines by a large margin. By contrast, CQL also achieves an expert performance on the high-quality dataset, e.g., expert and medium-expert tasks. For these high-quality datasets, our method achieves a comparable or slightly better performance over CQL. In terms of the online RL method REDQ, it fails on almost all tasks. Even though we employ an online limited exploration process, conventional online RL cannot benefit from such limited experiences. On the contrary, such limited online experiences could bring catastrophic consequences, e.g., on the halfcheetah-medium-replay task, REDQ suffers a significant instability issue. On the other hand, when dataset quality is high, e.g., expert or medium-expert dataset, our method can leverage the benefit of the offline RL that gains an expert performance with very limited interaction steps, such as around 20K steps. By contrast, when dataset quality is poor, e.g., the random or medium-replay dataset, only our method can learn effectively with less than 100K online steps, which demonstrate that our method can take the advantage of the limited online experiences as much as possible. In sum, our method can benefit the most from the limited online experience while still maintain the offline learning's ability. Besides, We also include the corresponding learning curves in Appendix A for a comprehensive understanding. These learning curves demonstrate that our method also achieves a better final average score at the last iteration in most cases.
As online interactions are available, we take the maximization over testing scores during the whole training process, and report the average of these max scores across five different seeds in Table 1.
Answer: The maximum is the max score for each seed. and we average across different seeds. We have updated it accordingly in section 5.2.
Question: How do the authors train the models in this paper?
Evidence: It is well known that language has certain structural properties which allows natural language speakers to make “infinite use of finite means" BIBREF3 . This structure allows us to generalize beyond the typical machine learning definition of generalization BIBREF4 (which considers performance on the distribution that generated the training set), permitting the understanding of any utterance sharing the same structure, regardless of probability. For example, sentences of length 100 typically do not appear in natural text or speech (our personal 'training set'), but can be understood regardless due to their structure. We refer to this notion as linguistic generalization .
Many problems in NLP are treated as sequence to sequence tasks with solutions built on seq2seq-attention based models. While these models perform very well on standard datasets and also appear to capture some linguistic structure BIBREF5 , BIBREF6 , BIBREF7 , they also can be quite brittle, typically breaking on uncharacteristic inputs BIBREF8 , BIBREF1 , indicating that the extent of linguistic generalization these models achieve is still somewhat lacking.
Due to the high capacity of these models, it is not unreasonable to expect them to learn some structure from the data. However, learning structure is not a sufficient condition to achieving linguistic generalization. If this structure is to be usable on data outside the training distribution, the model must learn the structure without additionally learning (overfitting on) patterns specific to the training data. One may hope, given the right hyperparameter configuration and regularization, that a model converges to a solution that captures the reusable structure without overfitting too much on the training set. While this solution exists in theory, in practice, it may be difficult to find.
In this work, we look at the feasibility of training and tuning seq2seq-attention models towards a solution that generalizes in this linguistic sense. In particular, we train models on a symbol replacement task with a well defined generalizable structure. The task is simple enough that all models achieve near perfect accuracy on the standard test set, i.e., where the inputs are drawn from the same distribution as that of the training set. We then test these models for linguistic generalization by creating test sets of uncharacteristic inputs, i.e., inputs that are not typical in the training distribution but still solvable given that the generalizable structure was learned. Our results indicate that generalization is highly sensitive; such that even changes in the random seed can drastically affect the ability to generalize. This dependence on an element that is not (or ideally should not be) a hyperparameter suggests that the line between generalization and failure is quite fine, and may not be feasible to reach simply by hyperparameter tuning alone.
In particular, we train models on a symbol replacement task with a well defined generalizable structure.
Answer: The authors train models on a symbol replacement task with a well defined generalizable structure.
Question: Identify which instrument is string or percussion: Rototom, Mugni
Answer: Mugni is string, Rototom is percussion.
Question: what pretrained models were used?
Evidence: The current release of Katecheo uses a Bi-Directional Attention Flow, or BiDAF, model for reading comprehension BIBREF6 . This BiDAF model includes a Convolutional Neural Network (CNN) based character level embedding layer, a word embedding layer that uses pre-trained GloVE embeddings, a Long Short-Term Memory Network (LSTM) based contextual embedding layer, an “attention flow layer", and a modeling layer include bi-directional LSTMs. We are using a pre-trained version of BiDAF available in the AllenNLP BIBREF7 library.
Future releases of Katecheo will include the ability to swap out the reading comprehension model for newer architectures based on, e.g., BERT BIBREF8 or XLNet BIBREF9 or custom trained models.
Architecture and Configuration
The current release of Katecheo uses a Bi-Directional Attention Flow, or BiDAF, model for reading comprehension BIBREF6 .
Future releases of Katecheo will include the ability to swap out the reading comprehension model for newer architectures based on, e.g., BERT BIBREF8 or XLNet BIBREF9 or custom trained models.

Architecture and Configuration
Answer: The answers are shown as follows:
* BiDAF
* BERT
Question: Given the following paragraph on the water cycle, what forms of precipitation do exist?
Evidence: Most water vapors found mostly in the ocean returns to it, but winds carry water vapor over land at the same rate as runoff into the sea, about 47 Tt per year whilst evaporation and transpiration happening in land masses also contribute another 72 Tt per year. Precipitation, at a rate of 119 Tt per year over land, has several forms: most commonly rain, snow, and hail, with some contribution from fog and dew. Dew is small drops of water that are condensed when a high density of water vapor meets a cool surface. Dew usually forms in the morning when the temperature is the lowest, just before sunrise and when the temperature of the earth's surface starts to increase. Condensed water in the air may also refract sunlight to produce rainbows.
Answer: Water can precipitate in the form of rain, snow, and hail as well as in the form of fog and dew. Rain is the most common form of precipitation.
Question: How many agents do they ensemble over?
Evidence: Ensemble, which selects a sentence using 100 agents trained on clustered dialogues as described in section SECREF4 – the agent in focus is chosen using a regressor as predictor of dialogue reward INLINEFORM0 using a similar neural net as the ChatDQN agents except for the final layer having one node and using Batch Normalisation BIBREF44 between hidden layers as in BIBREF36 ;
Ensemble, which selects a sentence using 100 agents trained on clustered dialogues as described in section SECREF4 – the agent in focus is chosen using a regressor as predictor of dialogue reward INLINEFORM0 using a similar neural net as the ChatDQN agents except for the final layer having one node and using Batch Normalisation BIBREF44 between hidden layers as in BIBREF36 ;
Answer: The answers are shown as follows:
* 100
Question: What do you think a conversation between two AI bots would look like?
Answer: I do not know but I would like to imagine the two bots are both trained on enormous corpse of data available online. If the datasets are different, the conversation could go very interesting and unexpected for humans ways. The common ML algorithms might be similar or shared so I wonder if the reasoning behind the bots will be similar and a pattern could be drawn on them.
Question: What is the difference between American Football and Futbol?
Answer: In America, football refers to the NFL style of football. This involves heavily padded players, scripted plays and a game where the goal is to get the ball into an endzone with a mix of pass and run plays. International Futbol refers to the game that is played primarily with a players feet, passing the ball and shooting with only their feet of head. Players in Futbol are not allowed to use their hands unless they are the goalie defending the goal. The goal of Futbol is to get the ball into the opposing team's net.
Question: What is the Nehemiah Hubbard House
Answer: The Nehemiah Hubbard House is a historic house at the corner of Laurel Grove and Wadsworth Street, Middletown, Connecticut. Built in 1745, it is a center-chimney colonial style house built of clapboard siding and brownstone foundation with wood shingle roof; using a structural system of wood frame, post and beam with gable roof. It was built as a residence which is its current use.