Lots-of-LoRAs/task460_qasper_answer_generation huggingface dataset:
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: We evaluate our model on both English and Chinese segmentation. For both languages we used standard datasets for word segmentation and language modeling. English
The Brent corpus is a standard corpus used in statistical modeling of child language acquisition BIBREF15 , BIBREF16  We use the commonly used version of the PTB prepared by BIBREF17 Since Chinese orthography does not mark spaces between words, there have been a number of efforts to annotate word boundaries. We evaluate against two corpora that have been manually segmented according different segmentation standards. The Beijing University Corpus was one of the corpora used for the International Chinese Word Segmentation Bakeoff BIBREF18 . We use the Penn Chinese Treebank Version 5.1 BIBREF19 . 
 Question: What dataset is used?
Output: 



Answer: Brent corpus PTB  Beijing University Corpus Penn Chinese Treebank
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Bi-LSTM BIBREF11 is a baseline for neural models. Bi-LSTM$_{+ att. + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task. GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge. CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge. 
 Question: How does the neural network architecture accomodate an unknown amount of senses per word?
Output: 



Answer: converts WSD to a sequence learning task  leverage gloss knowledge by extending gloss knowledge
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Recently, MADAMIRA BIBREF6 system has been evaluated using a blind testset (25K words for Modern Standard Arabic (MSA) selected from Penn Arabic Tree bank (PATB)), and the reported accuracy was 96.2% as the percentage of words where the chosen analysis (provided by SAMA morphological analyzer BIBREF7 ) has the correct lemma. As MSA is usually written without diacritics and IR systems normally remove all diacritics from search queries and indexed data as a basic preprocessing step, so another column for undiacritized lemma is added and it's used for evaluating our lemmatizer and comparing with state-of-the-art system for lemmatization; MADAMIRA. 
 Question: What is the state of the art?
Output: 



Answer:  MADAMIRA BIBREF6 system
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Machine translation finds use in cheminformatics in “translation" from one language (e.g. reactants) to another (e.g. products). The variational Auto-encoder (VAE) is another widely adopted text generation architecture BIBREF101. Generative Adversarial Network (GAN) models generate novel molecules by using two components: the generator network generates novel molecules, and the discriminator network aims to distinguish between the generated molecules and real molecules BIBREF107. 
 Question: Are this models usually semi/supervised or unsupervised?
Output: 



Answer: Both supervised and unsupervised, depending on the task that needs to be solved.
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: To compensate for the exclusion of slot-specific parameters, we incorporate better feature representation of user utterance and dialogue states using syntactic information and convolutional neural networks (CNN). 
 Question: What network architecture do they use for SIM?
Output: 



Answer: convolutional neural networks (CNN)
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Instead, our analysis reveals that this behavior is caused by subtle artifacts arising from the translation process itself. In particular, we show that translating different parts of each instance separately (e.g. the premise and the hypothesis in NLI) can alter superficial patterns in the data (e.g. the degree of lexical overlap between them), which severely affects the generalization ability of current models. Based on the gained insights, we improve the state-of-the-art in XNLI, and show that some previous findings need to be reconsidered in the light of this phenomenon. The benchmark consists of a competence test, which evaluates the ability to understand antonymy relation and perform numerical reasoning, a distraction test, which evaluates the robustness to shallow patterns like lexical overlap and the presence of negation words, and a noise test, which evaluates robustness to spelling errors. Just as with previous experiments, we report results for the best epoch checkpoint in each test set. 
 Question: What are examples of these artificats?
Output: 



Answer: the degree of lexical overlap between them presence of negation words
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: To show the effectiveness of our approach, we show results on the SICK dataset BIBREF1, a common benchmark for logic-based NLI, and find MonaLog to be competitive with more complicated logic-based approaches (many of which require full semantic parsing and more complex logical machinery). 
 Question: Do they beat current state-of-the-art on SICK?
Output: 



Answer: No
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: The involved algorithms are detailed as follows.

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

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

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

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

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

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

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

BOW denotes the conventional algorithm which is based of bag-of-words features. 
 Question: What are the other models they compare to?
Output: 



Answer: CNN-C CNN-W CNN-Lex-C CNN-Lex-W Bi-LSTM-C  Bi-LSTM-W Lex-rule BOW
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: As previously mentioned, there is no German hate speech corpus available for our needs, especially not for the very recent topic of the refugee crisis in Europe. We therefore had to compile our own corpus. 
 Question: What languages are were included in the dataset of hateful content?
Output: 



Answer: German
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns. InvoiceNo, CustomerID and StockCode are mostly 5 or 6-digit integers with occasional letters. Quantity is mostly 1 to 3-digit integers, a part of them being negative, and UnitPrice is composed of 1 to 6 digits floating values. InvoiceDate are dates all in the same format, Country contains strings representing 38 countries and Description is 4224 strings representing names of products. We reconstruct text mails from this data, by separating each token with a blank space and stacking the lines for a given invoice, grouped by InvoiceNo. 
 Question: What is the source of the tables?
Output: 



Answer: The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns.
Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. 
 Question: How many question types do they find in the datasets analyzed?
Output: 



Answer: seven