DataHammer/scimrc huggingface dataset:
Question: What kind of task does the decoder handle compared with the encoder?
Evidence: The encoder-decoder based framework BIBREF0, BIBREF1, BIBREF2 is the dominant approach for neural machine translation (NMT) BIBREF3, BIBREF4. Although the encoder and decoder usually adopt the same model structure (RNN BIBREF5, CNN BIBREF6 or self-attention BIBREF3, BIBREF7) and the same number of layers, they perform different functionalities: the encoder extracts the hidden representations of the source sentence, and the decoder generates target tokens conditioned on the source hidden representations as well as the previous generated tokens.
While most existing works focus on the design and improvement of encoder-decoder framework for NMT BIBREF8, BIBREF6, BIBREF3, BIBREF9 as well as its detailed analyses BIBREF10, BIBREF11, BIBREF12, BIBREF13, few works concentrate on the characteristics and functionalities of the encoder and the decoder, which are valuable to understand this popular framework and improve its performance in NMT. Therefore, in this paper, we conduct a study and aim to understand the characteristics of the encoder and the decoder in NMT. We observe some interesting phenomena:
The decoder handles an easier task than the encoder. 1) We find that adding more layers to the encoder achieves larger improvements than adding more layers to the decoder. 2) We also compare the training time of the encoder and decoder by fixing the parameters of a well-trained decoder (encoder), and just update the parameters of the encoder (decoder). We found that the decoder converges faster than the encoder. These two results suggest that the decoder handles an easier task than the encoder in NMT.
The decoder is more sensitive to the input noise than the encoder. We randomly add different level of noise to the input of the encoder and decoder respectively during inference, and find that adding noise to the input of the decoder leads to better accuracy drop than that of the encoder.
We further analyze why the decoder is more sensitive by masking the previous tokens, and comparing autoregressive NMT with the non-autoregressive counterpart. We find that the preceding tokens in the decoder provide strong conditional information, which partially explain the previous two observations on the decoder.
We believe our studies on the different characteristics of the encoder and decoder will inspire the following research on the encoder-decoder framework as well as improve the performance on NMT and other encoder-decoder based tasks.
The decoder handles an easier task than the encoder.
Answer: An easier task.
Question: Identify which instrument is string or percussion: Crotale, Kannel
Answer: Kannel is string, Crotale is percussion.
Question: Classify these quarterback as either won Superbowl or did not win: Joe Montana, Dan Marino, Jim Kelly, John Elway.
Answer: Won Superbowl: Joe Montana, John Elway
Did not win Superbowl: Dan Marino, Jim Kelly
Question: Prepare the count of trains which serves Mount Prospect for each day in a week starting with Monday. Use the format “Day-Count” and each entry should be in a new line.
Evidence: Mount Prospect is a station on Metra's Union Pacific Northwest Line located in Mount Prospect, Illinois. The station is located at 13 E. Northwest Highway near its intersection with Main St. (Illinois Route 83). Mount Prospect is 20.0 miles (32.2 km) from Ogilvie Transportation Center, the Northwest Line's southern terminus. In Metra's zone-based fare structure, Mount Prospect is located in zone D. As of 2018, Mount Prospect is the 11th busiest of the 236 non-downtown stations in the Metra system, with an average of 1,879 weekday boardings.The station has three tracks; an outbound track to the southwest, an inbound track to the northeast, and a bidirectional express track in the middle. There is a station house on the inbound platform where tickets may be purchased. Parking is available near Mount Prospect.As of April 3, 2023, Mount Prospect is served by 62 trains (31 in each direction) on weekdays, by 31 trains (16 inbound, 15 outbound) on Saturdays, and by 19 trains (nine inbound, 10 outbound) on Sundays.
Answer: Monday-62
Tuesday-62
Wednesday-62
Thursday-62
Friday-62
Saturday-31
Sunday-19
Question: Imagine you are walking alone on the sidewalk of a residential street, and a large dog comes running toward you. It does not look aggressive, but it is also showing no fear. What might be an appropriate course of action.
Answer: Because you are in a residential area, it is likely that the dog is someone's lost pet. If you are comfortable around dogs, it would be good to allow the dog to come close enough to you that you can see whether it has a collar and tags, and if so, to contact the owner listed on the tags. If the dog has no identification, it might be best to call animal control. And of course, be wary of signs of aggression, including any symptoms of rabies.
Question: Understanding these limitations, what model does the author propose?
Evidence: In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models. The results show significant improvement with an average of 26.8 point increase in F-measure on our test data and 38.6 increase on the totally new dataset.
Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models
Answer: They present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models
Question: What are 5 popular reality TV shows?
Answer: 1. The Real Housewives
2. Survivor
3. The Bachelor
4. RuPaul's Drag Race
5. The Voice
Question: Why are drill jigs less commonly used today than they were in the past?
Evidence: A drill jig is a type of jig that expedites repetitive hole center location on multiple interchangeable parts by acting as a template to guide the twist drill or other boring device into the precise location of each intended hole center. In metalworking practice, typically a hardened drill bushing lines each hole on the jig plate to keep the tool from damaging the jig.

Drill jigs started falling into disuse with the invention of the jig borer.

Since the widespread penetration of the manufacturing industry by CNC machine tools, in which servo controls are capable of moving the tool to the correct location automatically, the need for drill jigs (and for the jobs of the drill press operators who used them) is much less than it used to be.
Answer: Because of CNC machine tooling, which automate much of the drilling process
Question: What do I need to learn how to knit?
Answer: To learn to knit you will need a few basic knitting supplies. First, you will need some yarn, knitting needles, and if you’d like, a pattern. Knitting needles come in many sizes so if you plan to make a specific pattern follow the pattern instructions. You begin knitting by casting on a certain amount of stitches, then follow the pattern for specific knit or purl stitches.
Question: Which is bigger, a lion or a house cat?
Answer: A lion is bigger than a house cat
Question: What is blockchain technology?
Evidence: A blockchain is a distributed ledger with growing lists of records (blocks) that are securely linked together via cryptographic hashes.Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree, where data nodes are represented by leaves). The timestamp proves that the transaction data existed when the block was created. Since each block contains information about the previous block, they effectively form a chain (compare linked list data structure), with each additional block linking to the ones before it. Consequently, blockchain transactions are irreversible in that, once they are recorded, the data in any given block cannot be altered retroactively without altering all subsequent blocks.
Answer: A decentralized, distributed ledger that securely and openly records transactions is defined.

Blockchain technology was first introduced with the launch of Bitcoin in 2008 and has since grown to be used in a wide range of applications.

Technical details: Blockchain secures and verifies transactions using cryptography and hashing, and validates them using consensus techniques.

Applications: In addition to cryptocurrencies, blockchain has been utilized in supply chain management, voting systems, smart contracts, and other areas.

Limitations: Blockchain is criticized for using a lot of energy and for having problems scaling.
Question: What dataset do they use?
Evidence: In this paper, we work with the dataset of the 2019 GermEval shared task on hierarchical text classification BIBREF0 and use the predefined set of labels to evaluate our approach to this classification task.
hierarchical
In this paper, we work with the dataset of the 2019 GermEval shared task on hierarchical text classification BIBREF0 and use the predefined set of labels to evaluate our approach to this classification task.
Answer: The answers are shown as follows:
* 2019 GermEval shared task on hierarchical text classification
Question: List the countries north of the arctic circle ordered by the closest to the north pole
Answer: Greenland (Denmark), Canada, Russia, Norway, United States, Finland, Sweden, Iceland
Question: Through the analysis of the added interpretability of these models, what do they identify?
Evidence: The Transformer architecture BIBREF0 for deep neural networks has quickly risen to prominence in NLP through its efficiency and performance, leading to improvements in the state of the art of Neural Machine Translation BIBREF1, BIBREF2, as well as inspiring other powerful general-purpose models like BERT BIBREF3 and GPT-2 BIBREF4. At the heart of the Transformer lie multi-head attention mechanisms: each word is represented by multiple different weighted averages of its relevant context. As suggested by recent works on interpreting attention head roles, separate attention heads may learn to look for various relationships between tokens BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9.
The attention distribution of each head is predicted typically using the softmax normalizing transform. As a result, all context words have non-zero attention weight. Recent work on single attention architectures suggest that using sparse normalizing transforms in attention mechanisms such as sparsemax – which can yield exactly zero probabilities for irrelevant words – may improve performance and interpretability BIBREF12, BIBREF13, BIBREF14. Qualitative analysis of attention heads BIBREF0 suggests that, depending on what phenomena they capture, heads tend to favor flatter or more peaked distributions.
Recent works have proposed sparse Transformers BIBREF10 and adaptive span Transformers BIBREF11. However, the “sparsity" of those models only limits the attention to a contiguous span of past tokens, while in this work we propose a highly adaptive Transformer model that is capable of attending to a sparse set of words that are not necessarily contiguous. Figure FIGREF1 shows the relationship of these methods with ours.
Our contributions are the following:
We introduce sparse attention into the Transformer architecture, showing that it eases interpretability and leads to slight accuracy gains.
We propose an adaptive version of sparse attention, where the shape of each attention head is learnable and can vary continuously and dynamically between the dense limit case of softmax and the sparse, piecewise-linear sparsemax case.
We make an extensive analysis of the added interpretability of these models, identifying both crisper examples of attention head behavior observed in previous work, as well as novel behaviors unraveled thanks to the sparsity and adaptivity of our proposed model.
We make an extensive analysis of the added interpretability of these models, identifying both crisper examples of attention head behavior observed in previous work, as well as novel behaviors unraveled thanks to the sparsity and adaptivity of our proposed model.
Answer: Both crisper examples of attention head behavior observed in previous work, as well as novel behaviors unraveled thanks to the sparsity and adaptivity of the proposed model.
Question: To which systems do they compare their results against?
Evidence: We compare the performance of a standard Transformer Base model and our semi-parametric NMT approach on an English-French translation task. We create a new heterogeneous dataset, constructed from a combination of the WMT training set (36M pairs), the IWSLT bilingual corpus (237k pairs), JRC-Acquis (797k pairs) and OpenSubtitles (33M pairs). For WMT, we use newstest 13 for validation and newstest 14 for test. For IWSLT, we use a combination of the test corpora from 2012-14 for validation and test 2015 for eval. For OpenSubtitles and JRC-Acquis, we create our own splits for validation and test, since no benchmark split is publicly available. After deduping, the JRC-Acquis test and validation set contain 6574 and 5121 sentence pairs respectively. The OpenSubtitles test and validation sets contain 3975 and 3488 pairs. For multi-domain training, the validation set is a concatenation of the four individual validation sets.
We compare the performance of a standard Transformer Base model and our semi-parametric NMT approach on an English-French translation task.
Answer: The answers are shown as follows:
* standard Transformer Base model
Question: What type of natural disaster is known for occurring in San Francisco, California?
Answer: Earthquakes are associated with San Francisco
Question: Compared with confidence-based methods, in what task that EGL shows better?
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.
we show EGL to be superior to confidence-based methods on speech recognition tasks.
Answer: the authors show EGL to be superior to confidence-based methods on speech recognition tasks
Question: What architectural factors were investigated?
Evidence: We find that all the factors we tested can qualitatively affect how a model generalizes on the question formation task. These factors are the type of recurrent unit, the type of attention, and the choice of sequential vs. tree-based model structure. Even though all these factors affected the model's decision between move-main and move-first, only the use of a tree-based model can be said to impart a hierarchical bias, since this was the only model type that chose a hierarchical generalization across both of our tasks. Specific findings that support these general conclusions include:
We find that all the factors we tested can qualitatively affect how a model generalizes on the question formation task. These factors are the type of recurrent unit, the type of attention, and the choice of sequential vs. tree-based model structure.
Answer: The answers are shown as follows:
* type of recurrent unit
* type of attention
* choice of sequential vs. tree-based model structure
Question: Who owns the best dog in the world?
Answer: Reedo does because his little legend loves an away day and has great chat with the fam
Question: How big is their model?
Evidence: To figure out whether the proposed AEN-GloVe is a lightweight alternative of recurrent models, we study the model size of each model on the Restaurant dataset. Statistical results are reported in Table TABREF37 . We implement all the compared models base on the same source code infrastructure, use the same hyperparameters, and run them on the same GPU .
FLOAT SELECTED: Table 3: Model sizes. Memory footprints are evaluated on the Restaurant dataset. Lowest 2 are in bold.
Statistical results are reported in Table TABREF37 .
FLOAT SELECTED: Table 3: Model sizes. Memory footprints are evaluated on the Restaurant dataset. Lowest 2 are in bold.
Answer: Proposed model has 1.16 million parameters and 11.04 MB.
Question: Name five capital cities in the USA.
Answer: Five capital cities in the USA are Tallahassee, Austin, Sacramento, Atlanta, and Raleigh.
Question: Which is a bird or fish: Yellow-legged gull, Yellowfin tuna
Answer: Yellowfin tuna is a fish, Yellow-legged gull is a bird
Question: Why do so many people say it is special to be a grandparent?
Answer: Many people say that being a grandparent is special.  They say it is special being a grandparent for a variety of reasons.  One very popular reason is they feel great pride in seeing their children grow into adulthood and take on the responsibility of having children. A second reason grandparents say their role is so special is they get to enjoy their grandchildren, and then return them to their parents who have the ongoing responsibility for raising the children. This creates a special bond between the grandparents and grandchildren that many grandparents describe as special.  A third reason many say it is special to be a grandparent is tied to an instinctive love they describe that they have for their grandchildren. You may hear them say, "I loved this grandchild from the minute I heard they were conceived, and the love has nothing to do with any merit, just love because they are my grandchild."  Having this love for another is a key factor in why it is special to be a grandparent.
Question: Are the models required to also generate rationales?
Evidence: We explore how to utilize additional commonsense knowledge (i.e. rationales) as the input to the task. Like we mentioned in Section SECREF6, we search relevant sentences from the OMCS corpus as the additional distant rationales, and ground truth rationale sentences for dev/test data. The inputs are no longer the concept-sets themselves, but in a form of “[rationales$|$concept-set]” (i.e. concatenating the rationale sentences and original concept-set strings).
We explore how to utilize additional commonsense knowledge (i.e. rationales) as the input to the task. Like we mentioned in Section SECREF6, we search relevant sentences from the OMCS corpus as the additional distant rationales, and ground truth rationale sentences for dev/test data. The inputs are no longer the concept-sets themselves, but in a form of “[rationales$|$concept-set]” (i.e. concatenating the rationale sentences and original concept-set strings).
Answer: No.
Question: What are the disadvantages of Linux?
Answer: The disadvantages of Linux include limited hardware support, a steep learning curve, lack of commercial support, and compatibility issues.
Question: Tell me whether these mountains are included in the New Hampshire 4000 footers: Washington, Adams,  Moosilauke, South Carter, Garfield, Wildcat A, Wildcat B, Wildcat C.
Answer: Mount Washington, Adams, Moosilauke, South Carter, Garfield and Wildcat A are part of the New Hampshire 4000 footers. Although Wildcat B and Wildcat C are over 4000 feet in height, they are not included in the NH 4000 footers because they do not have sufficient prominence .
Question: What classifiers were used in experiments?
Evidence: The Bi-LSTM model consists of a Bi-LSTM layer followed by a linear layer to extract input features. The Bi-LSTM layer has a 300-dimensional hidden state for each direction. For the final classification, an additional linear layer is added to output predicted class distributions. For noise handling, we experiment with the Confusion Matrix model by BIBREF38 and the Cleaning model by BIBREF39. We repeat all the Bi-LSTM experiments 20 times and report the average F1-score (following the approach by BIBREF41) and the standard error.
The BERT model is obtained by fine-tuning the pre-trained BERT embeddings on NER data with an additional untrained CRF classifier. We fine-tuned all the parameters of BERT including that of the CRF end-to-end. This has been shown to give better performance than using word features extracted from BERT to train a classifier BIBREF19. The evaluation result is obtained as an average of 5 runs, we report the F1-score and the standard error in the result section.
The Bi-LSTM model consists of a Bi-LSTM layer followed by a linear layer to extract input features. The Bi-LSTM layer has a 300-dimensional hidden state for each direction. For the final classification, an additional linear layer is added to output predicted class distributions.
The BERT model is obtained by fine-tuning the pre-trained BERT embeddings on NER data with an additional untrained CRF classifier. We fine-tuned all the parameters of BERT including that of the CRF end-to-end.
Answer: The answers are shown as follows:
* Bi-LSTM
* BERT
Question: Write an email on behalf of a friend to a colleague to set up a chat with them. The friend is interested in learning more about their current job role and current company before applying.
Answer: Hi [colleague's name],
I hope you've been well! I have a friend [friend's name] who is interested in getting more insight on your work here at [company name] and how your overall experience as a [job role] has been. Would you be free to have a short chat with them? If so, I'd be happy to arrange for you two to connect. Let me know what you think!
Best,
[your name]