DataHammer/scimrc huggingface dataset:
Question: What do they examine in the paper?
Evidence:  INLINEFORM0 Work performed while the author was an intern at Google.
Language models are vital components of a wide variety of systems for Natural Language Processing (NLP) including Automatic Speech Recognition, Machine Translation, Optical Character Recognition, Spelling Correction, etc. However, most language models are trained and applied in a manner that is oblivious to the environment in which human language operates BIBREF0 . These models are typically trained only on sequences of words, ignoring the physical context in which the symbolic representations are grounded, or ignoring the social context that could inform the semantics of an utterance.
For incorporating additional modalities, the NLP community has typically used datasets such as MS COCO BIBREF1 and Flickr BIBREF2 for image-based tasks, while several datasets BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 have been curated for video-based tasks. Despite the lack of big datasets, researchers have started investigating language grounding in images BIBREF8 , BIBREF9 , BIBREF10 and to lesser extent in videos BIBREF11 , BIBREF1 . However, language grounding has focused more on obtaining better word and sentence representations or other downstream tasks, and to lesser extent on language modeling.
In this paper, we examine the problem of incorporating temporal visual context into a recurrent neural language model (RNNLM). Multimodal Neural Language Models were introduced in BIBREF12 , where log-linear LMs BIBREF13 were conditioned to handle both image and text modalities. Notably, this work did not use the recurrent neural model paradigm which has now become the de facto way of implementing neural LMs.
The closest work to ours is that of BIBREF0 , who report perplexity gains of around 5–6% on three languages on the MS COCO dataset (with an English vocabulary of only 16K words).
Our work is distinguishable from previous work with respect to three dimensions:
In this paper, we examine the problem of incorporating temporal visual context into a recurrent neural language model (RNNLM).
Answer: The problem of incorporating temporal visual context into a recurrent neural language model (RNNLM).
Question: How many times did Jorge compete in the Olympics?
Evidence: Jorge Rubén García Velazco (born 29 October 1962) is an Argentine windsurfer. He competed at the 1984 Summer Olympics and the 1988 Summer Olympics.
Answer: Two times
Question: According to Sanderson's Law of Magic, what is the difference between hard and soft magic?
Evidence: The idea of hard magic and soft magic was popularized by Sanderson for world building and creating magic systems in fictional settings. The terminology of hard and soft originate from hard and soft sciences, which lends itself towards hard science fiction and soft science fiction. Both terms are approximate ways of characterizing two ends of a spectrum. Hard magic systems follow specific rules, the magic is controlled and explained to the reader in the narrative detailing the mechanics behind the way the magic 'works' and can be used for building settings that revolve around the magic system. Soft magic systems may not have clearly defined rules or limitations, or they may provide limited exposition regarding their workings. They are used to create a sense of wonder to the reader.
Answer: Hard magic is always strictly constrained by a set of rules which can simulate a more ordered world to the reader, while soft magic if more flexible, has no clearly defined limitations and can be used to imbue a sense of wonder to the reader.
Question: Which model architecture do they for sentence encoding?
Evidence: We select the following training objectives to learn general-purpose sentence embeddings. Our desiderata for the task collection were: sufficient diversity, existence of fairly large datasets for training, and success as standalone training objectives for sentence representations.
Multi-task training setup
Our desiderata for the task collection were: sufficient diversity, existence of fairly large datasets for training, and success as standalone training objectives for sentence representations.

Multi-task training setup
Answer: Answer with content missing: (Skip-thought vectors-Natural Language Inference paragraphs) The encoder for the current sentence and the decoders for the previous (STP) and next sentence (STN) are typically parameterized as separate RNNs
- RNN
Question: What neural language models are explored?
Evidence: Since our focus in this paper is an additional loss exploiting negative examples (Section method), we fix the baseline LM throughout the experiments. Our baseline is a three-layer LSTM-LM with 1,150 hidden units at internal layers trained with the standard cross-entropy loss. Word embeddings are 400-dimensional, and input and output embeddings are tied BIBREF11. Deviating from some prior work BIBREF0, BIBREF1, we train LMs at sentence level as in sequence-to-sequence models BIBREF12. This setting has been employed in some previous work BIBREF3, BIBREF6.
 Our baseline is a three-layer LSTM-LM with 1,150 hidden units at internal layers trained with the standard cross-entropy loss. 
Answer: The answers are shown as follows:
* LSTM-LM
Question: Which is an imperial or a metric measurement: pound, stone, kilogram
Answer: pound and stone is imperial, kilogram is metric
Question: Is the following statement true or false: cat's have wings?
Answer: It is false.  Cat's do not have wings.
Question: What is the right size brad nail for crown moulding?
Answer: In most cases you can use 18-gauge 2-in brad nails. When choosing the correct size nail you want to consider the thickness of the crown moulding, the thickness of the stud and  the thickness of your drywall.  In most cases your crown moulding will be 1/2 inch with 1/2 inch drywall. At least half of your nail should be in the stud.  If your crown moulding or drywall is thicker than 1/2 inch you should consider a longer nail.
Question: For Thai sentence segmentation, what are the three metrics that the authors use in their assessment?
Evidence: During the evaluation, each task is assessed using different metrics based on previous works. For Thai sentence segmentation, three metrics are used in the evaluation: sentence boundary F1 score, non-sentence boundary F1 score, and space correct BIBREF8 . In this work, we mainly focus on the performance of sentence boundary prediction and not non-sentence boundary prediction or space prediction. Therefore, we make comparisons with other models regarding only their sentence boundary F1 scores. The equation for the sentence boundary F1 score metric is shown in f1sb. In calculating the F1 score, the positive class is defined as the sentence boundary, and the negative class is defined as the non-sentence boundary. INLINEFORM0 INLINEFORM1 
For English punctuation, the evaluation is measured on each type of punctuation and overall F1 score. For the punctuation restoration task, we care only about the performance of the samples belonging to the classes that are tagged to words followed by punctuation; therefore class INLINEFORM0 , which represents words not immediately followed by punctuation, is ignored in the evaluation. Consequently, the overall F1 score does not include INLINEFORM1 as the positive class in f1overall. INLINEFORM2 INLINEFORM3 
To compare the performance of each punctuation restoration model in a manner similar to sentence segmentation, the 2-class F1 score is calculated to measure model accuracy, as shown in f12class. The calculation of this metric is the same as that used in BIBREF35 . The metric considers only where the punctuation position is and ignores the type of restored punctuation. Therefore, this measure is similar to the metric sentence boundary F1, which only considers the position of the missing punctuation. INLINEFORM0 INLINEFORM1 
For Thai sentence segmentation, three metrics are used in the evaluation: sentence boundary F1 score, non-sentence boundary F1 score, and space correct.
Answer: Sentence boundary F1 score, non-sentence boundary F1 score, and space correct.
Question: How to optimize the pre-training process of BERT?
Evidence: BERT (Bidirectional Encoder Representations from Transformers) BIBREF14 is a new language representation model, which uses bidirectional transformers to pre-train a large unlabeled corpus, and fine-tunes the pre-trained model on other tasks. BERT has been widely used and shows great improvement on various natural language processing tasks, e.g., word segmentation, named entity recognition, sentiment analysis, and question answering. We use BERT to extract contextual feature for each character instead of BiLSTM in the original work BIBREF13. To further improve the performance, we optimize the pre-training process of BERT by introducing a semantic-enhanced task.
Original google BERT is pre-trained using two unsupervised tasks, masked language model (MLM) and next sentence prediction (NSP). MLM task enables the model to capture the discriminative contextual feature. NSP task makes it possible to understand the relationship between sentence pairs, which is not directly captured by language modeling. We further design a semantic-enhanced task to enhance the performance of BERT. It incorporate previous sentence prediction and document level prediction. We pre-train BERT by combining MLM, NSP and the semantic-enhanced task together.
To further improve the performance, we optimize the pre-training process of BERT by introducing a semantic-enhanced task.
Answer: The authors optimize the pre-training process of BERT by introducing a semantic-enhanced task.
Question: Who was the last king of the Reach before Aegon's Conquest?
Answer: Mern IX Gardener was the last king of the Reach. When the last of the Gardener's lineage died, the house went extinct and the Reach was assigned to House Tyrell to rule.
Question: Classify each of the following as a novel or poem: "A Silvia", "L'Infinito" , "I Promessi Sposi"
Answer: " A Silvia" is a poem, " L'Infinito" is a poem, " I Promessi Sposi" is a novel
Question: Give me a list of science fiction books that every sci-fi fan should read.
Answer: The following books are strongly recommended for any science fiction fan to read:
- The Three Body Problem Trilogy by Cixin Liu
- Dune by Frank Herbert
- The Robot Series by Isaac Asimov
- Foundation Trilogy by Isaac Asimov
- Ender's Game by Orson Scott Card
- A Hitchhiker's Guide to the Galaxy by Douglas Adams
- The Stand by Stephen King
- The Children of Men by PD James
Question: Identify which animal species is alive or extinct: Cave Lion, Sumatran Elephant
Answer: Sumatran Elephant is alive, Cave Lion is extinct.
Question: What was the first toy to be advertised on television?
Answer: Mr Potato Head.
Question: Find all of the Amsterdam museums mentioned in the text and put them in a numbered list.
Evidence: Amsterdam's main attractions include its historic canals; the Rijksmuseum, the state museum with a vast collection of Dutch Golden Age art; the Van Gogh Museum; the Dam Square, where the Royal Palace of Amsterdam and former city hall (stadhuis) are located; the Amsterdam Museum; Stedelijk Museum, with modern art; Hermitage Amsterdam, the Concertgebouw concert hall; the Anne Frank House; the Het Scheepvaartmuseum, the Heineken Experience, the Natura Artis Magistra; Hortus Botanicus, NEMO, the red-light district and many cannabis coffee shops. The city is also well known for its nightlife and festival activity; with several of its nightclubs (Melkweg, Paradiso) among the world's most famous. Primarily known for its artistic heritage, elaborate canal system and narrow canal houses with gabled façades; well-preserved legacies of the city's 17th-century Golden Age, and the establishment of the Van Gogh Museum, displaying the work of the famous Dutch modern artist, have attracted millions of visitors to Amsterdam annually.
Answer: The Amsterdam museums mentioned in this text are:
1. Rijksmuseum
2. Van Gogh Museum
3. Amsterdam Museum
4. Stedelijk Museum
5. Hermitage Amsterdam
6. Anne Frank House
7. Het Scheepvaartmuseum
8. NEMO
Question: Which conventional alignment models do they use as guidance?
Evidence: Inspired by the supervised reordering in conventional SMT, in this paper, we propose a Supervised Attention based NMT (SA-NMT) model. Specifically, similar to conventional SMT, we first run off-the-shelf aligners (GIZA++ BIBREF3 or fast_align BIBREF4 etc.) to obtain the alignment of the bilingual training corpus in advance. Then, treating this alignment result as the supervision of attention, we jointly learn attention and translation, both in supervised manners. Since the conventional aligners delivers higher quality alignment, it is expected that the alignment in the supervised attention NMT will be improved leading to better end-to-end translation performance. One advantage of the proposed SA-NMT is that it implements the supervision of attention as a regularization in the joint training objective (§3.2). Furthermore, since the supervision of attention lies in the middle of the entire network architecture rather than the top ( as in the supervision of translation (see Figure 1(b)), it serves to mitigate the vanishing gradient problem during the back-propagation BIBREF7 .
Inspired by the supervised reordering in conventional SMT, in this paper, we propose a Supervised Attention based NMT (SA-NMT) model. Specifically, similar to conventional SMT, we first run off-the-shelf aligners (GIZA++ BIBREF3 or fast_align BIBREF4 etc.) to obtain the alignment of the bilingual training corpus in advance. Then, treating this alignment result as the supervision of attention, we jointly learn attention and translation, both in supervised manners. Since the conventional aligners delivers higher quality alignment, it is expected that the alignment in the supervised attention NMT will be improved leading to better end-to-end translation performance.
Answer: The answers are shown as follows:
* GIZA++ BIBREF3 or fast_align BIBREF4
Question: How do I know it is the spring season?
Answer: Spring is typically associated with birth and renewal. This is often reflected by weather such as rain and more sunny days than seen in winter. Temperatures often increase. And one can witness new foliage or flower growth, appearance of newborn wildlife, and an increase in bird singing. People often report being happier. Spring can also represent a chance to renew habits or commitments, such as spring cleaning your home or closets.
Question: Based on the reference paragraph, when was the 149th Boat Race?
Evidence: The 149th Boat Race took place on 6 April 2003. Held annually, the Boat Race is a side-by-side rowing race between crews from the Universities of Oxford (in dark blue) and Cambridge (in light greenish blue) along a 4.2-mile (6.8 km) tidal stretch of the River Thames in south-west London. The lead changed twice during the race, which Oxford won by one foot (30 cm), the smallest margin of victory in the history of the event. The close race has been described as "epic", while Olympic gold medallist Steve Redgrave suggested that the race was the "greatest we will see in our lifetimes".
Answer: April 6, 2003
Question: Extract out the various endorsements Twice has done and separate them with a comma
Evidence: Endorsements:

Even before the official debut of Twice, the nine members had signed contracts as exclusive models for school uniform brand Skoolooks, alongside J. Y. Park. By December 2015, Twice had signed ten CF contracts, totaling earnings of KRW1.8 billion. By February 2017, they had one of the fastest growths in the advertising industry as the group's rate charged for endorsement hit 200 million won for 6 months and 300 million won for a year.

Twice is one of the celebrity endorsers of Lotte Duty Free. They also collaborated with shoe company Spris and created their own shoe brand called "Twice by Spris". In early 2017, Twice was selected by beverage maker Donga Otsuka to promote the company's flagship sports drink Pocari Sweat on its 30th anniversary. They became the first idol group to model for the brand. Pocari Sweat recorded 100 billion won sales for the first half of the year, a 10% increase over 2016's sales.

As of 2017, Twice has represented about 30 different brands including cosmetics, clothing, online and mobile games, food and beverages, credit cards, and more. Twice's first TV commercial in Japan for Ymobile was aired nationwide starting February 2, 2018, incorporating a pop arrangement of "Y.M.C.A." and Twice's signature "TT pose". In 2019, Twice was selected as South Korean ambassador of Estée Lauder. The group also became "Global Benchsetters" for Philippine clothing brand Bench. In March 2021, Kyungnam Pharmaceuticals announced that it had selected Twice as the representatives for its flagship vitamin brand Lemona. Later that year, Nintendo Korea featured Twice playing the Nintendo Switch game Mitopia in a video. In August 2021, the group was chosen to perform for Shopee Philippines's 9.9 Super Shopping Day TV special that was held on September 9. On October 16, 2021, Scarlett Whitening, an Indonesian beauty brand known for its skin whitening products, introduced Twice as their brand ambassadors. The endorsement was, however, criticized by fans, calling JYP Entertainment to withdraw the deal as it would be "unethical" for Twice to endorse a brand with skin whitening products, "knowing that they have fans with darker skin complexions".
Answer: Skoolooks,Lotte Duty Free,Spris,Pocari Sweat,Ymobile,Estée Lauder,Bench,Lemona,Nintendo Korea for Mitopia,Scarlett Whitening
Question: what are ways to improve public speaking skills?
Answer: practice, practice, practice! Utilize every opportunity that you can practice, whether a prepared speech or an impromptu one. Effective practices make a big difference. For introverted speakers or who are afraid the stage, the first step is to find a safe and supportive environment to try. Consider joining a toastmaster club. Start by talking about topics that you are most comfortable and familiar with, like yourself! To improve faster, find a mentor or a trusted person to evaluate your speeches and provide constructive feedback. You can also record or tape yourself and watch it afterward. Try to pay attention to the areas of improvement next time when you speak. Be mindful whenever you speak.
Question: Categorize the below types of organisms.
bacteriophages, coccus, bacillus, spiral, coryneform, filamentous
Answer: Bacteria - coccus, bacillus, spiral, coryneform, filamentous
Virus - bacteriophages
Question: What are labels available in dataset for supervision?
Evidence: Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (NLP) applications such as dialogue systems BIBREF1, question-answering systems BIBREF2, and humor recognition BIBREF3. In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive).
In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive).
Answer: The answers are shown as follows:
* negative
* positive
Question: Which training dataset allowed for the best generalization to benchmark sets?
Evidence: FLOAT SELECTED: Table 4: Test accuracies (%). For the baseline results (highlighted in bold) the training data and test data have been drawn from the same benchmark corpus. ∆ is the difference between the test accuracy and the baseline accuracy for the same training set. Results marked with * are for the development set, as no annotated test set is openly available. Best scores with respect to accuracy and difference in accuracy are underlined.
FLOAT SELECTED: Table 4: Test accuracies (%). For the baseline results (highlighted in bold) the training data and test data have been drawn from the same benchmark corpus. ∆ is the difference between the test accuracy and the baseline accuracy for the same training set. Results marked with * are for the development set, as no annotated test set is openly available. Best scores with respect to accuracy and difference in accuracy are underlined.
Answer: MultiNLI
Question: Who are the top Presidents of USA
Answer: Abraham Lincoln, Franklin D. Roosevelt, and George Washington are most often listed as the three highest-rated presidents among historians. 
More recent presidents such as Ronald Reagan and Bill Clinton are often rated among the greatest in public opinion polls, but generally do not rank as highly among presidential scholars and historians. 
Because William Henry Harrison and James A. Garfield both died within their first year in office, they are often omitted from presidential rankings. Zachary Taylor died after serving as president for only 16 months, but he is usually included. In the case of these three presidents, it is not clear whether they received low rankings due to their actions as president or because each was in office for such a limited time that they did not accomplish much.
Question: What is deep learning?
Evidence: Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.

The adjective "deep" in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, but that a network with a nonpolynomial activation function with one hidden layer of unbounded width can. Deep learning is a modern variation that is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability.
Answer: Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning.
Question: How many paralympics did Paraskevi compete in?
Evidence: Paraskevi has competed in three Paralympics competing in the T11 100m, 200m and long jump. Her first games were the 2004 Summer Paralympics in her home country where she won a bronze in the 100m. She was unable to match this in Beijing in the 2008 Summer Paralympics and in London in 2012 Summer Paralympics where she failed to win any medals.
Answer: Paraskevi competed in three paralympics.
Question: How many syllables are in the word cat?
Answer: The word cat has only one syllable.