DataHammer/scimrc huggingface dataset:
Question: How many world championships has Max Verstappen won?
Evidence: After winning the 2021 Abu Dhabi Grand Prix, Verstappen became the first Dutch driver to win the Formula One World Championship, and the 34th Formula One World Drivers' Champion. He won his second consecutive Formula One championship the next season.
Answer: Max Verstappen has won 2 Formula One World Championships. He won his first one in 2021 at the Abu Dhabi Grand Prix. In 2022 he won his second consecutive Formula One championship

As of the 3rd race of the 2023 season, he is leading the World Championship with 69 points.
Question: Are there any benefits of fasting?
Answer: Fasting is the method by which one can bring a conscious process to ingestion. It is not simply denying food and water to the system. Rather, it is a process of reducing the need for food. Fasting provides the digestive and assimilation process a break so that the rest of the body begins to excrete on the cellular level. This helps to remove all the toxic impurities that had accumulated in the cells over a long period of time. Hence intermittent fasting is extremely beneficial to the body.
Question: What are the three baseline approaches for the task of predicting?
Evidence: We use three baseline approaches for the task of predicting whether a query is out-of-scope: (1) oos-train, where we train an additional (i.e. 151st) intent on out-of-scope training data; (2) oos-threshold, where we use a threshold on the classifier's probability estimate; and (3) oos-binary, a two-stage process where we first classify a query as in- or out-of-scope, then classify it into one of the 150 intents if classified as in-scope.
To reduce the severity of the class imbalance between in-scope versus out-of-scope query samples (i.e., 15,000 versus 250 queries for OOS+), we investigate two strategies when using oos-binary: one where we undersample the in-scope data and train using 1,000 in-scope queries sampled evenly across all intents (versus 250 out-of-scope), and another where we augment the 250 OOS+ out-of-scope training queries with 14,750 sentences sampled from Wikipedia.
From a development point of view, the oos-train and oos-binary methods both require careful curation of an out-of-scope training set, and this set can be tailored to individual systems. The oos-threshold method is a more general decision rule that can be applied to any model that produces a probability. In our evaluation, the out-of-scope threshold was chosen to be the value which yielded the highest validation score across all intents, treating out-of-scope as its own intent.
We use three baseline approaches for the task of predicting whether a query is out-of-scope: (1) oos-train, where we train an additional (i.e. 151st) intent on out-of-scope training data; (2) oosthreshold, where we use a threshold on the classifier’s probability estimate; and (3) oos-binary, a two-stage process where we first classify a query as in- or out-of-scope, then classify it into one of the 150 intents if classified as in-scope
Answer: The authors use three baseline approaches for the task of predicting whether a query is out-of-scope: (1) oos-train; (2) oosthreshold; and (3) oos-binary.
Question: How do the authors' models perform?
Evidence: In this paper, we proposed a method of stacking multiple LSTM layers for modeling sentences, dubbed CAS-LSTM. It uses not only hidden states but also cell states from the previous layer, for the purpose of controlling the vertical information flow in a more elaborate way. We evaluated the proposed method on various benchmark tasks: natural language inference, paraphrase identification, and sentiment classification. Our models achieve the new state-of-the-art accuracy on SNLI and Quora Question Pairs datasets and obtain comparable results on MultiNLI and SST datasets. The proposed architecture can replace any stacked LSTM under one weak restriction—the size of states should be identical across all layers.
For future work we plan to apply the CAS-LSTM architecture beyond sentence modeling tasks. Various problems e.g. sequence labeling, sequence generation, and language modeling might benefit from sophisticated modulation on context integration. Aggregating diverse contexts from sequential data, e.g. those from forward and backward reading of text, could also be an intriguing research direction.
Our models outperformed plain LSTM-based models in all experiments and were competitive other state-of-the-art models.
Answer: Their models outperformed plain LSTM-based models in all experiments and theyre competitive other state-of-the-art models.
Question: Categorize the companies based on their ownership status.
Meta, Microsoft, Amazon, Deloitte, KPMG, Ernst & Young
Answer: Private held : Deloitte, KPMG, Ernst & Young 
Public : Meta, Microsoft, Amazon
Question: We are going to the zoo and need you to tell us which animals belong in which section. The animals are alligator, bald eagle, elephant, hippopotamus, camel, frog, penguin, and zebra. The sections are amphibians, birds, mammals, and reptiles.
Answer: The alligator is a reptile. The Bald Eagle and Penguin are birds. Camel and elephant are mammals. The frog is an amphibian.
Question: Classify each of the following people as an actor or athlete: Adam Sandler, Harry Kane, Hugo Lloris, Mike Myers, Tyreek Hill, Ryan Gosling, Joel Embiid
Answer: Adam Sandler: Actor
Harry Kane: Athlete
Hugo Lloris: Athlete
Mike Myers: Actor
Tyreek Hill: Athlete
Ryan Gosling: Actor
Joel Embiid: Athlete
Question: Can we move away from fossil fuels in the next decade  ?
Answer: In short No, Fossil fuels will be part of our Energy portfolio for several decades. Despite the general consensus that fossil fuels are the largest contributor to climate change, it’s also challenging to find an alternative energy source that replaces this on a global scale. As emerging nations grow, their demand for affordable energy will maintain the need for fossil fuels.  However, while  renewable energies have been projected to replace fossil fuels as the main energy source by 2050, we will still need to invest in decarbonization technology to offset the impact of maintaining emissions from fossil fuels.
Question: Extract the list of countries that have part of the Amazon rainforest. Place them in a bullet list
Evidence: The Amazon rainforest, also called Amazon jungle or Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 km2 (2,700,000 sq mi), of which 5,500,000 km2 (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations and 3,344 formally acknowledged indigenous territories.

The majority of the forest, 60%, is in Brazil, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Bolivia, Ecuador, French Guiana, Guyana, Suriname, and Venezuela. Four nations have "Amazonas" as the name of one of their first-level administrative regions, and France uses the name "Guiana Amazonian Park" for French Guiana's protected rainforest area. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees in about 16,000 species.

More than 30 million people of 350 different ethnic groups live in the Amazon, which are subdivided into 9 different national political systems and 3,344 formally acknowledged indigenous territories. Indigenous peoples make up 9% of the total population, and 60 of the groups remain largely isolated.
Answer: Brazil
Peru
Colombia
Bolivia
Ecuador
French Guiana
Guyana
Suriname
Venezuela
Question: What is significant about Octavia Butler?
Evidence: Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author and a multiple recipient of the Hugo and Nebula awards. In 1995, Butler became the first science-fiction writer to receive a MacArthur Fellowship. Born in Pasadena, California, Butler was raised by her widowed mother. Extremely shy as a child, Butler found an outlet at the library reading fantasy, and in writing. She began writing science fiction as a teenager. She attended community college during the Black Power movement. While participating in a local writer's workshop, she was encouraged to attend the Clarion Workshop, then held in Pennsylvania, which focused on science fiction.
Answer: She was the first science fiction writer to receive a MacArthur Fellowship.
Question: Categorize where each of these household items belong: bed, couch, desk
Answer: A bed belongs in a bedroom, a couch belongs in a living area, a desk belongs in an office
Question: Categorize each of these as either liquids or solids.
Answer: - Orange juice: liquid
- Gatorade: liquid
- Eye drops: liquid
- Water: liquid
- Peanut butter: solid
- Iphone: solid
- Beer: liquid
- Guitar: solid
Question: What is the results of multimodal compared to unimodal models?
Evidence: Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.
FLOAT SELECTED: Table 1. Performance of the proposed models, the LSTM and random scores. The Inputs column indicate which inputs are available at training and testing time.
Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available.
FLOAT SELECTED: Table 1. Performance of the proposed models, the LSTM and random scores. The Inputs column indicate which inputs are available at training and testing time.
Answer: Unimodal LSTM vs Best Multimodal (FCM)
- F score: 0.703 vs 0.704
- AUC: 0.732 vs 0.734 
- Mean Accuracy: 68.3 vs 68.4
Question: When entirely replacing its absolute position encodings, whether translation quality on two machine translation tasks get improvements?
Evidence: Recent approaches to sequence to sequence learning typically leverage recurrence BIBREF0 , convolution BIBREF1 , BIBREF2 , attention BIBREF3 , or a combination of recurrence and attention BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 as basic building blocks. These approaches incorporate information about the sequential position of elements differently.
Recurrent neural networks (RNNs) typically compute a hidden state $h_t$ , as a function of their input at time $t$ and a previous hidden state $h_{t-1}$ , capturing relative and absolute positions along the time dimension directly through their sequential structure. Non-recurrent models do not necessarily consider input elements sequentially and may hence require explicitly encoding position information to be able to use sequence order.
One common approach is to use position encodings which are combined with input elements to expose position information to the model. These position encodings can be a deterministic function of position BIBREF8 , BIBREF3 or learned representations. Convolutional neural networks inherently capture relative positions within the kernel size of each convolution. They have been shown to still benefit from position encodings BIBREF1 , however.
For the Transformer, which employs neither convolution nor recurrence, incorporating explicit representations of position information is an especially important consideration since the model is otherwise entirely invariant to sequence ordering. Attention-based models have therefore used position encodings or biased attention weights based on distance BIBREF9 .
In this work we present an efficient way of incorporating relative position representations in the self-attention mechanism of the Transformer. Even when entirely replacing its absolute position encodings, we demonstrate significant improvements in translation quality on two machine translation tasks.
Our approach can be cast as a special case of extending the self-attention mechanism of the Transformer to considering arbitrary relations between any two elements of the input, a direction we plan to explore in future work on modeling labeled, directed graphs.
Even when entirely replacing its absolute position encodings, we demonstrate significant improvements in translation quality on two machine translation tasks.
Answer: Yes.
Question: I want to try shooting sports, Can you throw some light on this using the given text as a reference?
Evidence: Shooting sports is a group of competitive and recreational sporting activities involving proficiency tests of accuracy, precision and speed in shooting — the art of using ranged weapons, mainly small arms (firearms and airguns, in forms such as handguns, rifles and shotguns) and bows/crossbows.
Answer: 1. A group of competitive and leisure sports activities is referred to as shooting sports.
2. It involves shooting competency examinations that measure accuracy, precision, and speed. Shooting is the art of employing ranged weapons, mostly small arms (firearms and airguns, in forms such pistols, rifles, and shotguns), as well as bows and crossbows.
Question: Did the authors use a crowdsourcing platform?
Evidence: This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.
This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. 
Answer: Yes.
Question: Given this passage about the human heart, tell me about the major structural features of the human heart as well as the primary purpose of the organ.
Evidence: The heart is a muscular organ in most animals. This organ pumps blood through the blood vessels of the circulatory system. The pumped blood carries oxygen and nutrients to the body, while carrying metabolic waste such as carbon dioxide to the lungs. In humans, the heart is approximately the size of a closed fist and is located between the lungs, in the middle compartment of the chest, called mediastinum .

In humans, other mammals, and birds, the heart is divided into four chambers: upper left and right atria and lower left and right ventricles. Commonly the right atrium and ventricle are referred together as the right heart and their left counterparts as the left heart. Fish, in contrast, have two chambers, an atrium and a ventricle, while most reptiles have three chambers. In a healthy heart blood flows one way through the heart due to heart valves, which prevent backflow. The heart is enclosed in a protective sac, the pericardium, which also contains a small amount of fluid. The wall of the heart is made up of three layers: epicardium, myocardium, and endocardium.
Answer: The major structural features of the human heart are its four chambers for pumping blood. The heart is commonly viewed as having a left side and a right side, with each side comprising two chambers -- an atrium and a ventricle. The four total chambers make up the organ's primary structural features. Together, they are responsible for maintaining blood flow throughout the body via the circulatory system. The heart is also responsible for moving waste products through the bloodstream to the organs that process them.
Question: What ways did the authors provide to ameliorate the resource gap and the transcription bottleneck?
Evidence: Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask. Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks. Low-resource languages still lag behind.
Endangered languages pose an additional challenge. The process of documenting an endangered language typically includes the creation of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored in large online linguistics archives. This process is often hindered by the Transcription Bottleneck: the linguistic fieldworker and the language community may not have time to transcribe all of the recordings and may only transcribe segments that are linguistically salient for publication or culturally significant for the creation of community resources.
With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina. We hope to ameliorate the resource gap and the transcription bottleneck in two ways. First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation). In providing baselines and datasets splits, we hope to further facilitate research on low-resource NLP for this language through our data set. Research on low-resource speech recognition is particularly important in relieving the transcription bottleneck, while tackling the research challenges that speech synthesis and machine translation pose for such languages could lead to such systems being deployed to serve more under-represented communities.
First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation). 
Answer: The authors provide a larger data set and baselines for NLP tasks.
Question: What is the performance of the authors' models?
Evidence: Pre-trained models BIBREF0, BIBREF1 have received much of attention recently thanks to their impressive results in many down stream NLP tasks. Additionally, multilingual pre-trained models enable many NLP applications for other languages via zero-short cross-lingual transfer. Zero-shot cross-lingual transfer has shown promising results for rapidly building applications for low resource languages. BIBREF2 show the potential of multilingual-BERT BIBREF0 in zero-shot transfer for a large number of languages from different language families on five NLP tasks, namely, natural language inference, document classification, named entity recognition, part-of-speech tagging, and dependency parsing.
Although multilingual models are an important ingredient for enhancing language technology in many languages, recent research on improving pre-trained models puts much emphasis on English BIBREF3, BIBREF4, BIBREF5. The current state of affairs makes it difficult to translate advancements in pre-training from English to non-English languages. To our best knowledge, there are only three available multilingual pre-trained models to date: (1) the multilingual-BERT (mBERT) that supports 104 languages, (2) cross-lingual language model BIBREF6 that supports 100 languages, and (3) Language Agnostic SEntence Representations BIBREF7 that supports 93 languages. Among the three models, LASER is based on neural machine translation approach and strictly requires parallel data to train.
Do multilingual models always need to be trained from scratch? Can we transfer linguistic knowledge learned by English pre-trained models to other languages? In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM. We apply our approach to autoencoding language models with masked language model objective and show the advantage of the proposed approach in zero-shot transfer. Our main contributions in this work are:
We propose a fast adaptation method for obtaining a bilingual BERT$_{\textsc {base}}$ of English and a target language within a day using one Tesla V100 16GB GPU.
We evaluate our bilingual LMs for six languages on two zero-shot cross-lingual transfer tasks, namely natural language inference BIBREF9 and universal dependency parsing. We show that our models offer competitive performance or even better that mBERT.
We illustrate that our bilingual LMs can serve as an excellent feature extractor in supervised dependency parsing task.
We show that our models offer competitive performance or even better that mBERT.
Answer: They show that their models offer competitive performance or even better that mBERT.
Question: Share some information regarding the Red Telephone box from the given text
Evidence: From 1926 onwards, the fascias of the kiosks were emblazoned with a prominent crown, representing the British government. The red phone box is often seen as a British cultural icon throughout the world. In 2006, the K2 telephone box was voted one of Britain's top 10 design icons, which included the Mini, Supermarine Spitfire, London tube map, World Wide Web, Concorde and the AEC Routemaster bus. In 2009, the K2 was selected by the Royal Mail for their "British Design Classics" commemorative postage stamp issue.
Answer: 1.	The fascias of the kiosks began to bear a conspicuous crown, which stood in for the British government, in 1926.
2.	The red phone box is very often seen as a British cultural icon all over the world.
3.	In the year 2006, the K2 telephone box was voted one of Britain's top 10 design icons, which included the Mini, Supermarine Spitfire, London tube map, World Wide Web, Concorde and the AEC Routemaster bus.
4.	In the year 2009, the K2 was selected by the Royal Mail for their "British Design Classics" commemorative postage stamp issue.
Question: What are the fastest growing cities in the US in 2023?
Answer: 1) Fort Myers, FL
2) North Port, FL
3) Winter Haven, FL
4) New Braunfels, TX
5) Port St. Lucie, FL
6) Dayton Beach, FL
7) Edinburg, TX
8) Madera, CA
9) Palm Bay, FL
10) Roseville, CA
Question: What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter?
Evidence: We use the above findings for predicting depressive behavior. Our model exploits early fusion BIBREF32 technique in feature space and requires modeling each user INLINEFORM0 in INLINEFORM1 as vector concatenation of individual modality features. As opposed to computationally expensive late fusion scheme where each modality requires a separate supervised modeling, this model reduces the learning effort and shows promising results BIBREF75 . To develop a generalizable model that avoids overfitting, we perform feature selection using statistical tests and all relevant ensemble learning models. It adds randomness to the data by creating shuffled copies of all features (shadow feature), and then trains Random Forest classifier on the extended data. Iteratively, it checks whether the actual feature has a higher Z-score than its shadow feature (See Algorithm SECREF6 and Figure FIGREF45 ) BIBREF76 .
To develop a generalizable model that avoids overfitting, we perform feature selection using statistical tests and all relevant ensemble learning models. It adds randomness to the data by creating shuffled copies of all features (shadow feature), and then trains Random Forest classifier on the extended data.
Answer: The answers are shown as follows:
* Random Forest classifier