DataHammer/scimrc huggingface dataset:
Question: What is the best way to make a camp fire
Answer: Nothing screams camping out in the wilderness like building a campfire.   The memories, conversations, scents, roasted marshmallows, and crackling of the wood are all part of the experience.   In order to build a strong campfire, you need to perform the following steps.
Step 1.  Clear a perimeter that is roughly 12 feet in diameter.   Make sure you are in an open area, where tree limbs are not hanging over the campfire to avoid secondary fires.   Before you start the fire, get all of the necessary components such as dry kindle/twigs, larger sticks, and larger logs, paper, and lighter
Step 2.   Build a small tepee shape with the dry kindle and dry small logs.    I would recommend adding paper with the kindle to assist in starting the fire.
Step 3.  Stack larger sticks around the teepee structure in a square formation.   Build up multiple levels, so the square structue is higher than the teepee.   This will help the fire expand and spread to the surrounding structure.
Step 4.  Light the paper in the teepee with a match or lighter, and watch the  dry kindle  start to catch.    At this early stage, you will most likely see smoke.   You may need to blow on the teepee structure lightly in order to add some oxygen to the fire and help it catch to the larger sticks square structure.
Step 5.  Once the square structure begins to light, start to add larger logs diagonally across the structure.   The diagonal formation will fuel the fire, and expand upwards .
Step 6.  Keep adding larger logs to maintain the fire.   With the prior kindle, sticks, and logs already hot, adding the new larger logs will burn quickly and self ignite.
Step 7.   When you are done enjoying the fire, have a couple of buckets of water nearby to extinguish it.   Do not leave the campfire unattended as the hot coals and wind could cause a unintended spread.
Question: What is Chichen Itza?
Evidence: Chichén Itzá (often spelled Chichen Itza in English and traditional Yucatec Maya) was a large pre-Columbian city built by the Maya people of the Terminal Classic period. The archeological site is located in Tinúm Municipality, Yucatán State, Mexico.

Chichén Itzá was a major focal point in the Northern Maya Lowlands from the Late Classic (c. AD 600–900) through the Terminal Classic (c. AD 800–900) and into the early portion of the Postclassic period (c. AD 900–1200). The site exhibits a multitude of architectural styles, reminiscent of styles seen in central Mexico and of the Puuc and Chenes styles of the Northern Maya lowlands. The presence of central Mexican styles was once thought to have been representative of direct migration or even conquest from central Mexico, but most contemporary interpretations view the presence of these non-Maya styles more as the result of cultural diffusion.

Chichén Itzá was one of the largest Maya cities and it was likely to have been one of the mythical great cities, or Tollans, referred to in later Mesoamerican literature. The city may have had the most diverse population in the Maya world, a factor that could have contributed to the variety of architectural styles at the site.

The ruins of Chichén Itzá are federal property, and the site's stewardship is maintained by Mexico's Instituto Nacional de Antropología e Historia (National Institute of Anthropology and History). The land under the monuments had been privately owned until 29 March 2010, when it was purchased by the state of Yucatán.

Chichén Itzá is one of the most visited archeological sites in Mexico with over 2.6 million tourists in 2017.
Answer: Chichén Itzá was one of the largest Maya cities which was a major focal point around AD 900. It is now one of the most visited archeological sites in Mexico.
Question: How many variants of MPAD are there？
Evidence: Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words $\rightarrow $ bigrams $\rightarrow $ compositions of bigrams, etc.). However, it might be beneficial to explicitly capture document hierarchy. Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation BIBREF24, BIBREF25, sentiment and topic classification BIBREF26, BIBREF27, and spoken language understanding BIBREF28, BIBREF29. Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows. In all of them, we represent each sentence in the document as a word co-occurrence network, and obtain an embedding for it by applying MPAD as previously described.
MPAD-sentence-att. Here, the sentence embeddings are simply combined through self-attention.
MPAD-clique. In this variant, we build a complete graph where each node represents a sentence. We then feed that graph to MPAD, where the feature vectors of the nodes are initialized with the sentence embeddings previously obtained.
MPAD-path. This variant is similar to the clique one, except that instead of a complete graph, we build a path according to the natural flow of the text. That is, two nodes are linked by a directed edge if the two sentences they represent follow each other in the document.
MPAD-sentence-att. Here, the sentence embeddings are simply combined through self-attention. MPAD-clique. In this variant, we build a complete graph where each node represents a sentence. We then feed that graph to MPAD, where the feature vectors of the nodes are initialized with the sentence embeddings previously obtained. MPAD-path. This variant, shown in Fig. 1, is similar to the clique one, except that instead of a complete graph, we build a path according to the natural flow of the text.
Answer: Three. MPAD-sentence-att, MPAD-clique and MPAD-path.
Question: What are the potential source of energy for an engine?
Evidence: An engine or motor is a machine designed to convert one or more forms of energy into mechanical energy.
Available energy sources include potential energy (e.g. energy of the Earth's gravitational field as exploited in hydroelectric power generation), heat energy (e.g. geothermal), chemical energy, electric potential and nuclear energy (from nuclear fission or nuclear fusion). Many of these processes generate heat as an intermediate energy form, so heat engines have special importance. Some natural processes, such as atmospheric convection cells convert environmental heat into motion (e.g. in the form of rising air currents). Mechanical energy is of particular importance in transportation, but also plays a role in many industrial processes such as cutting, grinding, crushing, and mixing.
Answer: Potential sources of an engine includes potential energy, heat energy chemical energy, electrical energy and nuclear energy.
Question: What is a circle?
Answer: A circle is a geometric shape. It is primarily defined by a center point, and a circumference around that point at a fixed radius. What differs a circle from other shapes is that it does not have any edges.
Question: Do they single out a validation set from the fixed SRE training set?
Evidence: Since the introduction of i-vectors in BIBREF0 , the speaker recognition community has seen a significant increase in recognition performance. i-Vectors are low-dimensional representations of Baum-Welch statistics obtained with respect to a GMM, referred to as universal background model (UBM), in a single subspace which includes all characteristics of speaker and inter-session variability, named total variability matrix BIBREF0 . We trained on each acoustic feature a full covariance, gender-independent UBM model with 2048 Gaussians followed by a 600-dimensional i-vector extractor to establish our MFCC- and PLP-based i-vector systems. The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor. The open-source Kaldi software has been used for all these processing steps BIBREF12 .
 The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor.
Answer: No.
Question: What are current state-of-the-art methods that consider the two tasks independently?
Evidence: In this section, we compare our model with state-of-the-art systems, including those with different degrees of supervision. The baselines include: (1) Procrustes BIBREF11, which learns a linear mapping through Procrustes Analysis BIBREF36. (2) GPA BIBREF37, an extension of Procrustes Analysis. (3) GeoMM BIBREF38, a geometric approach which learn a Mahalanobis metric to refine the notion of similarity. (4) GeoMM$_{semi}$, iterative GeoMM with weak supervision. (5) Adv-C-Procrustes BIBREF11, which refines the mapping learned by Adv-C with iterative Procrustes, which learns the new mapping matrix by constructing a bilingual lexicon iteratively. (6) Unsup-SL BIBREF13, which integrates a weak unsupervised mapping with a robust self-learning. (7) Sinkhorn-BT BIBREF28, which combines sinkhorn distance BIBREF29 and back-translation. For fair comparison, we integrate our model with two iterative refinement methods (Procrustes and GeoMM$_{semi}$).
In this section, we compare our model with state-of-the-art systems, including those with different degrees of supervision. The baselines include: (1) Procrustes BIBREF11, which learns a linear mapping through Procrustes Analysis BIBREF36. (2) GPA BIBREF37, an extension of Procrustes Analysis. (3) GeoMM BIBREF38, a geometric approach which learn a Mahalanobis metric to refine the notion of similarity. (4) GeoMM$_{semi}$, iterative GeoMM with weak supervision. (5) Adv-C-Procrustes BIBREF11, which refines the mapping learned by Adv-C with iterative Procrustes, which learns the new mapping matrix by constructing a bilingual lexicon iteratively. (6) Unsup-SL BIBREF13, which integrates a weak unsupervised mapping with a robust self-learning. (7) Sinkhorn-BT BIBREF28, which combines sinkhorn distance BIBREF29 and back-translation.
Answer: The answers are shown as follows:
* Procrustes
* GPA
* GeoMM
* GeoMM$_{semi}$
* Adv-C-Procrustes
* Unsup-SL
* Sinkhorn-BT
Question: Of the living animal species which have been described, which group has the most?
Evidence: Animals are multicellular, eukaryotic organisms in the biological kingdom Animalia. With few exceptions, animals consume organic material, breathe oxygen, are able to move, can reproduce sexually, and grow from a hollow sphere of cells, the blastula, during embryonic development. As of 2022, 2.16 million living animal species have been described—of which around 1.05 million are insects, over 36,000 are fishes, around 11,700 are reptiles, over 11,100 are birds, and 6,596 mammals—but it has been estimated there are around 7.77 million animal species in total. Animals range in length from 8.5 micrometres (0.00033 in) to 33.6 metres (110 ft). They have complex interactions with each other and their environments, forming intricate food webs. The scientific study of animals is known as zoology.
Answer: There are 1.05 million (1,050,000) insect species, which is the largest group in the animal kingdom.
Question: Give me a list of some of the core beliefs in Stoicism
Answer: 1. It is important to live according to Nature for a smooth flowing life.
2. Happiness is found in Virtue, our values and choices.
3. We can only control our internal world, i.e. thoughts, opinions, and decisions, but have no control over external events.
4. We are responsible for our own life and hence should not complain or blame anyone else.
5. For our personal development, we need to act in society and try to live in harmony and cooperation with others.
6. Progress is driven by having a pragmatic attitude, not via perfection.
Question: What genres of music were popular in the late 1990s?
Answer: In the late 90s, there were a few dominant genres of popular music: boy bands and the so-called "Latin Explosion." Boy bands usually consisted of 5 members, each with a distinct "look" that's desirable to a range of (usually) teenage girl fans. Their songs were usually "bubblegum pop" or love songs, all with catchy hooks or choruses. Popular groups in the late 90s were the Backstreet Boys, *NSync, 98 Degrees, and O-Town. "Latin Explosion" artists were Hispanic/Latino or of Hispanic/Latino descent and the songs sometimes featured Spanish words or phrases, along with Latino beats in otherwise standard pop music arrangements. Some popular acts include Ricky Martin, Jennifer Lopez, Marc Anthony, and Enrique Igelsias.
Question: tell me whether these are European or American cities: New York, Milan, Paris, Chicago, San Francisco, London
Answer: European: Milan, Paris, London
American: New York, Chicago, San Francisco
Question: Who is Tina Turner
Evidence: Tina Turner (born Anna Mae Bullock; November 26, 1939) is an American-born and naturalized Swiss retired singer, dancer and actress. Widely referred to as the "Queen of Rock 'n' Roll", she rose to prominence as the lead singer of the Ike & Tina Turner Revue before launching a successful career as a solo performer.

Turner began her career with Ike Turner's Kings of Rhythm in 1957. Under the name Little Ann, she appeared on her first record, "Boxtop", in 1958. In 1960, she debuted as Tina Turner with the hit duet single "A Fool in Love". The duo Ike & Tina Turner became "one of the most formidable live acts in history". They released hits such as "It's Gonna Work Out Fine", "River Deep – Mountain High", "Proud Mary", and "Nutbush City Limits" before disbanding in 1976.
Answer: Tina Turner was born as Anna Mae Bullock on November 26, 1939 and is an American-born and naturalized Swiss retired singer, dancer and actress. Widely referred to as the Queen of Rock 'n' Roll, she rose to prominence as the lead singer of the Ike & Tina Turner Revue before launching a successful career as a solo performer.
Question: What is future work planned for improvement?
Evidence: We propose a united representation for knowledge graph, utilizing both structure and text description information of the entities. Experiments show that our proposed jointly representation learning with gating mechanism is effective, which benefits to modeling the meaning of an entity.
In the future, we will consider the following research directions to improve our model:
In the future, we will consider the following research directions to improve our model: 1.Currently, our score function is based on TransE since the main focus of this work is how to integrate both structural and textual information. We believe our models can be further improved with the recently proposed knowledge graph embedding models. 2.We will try to design dynamical gating strategy,which is estimated according to the context information. 3.Intuitively, images of relations and entities may further improve the representation.
Answer: In the future, they will consider the following research directions to improve their model: 1.Currently, their score function is based on TransE since the main focus of this work is how to integrate both structural and textual information. They believe their models can be further improved with the recently proposed knowledge graph embedding models. 2.They will try to design dynamical gating strategy,which is estimated according to the context information. 3.Intuitively, images of relations and entities may further improve the representation.
Question: What training data did they use?
Evidence: Each drug label is a collection of sections (e.g., DOSAGE & ADMINISTRATION, CONTRAINDICATIONS, and WARNINGS) where each section contains one or more sentences. Each sentence is annotated with a list of zero or more mentions and interactions. The training data released for this task contains 22 drug labels, referred to as Training-22, with gold standard annotations. Two test sets of 57 and 66 drug labels, referred to as Test Set 1 and 2 respectively, with gold standard annotations are used to evaluate participating systems. As Training-22 is a relatively small dataset, we additionally utilize an external dataset with 180 annotated drug labels dubbed NLM-180 BIBREF5 (more later). We provide summary statistics about these datasets in Table TABREF3 . Test Set 1 closely resembles Training-22 with respect to the sections that are annotated. However, Test Set 1 is more sparse in the sense that there are more sentences per drug label (144 vs. 27), with a smaller proportion of those sentences having gold annotations (23% vs. 51%). Test Set 2 is unique in that it contains annotations from only two sections, namely DRUG INTERACTIONS and CLINICAL PHARMACOLOGY, the latter of which is not represented in Training-22 (nor Test Set 1). Lastly, Training-22, Test Set 1, and Test Set 2 all vary with respect to the distribution of interaction types, with Training-22, Test Set 1, and Test Set 2 containing a higher proportion of PD, UN, and PK interactions respectively.
The training data released for this task contains 22 drug labels, referred to as Training-22, with gold standard annotations. 
As Training-22 is a relatively small dataset, we additionally utilize an external dataset with 180 annotated drug labels dubbed NLM-180 BIBREF5 (more later). 
Answer: The answers are shown as follows:
* Training-22
* NLM-180
Question: Do the sparse models have higher scores on  METEOR?
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
We can see that on average, the sparse models (csparsemax as well as sparsemax combined with coverage models) have higher scores on both BLEU and METEOR.
Answer: Yes.
Question: Which of the following are studio albums created by J. Cole: KOD, The Off-Season, Illmatic, Reasonable Doubt, The Eminem Show, Born Sinner
Answer: KOD, The Off-Season, Born Sinner
Question: What's the significance of the lighthouse in Virginia Woolf's "In The Lighthouse?"
Answer: The Lighthouse symbolizes human desire, a force that pulsates over the indifferent sea of the natural world and guides people’s passage across it. Yet even as the Lighthouse stands constant night and day, season after season, it remains curiously unattainable. James’ frustrated desire to visit the Lighthouse begins the novel, and Mrs. Ramsay looks at the Lighthouse as she denies Mr. Ramsay the profession of love he wants so badly at the end of Chapter 1. James, finally reaching the Lighthouse in Chapter 3 a decade after he’d first wanted to go, sees that, up close, the Lighthouse looks nothing like it does from across the bay. That misty image he’d desired from a distance remains unattainable even when he can sail right up to the structure it’s supposedly attached to. The novel’s title can be understood as a description for experience itself: one moves through life propelled by desire towards the things one wants, and yet seems rarely to reach them. One’s life, then, is the process of moving towards, of reaching, of desiring. It is “to” the Lighthouse, not “at” it
Question: Who is the best soccer player ?
Answer: This is a question that is often debated during dinners. To remain as factual as possible, it would certainly be appropriate to mention the name of the current Golden Ball: Karim Benzema.
Question: Do they look for inconsistencies between different UD treebanks?
Evidence: The contributions of this work are:
The contributions of this work are:
Answer: Yes.
Question: Give me a bulleted list of the best chain coffee shops in the United States
Answer: - Starbucks
- Dunkin Donuts
- Peet's
- Aroma Joe's
- Panera Bread
Question: How are their changes evaluated?
Evidence: To evaluate the performance of our approach, we used a subset of the SNIPS BIBREF12 dataset, which is readily available in RASA nlu format. Our training data consisted of 700 utterances, across 7 different intents (AddToPlaylist, BookRestaurant, GetWeather, PlayMusic, RateBook, SearchCreativeWork, and SearchScreeningEvent). In order to test our implementation of incremental components, we initially benchmarked their non-incremental counterparts, and used that as a baseline for the incremental versions (to treat the sium component as non-incremental, we simply applied all words in each utterance to it and obtained the distribution over intents after each full utterance had been processed).
We use accuracy of intent and entity recognition as our task and metric. To evaluate the components worked as intended, we then used the IncrementalInterpreter to parse the messages as individual ius. To ensure REVOKE worked as intended, we injected random incorrect words at a rate of 40%, followed by subsequent revokes, ensuring that an ADD followed by a revoke resulted in the same output as if the incorrect word had never been added. While we implemented both an update-incremental and a restart-incremental RASA nlu component, the results of the two cannot be directly compared for accuracy as the underlying models differ greatly (i.e., sium is generative, whereas Tensorflow Embedding is a discriminative neural network; moreover, sium was designed to work as a reference resolution component to physical objects, not abstract intents), nor are these results conducive to an argument of update- vs. restart-incremental approaches, as the underlying architecture of the models vary greatly.
To evaluate the performance of our approach, we used a subset of the SNIPS BIBREF12 dataset, which is readily available in RASA nlu format. 
We use accuracy of intent and entity recognition as our task and metric. 
Answer: The changes are evaluated based on accuracy of intent and entity recognition on SNIPS dataset
Question: What is Bayes theorem
Evidence: In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately by conditioning it relative to their age, rather than simply assuming that the individual is typical of the population as a whole.

One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics, being considered by one authority as; "to the theory of probability what Pythagoras's theorem is to geometry."
Answer: Bayes theorem is based on conditional probability related to prior knowledge of the event.