DataHammer/scimrc huggingface dataset:
Question: Give me a few different ways to reduce my household electricity bill.
Answer: There are many ways to reduce your electricity consumption. A few examples are adjusting your thermostat so your furnace and air conditioning unit run less frequently, turning off all lights in unoccupied rooms, and reducing the water temperature on your hot water tank.
Question: What training recipe did the authors use for the systems?
Evidence: Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish.
For both these systems, we used the nnet training recipe from Kaldi.
Answer: The nnet training recipe from Kaldi.
Question: Name some of the more renowned British Romantic poets.
Answer: Among the most famous of the British Romantic poets were William Wordsworth, Samuel Taylor Coleridge, John Keats, and Lord Byron.
Question: How much does the approach outperform the previous ones?
Evidence: Recent work on convolutional neural network architectures showed that they are competitive with recurrent architectures even on tasks where modeling long-range dependencies is critical, such as language modeling BIBREF0 , machine translation BIBREF1 , BIBREF2 and speech synthesis BIBREF3 . In end-to-end speech recognition however, recurrent architectures are still prevalent for acoustic and/or language modeling BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 .
There is a history of using convolutional networks in speech recognition, but only as part of an otherwise more traditional pipeline. They have been first introduced as TDNNs to predict phoneme classes BIBREF9 , and later to generate HMM posteriorgrams BIBREF10 . They have more recently been used in end-to-end frameworks, but only in combination with recurrent layers BIBREF6 , or n-gram language models BIBREF11 , or for phone recognition BIBREF12 , BIBREF13 . Nonetheless, convolutional architectures are prevalent when learning from the raw waveform BIBREF14 , BIBREF15 , BIBREF16 , BIBREF13 , BIBREF17 , because they naturally model the computation of standard features such as mel-filterbanks. Given the evidence that they are also suitable on long-range dependency tasks, we expect convolutional neural networks to be competitive at all levels of the speech recognition pipeline.
In this paper, we present a fully convolutional approach to end-to-end speech recognition. Building on recent advances in convolutional learnable front-ends for speech BIBREF13 , BIBREF17 , convolutional acoustic models BIBREF11 , and convolutional language models BIBREF0 , the paper has four main contributions:
In particular, we decrease by 2% (absolute) the Word Error Rate on the noisy test set of Librispeech compared to DeepSpeech 2 [7] and the best sequence-to-sequence model [9]. On clean speech, the improvement is about 0.5% on Librispeech compared to the best end-to-end systems; on WSJ, our results are competitive with the current state-of-the-art, a DNN-HMM system [19].In particular, the detailed results show that the convolutional language model yields systematic and consistent improvement over a 4-gram language model for its better perplexity and larger receptive field.
Answer: In particular, they decrease by 2% (absolute) the Word Error Rate on the noisy test set of Librispeech compared to DeepSpeech 2 [7] and the best sequence-to-sequence model [9]. On clean speech, the improvement is about 0.5% on Librispeech compared to the best end-to-end systems; on WSJ, their results are competitive with the current state-of-the-art, a DNN-HMM system [19].In particular, the detailed results show that the convolutional language model yields systematic and consistent improvement over a 4-gram language model for its better perplexity and larger receptive field.
Question: From this summary, list three ways in which artificial intelligence systems show behavior misaligned human values, covered by Brian Christian in the three sections of his book "The Alignment Problem"?
Evidence: The book is divided into three sections: Prophecy, Agency, and Normativity. Each section covers researchers and engineers working on different challenges in the alignment of artificial intelligence with human values.

Prophecy
In the first section, Christian interweaves discussions of the history of artificial intelligence research, particularly the machine learning approach of artificial neural networks such as the Perceptron and AlexNet, with examples of how AI systems can have unintended behavior. He tells the story of Julia Angwin, a journalist whose ProPublica investigation of the COMPAS algorithm, a tool for predicting recidivism among criminal defendants, led to widespread criticism of its accuracy and bias towards certain demographics. One of AI's main alignment challenges is its black box nature (inputs and outputs are identifiable but the transformation process in between is undetermined). The lack of transparency makes it difficult to know where the system is going right and where it is going wrong.

Agency
In the second section, Christian similarly interweaves the history of the psychological study of reward, such as behaviorism and dopamine, with the computer science of reinforcement learning, in which AI systems need to develop policy ("what to do") in the face of a value function ("what rewards or punishment to expect"). He calls the DeepMind AlphaGo and AlphaZero systems "perhaps the single most impressive achievement in automated curriculum design." He also highlights the importance of curiosity, in which reinforcement learners are intrinsically motivated to explore their environment, rather than exclusively seeking the external reward.

Normativity
The third section covers training AI through the imitation of human or machine behavior, as well as philosophical debates such as between possibilism and actualism that imply different ideal behavior for AI systems. Of particular importance is inverse reinforcement learning, a broad approach for machines to learn the objective function of a human or another agent. Christian discusses the normative challenges associated with effective altruism and existential risk, including the work of philosophers Toby Ord and William MacAskill who are trying to devise human and machine strategies for navigating the alignment problem as effectively as possible.
Answer: In the first section, Prophecy, the author talks about predictive models making decisions that are difficult to examine and may become discriminative or unfair.

In the second section, Agency, about reinforcement learning systems, despite some remarkable achievements, are guided nearly exclusively by their objective function, not so much by curiosity to discover the world.

In the third section, Normativity, inverse reinforcement learning techniques face the challenge of figuring out the objective function of humans.
Question: How much better are results of proposed model compared to pivoting method?
Evidence: First we compare our models with the conventional pivoting method, i.e., round-trip translation. As shown in Figure FIGREF15 (a)(b), either the bilingual or the multilingual model is better than the baseline in terms of relevance and diversity in most cases. In other words, with the same generation diversity (measured by both Distinct-2 and Self-BLEU), our models can generate paraphrase with more semantically similarity to the input sentence.
As shown in Table TABREF28, our method outperforms the baseline in both relevance and fluency significantly. We further calculate agreement (Cohen's kappa) between two annotators.
Both round-trip translation and our method performs well as to fluency. But the huge gap of relevance between the two systems draw much attention of us. We investigate the test set in details and find that round-trip approach indeed generate more noise as shown in case studies.
FLOAT SELECTED: Table 3: Human evaluation results.
First we compare our models with the conventional pivoting method, i.e., round-trip translation. As shown in Figure FIGREF15 (a)(b), either the bilingual or the multilingual model is better than the baseline in terms of relevance and diversity in most cases. In other words, with the same generation diversity (measured by both Distinct-2 and Self-BLEU), our models can generate paraphrase with more semantically similarity to the input sentence.
As shown in Table TABREF28, our method outperforms the baseline in both relevance and fluency significantly. We further calculate agreement (Cohen's kappa) between two annotators.

Both round-trip translation and our method performs well as to fluency. But the huge gap of relevance between the two systems draw much attention of us. We investigate the test set in details and find that round-trip approach indeed generate more noise as shown in case studies.
FLOAT SELECTED: Table 3: Human evaluation results.
Answer: The answers are shown as follows:
* our method outperforms the baseline in both relevance and fluency significantly.
Question: What are the causes of population growth in the Sun Belt in the US?
Evidence: The Sun Belt has seen substantial population growth since post-World War II from an influx of people seeking a warm and sunny climate, a surge in retiring baby boomers, and growing economic opportunities. The advent of air conditioning created more comfortable summer conditions and allowed more manufacturing and industry to locate in the Sun Belt. Since much of the construction in the Sun Belt is new or recent, housing styles and design are often modern and open. Recreational opportunities in the Sun Belt are often not tied strictly to one season, and many tourist and resort cities, such as Fort Lauderdale, Gulf Shores, Houston, Las Vegas, Los Angeles, Miami, Myrtle Beach, New Orleans, Orlando, Palm Springs, Phoenix, San Antonio, San Diego, Tampa, and Tucson support a tourist industry all year.
The traditional explanations for the growth are increasing productivity in the South and West and increasing demand for Sunbelt amenities, especially its pleasant weather. Job decline in the Rust Belt is another major reason for migration.
Answer: Since WWII, many people have migrated from the US Midwest or Northeast to the Sun Belt. One significant factor is the milder climate and the recreational possibilities it makes possible. Economic opportunity is another factor, as many automobile and other manufacturers have left the Midwest and Northeast and built factories in the Sun Belt. The housing stock is newer and affordable, which attracts people of all ages.
Question: Whats are the 5 best places to visit in late spring months like May?
Answer: Late spring is the time where the weather opens up and the temperature warms but the schools are still in session, so its one of the best times to visit places that get specially hot or crowded during summer. 
   Here is a list of some of the best places for late spring travels in the continental United States
1. Skagit Valley and Tulip Festival ( Washington)  
2. Antelope Canyon (Arizona)
3. Zion National Park, Utah
4. Honolulu, Hawaii
5. Arches National Park, Utah
Question: Following prior work (Janner et al., 2019), we learn an additional model to predict the true environment. What does it mean by "additional model"?
Evidence: Figure 8: Alternative Model Learning Objectives: Using the DClawScrewFixed-v0 task, we compare MnM and MBPO (Janner et al., 2019) to two additional model learning objectives suggested in the literature, VAML (Farahmand et al., 2017) and value-weighted maximum likelihood (Lambert et al., 2020). MnM (our method) outperforms these alternative approaches.
Answer: We mean that we train another neural network (i.e., different from the dynamics model and the classifier) that takes as input the state and action and outputs the predicted reward. We have revised this sentence to clarify this point.
Question: What is the research field of the paper?
Evidence: Sarcasm, a creative device used to communicate an intended meaning that is actually the opposite of its literal meaning, is notoriously difficult to convey and interpret through text, in part because doing so relies heavily upon shared contextual understandings that can be marked more easily by altered prosody (e.g., emphasis upon certain words) or non-verbal signals (e.g., rolling one's eyes). It is a complex process even for humans, and in fact an inability to detect sarcasm has been linked with a number of neurocognitive disorders, including dementia BIBREF0 . It is similarly a challenging open task in natural language processing, and has direct implications to a number of other critical application areas, such as sentiment analysis.
Most research on automatic sarcasm detection to date has focused on the Twitter domain, which boasts an ample source of publicly-available data, some of which is already self-labeled by users for the presence of sarcasm (e.g., with #sarcasm). However, Twitter is highly informal, space-restricted, and subject to frequent topic fluctuations from one post to the next due to the ebb and flow of current events—in short, it is not broadly representative of most text domains. Thus, sarcasm detectors trained using features designed for maximum Twitter performance are not necessarily transferable to other domains. Despite this, it is desirable to develop approaches that can harness the more generalizable information present in the abundance of Twitter data.
In this work, we develop a set of domain-independent features for sarcasm detection and show that the features generally perform well across text domains. Further, we validate that domain adaptation can be applied to sarcasm detection to leverage patterns in out-of-domain training data, even when results from training only on that source domain data are extremely bad (far below baseline results), to improve over training on only the target data or over training on the simply combined dataset. Finally, we make a new dataset of sarcastic and non-sarcastic tweets available online as a resource to other researchers.
In this work, we develop a set of domain-independent features for sarcasm detection and show that the features generally perform well across text domains.
Answer: Sarcasm detection.
Question: What is a modem?
Answer: A modem was a device that let computer connect to the internet over a dial-up connection.  This connection is established and hosted through an ISP or Internet Service Provider.  The three most common types of modem connection are dial-up, DSL, or cable.  DSL was a lot faster than dial-up, and cable modem connection was the fastest.
Question: Why do home power outages occur?
Answer: Power outages can occur for a number of reasons. First, some perceived "outages" may actually be caused by overloading a circuit breaker in a home. To eliminate this as a possibility and confirm the issue is not localized to your home, check to make sure that all circuits in your electric panel are still on. 

If you are experiencing a power outage, it may been caused by a few different reasons:
- Power safety shutoff: in some situations, a power provider will de-electrify an area to prevent issues. This might be due to maintenance activities with employees working on a section of the power grid, or to prevent issues in inclimate weather, such as preventing wildfire ignition in high-wind events. 
- Weather-caused issues: high wind or ice-storms can sometimes snap power lines or cause tree limbs to fall. 

Check with your utility provider to stay up to date on any outage you are experiencing. Most utilities provide SMS/text updates, which will keep you updated in case you lose access to the internet (as your home router requires power too!)
Question: What kind of method do they present in the paper?
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.
In this work we present an efficient way of incorporating relative position representations in the self-attention mechanism of the Transformer.
Answer: An efficient way of incorporating relative position representations in the self-attention mechanism of the Transformer.
Question: From the passage list down the materials used to prepare boomerang. List the results in comma separated format.
Evidence: A boomerang is a throwing stick with aerodynamic properties, traditionally made of wood, but also of bone, horn, tusks and even iron. Modern boomerangs used for sport may be made from plywood or plastics such as ABS, polypropylene, phenolic paper, or carbon fibre-reinforced plastics.

Boomerangs come in many shapes and sizes depending on their geographic or tribal origins and intended function. Many people think only of the traditional Australian type, although today there are many types of more easily usable boomerangs, such as the cross-stick, the pinwheel, the tumble-stick, the Boomabird, and many other less common types.

An important distinction should be made between returning boomerangs and non-returning boomerangs:

Returning boomerangs fly, and are examples of the earliest heavier-than-air human-made flight. A returning boomerang has two or more aerofoil section wings arranged so that when spinning they create unbalanced aerodynamic forces that curve its path into an ellipse, returning to its point of origin when thrown correctly. Their typical L-shape makes them the most recognisable form of boomerang. Although used primarily for leisure or recreation, returning boomerangs are also used to decoy birds of prey, thrown above the long grass to frighten game birds into flight and into waiting nets. Non-traditional, modern, competition boomerangs come in many shapes, sizes and materials.
Non-returning boomerangs, throwing sticks, valari, or kylies, are primarily used as weapons. They lack the aerofoil sections, are generally heavier and designed to travel as straight and forcefully as possible to the target to bring down game. The Tamil valari variant, of ancient origin and mentioned in the Tamil Sangam literature "Purananuru", was one of these. The usual form of the Valari is two limbs set at an angle; one thin and tapering, the other rounded as a handle. Although valaris come in many shapes and sizes they are usually made of cast iron cast from moulds. However, some may have wooden limbs tipped with iron or with lethally sharpened edges or with special double-edged and razor-sharp daggers known as kattari.
Answer: wood, bone, horn, tusks, iron, plywood, plastics such as ABS, polypropylene, phenolic paper, carbon fibre-reinforced plastics
Question: How did Washington become a state?
Evidence: Washington, officially the State of Washington and often referred to as Washington state to distinguish it from the USA's capital, Washington, D.C., is a state in the Pacific Northwest region of the Western United States. Named for George Washington—the first U.S. president—the state was formed from the western part of the Washington Territory, which was ceded by the British Empire in 1846, by the Oregon Treaty in the settlement of the Oregon boundary dispute. The state is bordered on the west by the Pacific Ocean, Oregon to the south, Idaho to the east, and the Canadian province of British Columbia to the north. It was admitted to the Union as the 42nd state in 1889. Olympia is the state capital; the state's largest city is Seattle.
Answer: Washington became the 42nd state of the United States of America in 1889. The United States Congress passed an act enabling the territories of Washington, North Dakota, South Dakota, and Montana to seek statehood.
Question: Did José Altuve cheat in 2017?
Evidence: In 2019, Altuve's role in the 2017 World Series gained nationwide attention in the Houston Astros sign stealing scandal. With regard to the scandal, Altuve said, "I'm not going to say to you that it was good — it was wrong. We feel bad, we feel remorse, like I said, the impact on the fans, the impact on the game — we feel bad." According to the website signstealingscandal.com, Altuve's instances of hearing the trash can banging were significantly lower than those of any other everyday player. Peter Gammons noted in 2022 that when he talked to Altuve in 2020 about how players, coaches, and members in the organization believed that Altuve did not participate in the stealing, Altuve declined to talk about it. He stated that it would be a "betrayal of my teammates" to discuss the matter and asked that Gammons not write about it. Despite this, many Astros players have defended Altuve in particular of any wrongdoing. Carlos Correa, in an interview with Ken Rosenthal, stated of Altuve's role in the scandal: "The few times that the trash can was banged was without his consent, and he would go inside the clubhouse and inside the dugout to whoever was banging the trash can and he would get pissed. He would get mad. He would say, 'I don't want this. I can't hit like this. Don't you do that to me.' He played the game clean"; Correa's comments came after Dodgers outfielder Cody Bellinger accused Altuve of stealing the MVP Award from runner-up Aaron Judge.
Answer: José Altuve played for the 2017 Astros, who are known to have used an illegal sign stealing system, but Altuve himself did not participate in the system and did not cheat.
Question: What is accepted to be California's first official wine-producing vineyard?
Answer: Mission San Juan Capistrano is recognized as the location of California's first wine vineyard,  which was planted by Father Junípero Serra.