DataHammer/scimrc huggingface dataset:
Question: Summarize the important contributions of Thomas Edison from this paragraph in a bulleted list.
Evidence: Thomas Alva Edison (February 11, 1847 – October 18, 1931) was an American inventor and businessman. He developed many devices in fields such as electric power generation, mass communication, sound recording, and motion pictures.These inventions, which include the phonograph, the motion picture camera, and early versions of the electric light bulb, have had a widespread impact on the modern industrialized world. He was one of the first inventors to apply the principles of organized science and teamwork to the process of invention, working with many researchers and employees. He established the first industrial research laboratory.
Answer: Thomas Edison's research and inventions have had a significant influence over the modern industrialized world. Here is a glimpse of his achievements:
1. Invented the phonograph
2. Invented the motion picture camera
3. Invented early versions of electric light bulbs
4. He established the first industrial research labratory
5. He developed devices across several fields of research, including: power generation, mass communication, sound recording, and motion pictures
Question: What are some of the most popular womens sports in North America
Answer: Popular sports include soccer, ice hockey, tennis & basketball. There is a wide variety of sports and some have more developed professional leagues while others are more mature NCAA sports or Olympic driven
Question: Given this paragraph about WWII, how many fatalities happened?
Evidence: World War II or the Second World War, often abbreviated as WWII or WW2, was a global conflict that lasted from 1939 to 1945. The vast majority of the world's countries, including all of the great powers, fought as part of two opposing military alliances: the Allies and the Axis. Many participants threw their economic, industrial, and scientific capabilities behind this total war, blurring the distinction between civilian and military resources. Aircraft played a major role, enabling the strategic bombing of population centres and the delivery of the only two nuclear weapons ever used in war.

World War II was by far the deadliest conflict in history; it resulted in an estimated 70 to 85 million fatalities, mostly among civilians. Tens of millions died due to genocides (including the Holocaust), starvation, massacres, and disease. In the wake of the Axis defeat, Germany and Japan were occupied, and war crimes tribunals were conducted against German and Japanese leaders.

The causes of World War II are debated, but contributing factors included the Second Italo-Ethiopian War, Spanish Civil War, Second Sino-Japanese War, Soviet–Japanese border conflicts, the rise of fascism in Europe, and European tensions in the aftermath of World War I. World War II is generally considered to have begun on 1 September 1939, when Nazi Germany, under Adolf Hitler, invaded Poland. The United Kingdom and France subsequently declared war on Germany on 3 September. Under the Molotov–Ribbentrop Pact of August 1939, Germany and the Soviet Union had partitioned Poland and marked out their "spheres of influence" across Finland, Estonia, Latvia, Lithuania and Romania. From late 1939 to early 1941, in a series of campaigns and treaties, Germany conquered or controlled much of continental Europe, in a military alliance with Italy, Japan and other countries called the Axis. Following the onset of campaigns in North Africa and East Africa, and the fall of France in mid-1940, the war continued primarily between the European Axis powers and the British Empire, with war in the Balkans, the aerial Battle of Britain, the Blitz of the United Kingdom, and the Battle of the Atlantic. On 22 June 1941, Germany led the European Axis powers in an invasion of the Soviet Union, opening the Eastern Front, the largest land theatre of war in history.
Answer: According to the paragraph, World War II resulted in an estimated 70 to 85 million fatalities. Most of these fatalities were amongst civilians.
Question: Classify these technologies as frontend or backend oriented: React, Kafka, MySQL, JavaScript, gRPC, SQL, Prometheus, Android, Swift, Redis, JDBC
Answer: React, Android, Swift and JavaScript are frontend oriented, while Kafka, MySQL, gRPC, SQL, Prometheus, Redis and JDBC are backend oriented.
Question: Whether these two approaches can lead to a significant gain on the four downstream tasks the authors considered relative to ELMo-only baselines?
Evidence: Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.
Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines.
Answer: No.
Question: Which classical musician went deaf in the prime of their career?
Answer: Beethoven went deaf in the height of his career
Question: Which dataset do they train their models on?
Evidence: Character-level language modeling (or character prediction) consists in predicting the next character while reading a document one character at a time. It is a common benchmark for rnn because of the heightened need for shared parametrization when compared to word-level models. We test mgru on two well-known datasets, the Penn Treebank and Text8.
We test mgru on two well-known datasets, the Penn Treebank and Text8.
Answer: The answers are shown as follows:
* Penn Treebank
* Text8
Question: Is their model more effective than existing methods in integrating Flickr tags with structured information?
Evidence: In this paper, we have proposed a model to learn geographic location embeddings using Flickr tags, numerical environmental features, and categorical information. The experimental results show that our model can integrate Flickr tags with structured information in a more effective way than existing methods, leading to substantial improvements over baseline methods on various prediction tasks about the natural environment.
In this paper, we have proposed a model to learn geographic location embeddings using Flickr tags, numerical environmental features, and categorical information. The experimental results show that our model can integrate Flickr tags with structured information in a more effective way than existing methods, leading to substantial improvements over baseline methods on various prediction tasks about the natural environment.
Answer: Yes.
Question: How to make a bald guy look great?
Answer: There are many options, more importantly you be yourself and you look great.
Question: What are some top ski resorts to visit if I live in SF bay area?
Answer: Here are some most popular ski resorts you can consider: Palisades Tahoe is the closest to SF bay area. You can drive there in about 5-6 hours if the road condition is okay. There is also Mammoth Mountain ski resort in California, which is further away but worth visiting. You can also consider mountains in Utah or Colorado, which are a quick flight away. Some of them include Aspen Snowmass in Colorado,  Vail in Colorado and Deer Valley in Utah.
Question: What is the Nazi foreign policy debate
Answer: The foreign policy and war aims of the Nazis have been the subject of debate among historians. The Nazis governed Germany between 1933 and 1945. There has been disagreement over whether Adolf Hitler aimed solely at European expansion and domination, or whether he planned for a long-term global empire.
Question: Which president was 1st to win a Nobel prize.
Answer: Theodore Roosevelt was the 1st president to win a Nobel Peace Prize. Roosevelt won the prize for helping negotiate an end to the Russo-Japanese War in 1904-1905.
Question: Tell me whether these people are authors or actors: Ernest Hemingway, Stephen King, George Orwell, Johnny Depp, George Clooney, Jonah Hill
Answer: Ernest Hemingway: Author
Stephen King: Author
George Orwell: Author
Johnny Depp: Actor
George Clooney: Actor
Jonah Hill: Actor
Question: What kind of questions can I ask you?
Answer: I can help with a wide variety of questions. Here are some examples:

- I can help you make a difficult decision, like whether to buy vs. rent a house 
- I can help answer broad questions, like how to live healthier
- I can help brainstorm ideas, like for a blog post or essay
- I can help edit something you've already written, for clarity or brevity

I am not perfect, but I'll try my best!
Question: Which is a bird or fish: Black tern, Blackfin tuna
Answer: Black tern is a bird, Blackfin tuna is a fish
Question: When is the Indian Teachers day celebrated?
Answer: Indian Teachers day is celebrated on 5th September
Question: Does the additional training on supervised tasks hurt performance in some tasks?
Evidence: Table 1 shows our results on GLUE with and without STILTs. Our addition of supplementary training boosts performance across many of the two sentence tasks. On each of our models trained with STILTs, we show improved overall average GLUE scores on the development set. For MNLI and QNLI target tasks, we observe marginal or no gains, likely owing to the two tasks already having large training sets. For the two single sentence tasks—the syntax-oriented CoLA task and the SST sentiment task—we find somewhat deteriorated performance. For CoLA, this mirrors results reported in BIBREF10 , who show that few pretraining tasks other than language modeling offer any advantage for CoLA. The Overall Best score is computed based on taking the best score for each task.
For the two single sentence tasks—the syntax-oriented CoLA task and the SST sentiment task—we find somewhat deteriorated performance.
Answer: Yes.
Question: Write a convincing letter to complain to a perfume manufacturer about a broken perfume bottle.
Answer: Hi

I recently ordered one of your perfume bottles. When the bottle arrived, I was disappointed to find out that there was a crack in the glass, and that some of the contents of the bottle had leaked into the surroundings. Unfortunately, this makes it impossible for me to use the bottle, or to take it on a trip for fear of further leaks.

Is there any chance you could refund the price of the bottle, or alternatively send me a new one?

Kind regards
Question: What is the dataset used to train the model?
Evidence: Clean-labeled Datasets. We use three clean labeled datasets. The first one is the movie sentence polarity dataset from BIBREF19. The other two datasets are laptop and restaurant datasets collected from SemEval-2016 . The former consists of laptop review sentences and the latter consists of restaurant review sentences. The original datasets (i.e., Laptop and Restaurant) were annotated with aspect polarity in each sentence. We used all sentences with only one polarity (positive or negative) for their aspects. That is, we only used sentences with aspects having the same sentiment label in each sentence. Thus, the sentiment of each aspect gives the ground-truth as the sentiments of all aspects are the same.
Noisy-labeled Training Datasets. For the above three domains (movie, laptop, and restaurant), we collected 2,000 reviews for each domain from the same review source. We extracted sentences from each review and assigned review's label to its sentences. Like previous work, we treat 4 or 5 stars as positive and 1 or 2 stars as negative. The data is noisy because a positive (negative) review can contain negative (positive) sentences, and there are also neutral sentences. This gives us three noisy-labeled training datasets. We still use the same test sets as those for the clean-labeled datasets. Summary statistics of all the datasets are shown in Table TABREF9.
Clean-labeled Datasets. We use three clean labeled datasets. The first one is the movie sentence polarity dataset from BIBREF19. The other two datasets are laptop and restaurant datasets collected from SemEval-2016 .
Noisy-labeled Training Datasets. For the above three domains (movie, laptop, and restaurant), we collected 2,000 reviews for each domain from the same review source.
Answer: The answers are shown as follows:
*  movie sentence polarity dataset from BIBREF19
* laptop and restaurant datasets collected from SemEval-201
* we collected 2,000 reviews for each domain from the same review source
Question: How many F1 races are there in a year?
Answer: There are 23 scheduled races in 2023, the most of any year. When F1 first started in 1950, there were only 7 races. Since then, the number of races has steadily increased.
Question: Identify which instrument is string or percussion: Tbilat, Harp guitar
Answer: Harp guitar is string, Tbilat is percussion.
Question: what classic language models are mentioned in the paper?
Evidence: In this paper, we examine the quality–performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well documented BIBREF3 , but to our knowledge no one has examined the performances tradeoffs. With deployment on a mobile device in mind, we evaluate energy usage and inference latency on a Raspberry Pi (which shares the same ARM architecture as nearly all smartphones today). We find that a 2.5 $\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\times $ longer and requires 32 $\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality–performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. Nevertheless, NLP engineers should arguably keep these tradeoffs in mind when selecting a particular operating point.
Kneser–Ney smoothing
In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today.
Answer: The answers are shown as follows:
* Kneser–Ney smoothing
Question: Write a short story about a person who gets fired from his job, but ends up having the best day of his life.
Answer: On March 3rd 1974, Jim was fired from his job as a janitor at Elmhurst Elementary School. While officially, Jim was let go due to "budgetary constraints", it was quite clear to him that it had more to do with his age than any financial strain on the school district. See, Jim was born in 1899, he had just turned 75 years old in January and was starting to "slow down" on the job. He could no longer do some of the highly intensive labor that his profession often entailed, and it became noticeable. 

After his last day of work, Jim walked to his home, which conveniently was only three blocks south of the school. On his walk, he couldn't help but feel sad and slightly embarrassed. He knew that his wife, Miriam, would understand, but he felt like he let her and himself down. For nearly 40 years, Jim worked at Elmhurst Elementary School and was beloved by the thousands of students that came through those doors. While he wasn't going to miss cleaning up student's throw-up or a cafeteria that resembled more of a war zone than a public eating space, he was going to miss the children. Watching them learn, play, and grow was always what made the job worth it to him.

When Jim walked through the door of his one story home, Miriam was there to greet him. She could tell what had happened by the look on his face. Particularly, she could tell by his eyes. Jim's eyes were a portal into his soul and, as such, a window into his emotions.

As Jim walked through the living room and into the kitchen, he saw two people, a man and a woman, sitting at the table, and in the woman's arms was a baby. This man was his son, Tommy, who he had not seen since he deployed for the Vietnam War three years prior. Up until this moment, Jim wasn't even sure if his son was alive. Jim's eyes opened wide with sheer elation at the sight, and a tear slowly formed in the corner of his left eye.

Over the next few hours, Jim sat with his son and his family and discussed all that had happened since the last time they had seen each other. Tommy spoke about how he had been designated as MIA by the US government after a particularly brutal ambush during one of his morning patrols. After many months of hiding from the enemy and searching for his unit, Tommy eventually stumbled across one of the final remaining US camps stationed in South Vietnam. It was at this camp that Tommy met his now wife, Sara. She was the nurse who oversaw his recovery. They fell in love immediately, got married, and Sara became pregnant, all before even leaving Vietnam for the United States. Tommy and Sara had returned to the US only a month ago, and their baby was born two weeks later.

While Jim laid in bed that night, he couldn't believe all the change that one day could bring. He woke up a janitor, and went to bed a grandfather. He knew his new job, and he looked forward to it. This was the best day of his life.
Question: What is used to provide reproducible benchmark results?
Evidence: Several works have been proposed to learn multilingual word embeddings, which are then combined to perform cross-lingual document classifications. These word embeddings are trained on either word alignments or sentence-aligned parallel corpora. To provide reproducible benchmark results, we use MultiCCA word embeddings published by BIBREF3 .
There are multiple ways to combine these word embeddings for classification. We train a simple one-layer convolutional neural network (CNN) on top of the word embeddings, which has shown to perform well on text classification tasks regardless of training data size BIBREF4 . Specifically, convolutional filters are applied to windows of word embeddings, with a max-over-time pooling on top of them. We freeze the multilingual word embeddings while only training the classifier. Hyper-parameters such as convolutional output dimension, window sizes are done by grid search over the Dev set of the same language as the train set.
To provide reproducible benchmark results, we use MultiCCA word embeddings published by (Ammar et al., 2016).
Answer: MultiCCA word embeddings.
Question: List top 5 people with most gold medals
Answer: 1. Michael Phelps - 23 gold
2. Larisa Latynina - 9 gold 
3. Paavo Nurmi - 9 gold 
4. Mark Spitz - 9 gold 
5. Carl Lewis - 9 gold
Question: Which films contain Tom Cruise and which do not contain Tom Cruise: "Trading Places", "Risky Business", "The Hunt for Red October", "Top Gun", "The Hangover", "Born on the 4th of July", "Saving Private Ryan", and "Reds"
Answer: The films which contain Tom Cruise are: "Risky Business", "Top Gun", and "Born on the 4th of July." The films which do not contain Tom Cruise are: "Trading Places", "The Hunt for Red October","The Hangover", "Saving Private Ryan", and "Reds."
Question: What is Genetics?
Answer: Genetics is the study of genes, genetic variation, and heredity in organisms. It is an important branch in biology because heredity is vital to organisms' evolution. Gregor Mendel, a Moravian Augustinian friar working in the 19th century in Brno, was the first to study genetics scientifically. Mendel studied "trait inheritance", patterns in the way traits are handed down from parents to offspring over time. He observed that organisms (pea plants) inherit traits by way of discrete "units of inheritance". This term, still used today, is a somewhat ambiguous definition of what is referred to as a gene.

Trait inheritance and molecular inheritance mechanisms of genes are still primary principles of genetics in the 21st century, but modern genetics has expanded to study the function and behavior of genes. Gene structure and function, variation, and distribution are studied within the context of the cell, the organism (e.g. dominance), and within the context of a population. Genetics has given rise to a number of subfields, including molecular genetics, epigenetics and population genetics. Organisms studied within the broad field span the domains of life (archaea, bacteria, and eukarya).

Genetic processes work in combination with an organism's environment and experiences to influence development and behavior, often referred to as nature versus nurture. The intracellular or extracellular environment of a living cell or organism may increase or decrease gene transcription. A classic example is two seeds of genetically identical corn, one placed in a temperate climate and one in an arid climate (lacking sufficient waterfall or rain). While the average height of the two corn stalks may be genetically determined to be equal, the one in the arid climate only grows to half the height of the one in the temperate climate due to lack of water and nutrients in its environment.
Question: How many planets are there in the Solar System ?
Answer: As of 2023, there are 8 planets in the Solar system :
- Mercury
- Venus
- Earth
- Mars
- Jupiter
- Saturn
- Uranus
- Neptune
Up until 2006, Pluto was considered the 9th planet before being reclassified as a "dwarf planet" by the International Astronomical Union (IAU).
Question: Give me some ideas of where I should go on holiday.
Answer: You could go for a beach, or resort holiday - somewhere like the Caribbean, Hawaii or Malaysia, where you can relax by the sea drinking a cocktail, or go swimming, snorkelling or try out water sports. Alternatively, you could go to a national park, like Yellowstone or the Grand Canyon, where you can go hiking and enjoy beautiful nature. Europe has many exciting holiday options as well: you could also visit the ice hotel in Finland, ride a gondala in Venice or visit Buckingham Palace, the home of the King, in London.
Question: Write a short story about a revolution for decentralisation against an evil government set in a dystopian future where cryptocurrencies prevail.
Answer: ‘Viva la revolución’ Lia thought as she completed the last transaction. The artifice hack had gone down flawlessly with stacks of cryptocurrencies airdropped to all Skvllpvnkz in the Hideout. The Skvllpvnkz were coalition of survivors bound to overthrow the evil corporation.

Lia knew she’d been connected to the Corp’s servers far too long now and her location would be exposed. There was little time. She began the live stream: 

‘Skvllpvnkz! We endured and raged with fury, but now we rise from the ashes! Delirious from abstract memories of a world the elders spoke of and only dream of! By the will of Gods we will prevail! Our ancestors fought for freedom. We fight for decentralisation!’ 

The aircast is interrupted and Lia is cut off.

At the Hideout, time stood still as Lia’s voice echoed in the vast underground maze. The silence that followed was brief. As the cryptocurrency airdrop materialised, a victorious uproar spread like a wave. The Skvllpvnkz would finally be free.
Question: Tell me which of the following clubs are playing American football or soccer? Inter Milan, Juventus, Barcelona, New York Jets, Seattle Sounders, Seattle Seahawks, LA Galaxy.
Answer: Inter Milan, Juventus, Barcelona,  Seattle Sounders,  LA Galaxy are playing soccer. 

New York Jets, Seattle Seahawks are American football teams.