DataHammer/scimrc huggingface dataset:
Question: What do vector space methods that measure semantic similarity and relatedness often rely on?
Evidence: Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co--occurrence frequencies or statistical measures of association to weight the importance of particular co--occurrences. In this paper, we extend these methods by incorporating a measure of semantic similarity based on a human curated taxonomy into a second--order vector representation. This results in a measure of semantic relatedness that combines both the contextual information available in a corpus--based vector space representation with the semantic knowledge found in a biomedical ontology. Our results show that incorporating semantic similarity into a second order co--occurrence matrices improves correlation with human judgments for both similarity and relatedness, and that our method compares favorably to various different word embedding methods that have recently been evaluated on the same reference standards we have used.
Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co–occurrence frequencies or statistical measures of association to weight the importance of particular co–occurrences.
Answer: Distributional information such as co–occurrence frequencies or statistical measures of association to weight the importance of particular co–occurrences.
Question: Which exchanges is RELX listed on?
Evidence: RELX plc (pronounced "Rel-ex") is a British multinational information and analytics company headquartered in London, England. Its businesses provide scientific, technical and medical information and analytics; legal information and analytics; decision-making tools; and organise exhibitions. It operates in 40 countries and serves customers in over 180 nations. It was previously known as Reed Elsevier, and came into being in 1993 as a result of the merger of Reed International, a British trade book and magazine publisher, and Elsevier, a Netherlands-based scientific publisher. The company is publicly listed, with shares traded on the London Stock Exchange, Amsterdam Stock Exchange and New York Stock Exchange (ticker symbols: London: REL, Amsterdam: REN, New York: RELX). The company is one of the constituents of the FTSE 100 Index, Financial Times Global 500 and Euronext 100 Index.
Answer: RELX plc is listed on the London, Amsterdam and New York Stock exchanges.
Question: Why do Germans love their cars?
Answer: Germans spend a lot of money on their cars. Whether by leasing or buying. It is a kind of status symbol and German engineering is memorized every day via advertising or even cars driving around. Many sports car manufacturers like Porsche or even Mercedes manage to attract more and more clientele through their pricing and advertising through social media, serving not only the rich society.
Question: Given this paragraph, in what part of England does the River Thames flow, and what is its length in kilometres?
Evidence: The River Thames (/tɛmz/ (listen) TEMZ), known alternatively in parts as the River Isis, is a river that flows through southern England including London. At 215 miles (346 km), it is the longest river entirely in England and the second-longest in the United Kingdom, after the River Severn. The river rises at Thames Head in Gloucestershire and flows into the North Sea near Tilbury, Essex and Gravesend, Kent, via the Thames Estuary. From the west it flows through Oxford (where it is sometimes called the Isis), Reading, Henley-on-Thames and Windsor. The Thames also drains the whole of Greater London.
Answer: The River Thames flows through Southern England and it is 346 kilometres long.
Question: Given a reference text about the Build Back Better Plan, what are the 3 parts that make up the plan?
Evidence: The Build Back Better Plan or Build Back Better agenda was a legislative framework proposed by U.S. president Joe Biden between 2020 and 2021. Generally viewed as ambitious in size and scope, it sought the largest nationwide public investment in social, infrastructural, and environmental programs since the 1930s Great Depression-era policies of the New Deal.

The Build Back Better plan was divided into three parts:

American Rescue Plan (ARP), a COVID-19 pandemic-relief bill;
American Jobs Plan (AJP), a proposal to address long-neglected infrastructure needs and reduce America's contributions to destructive effects of climate change; and
American Families Plan (AFP), a proposal to fund a variety of social policy initiatives, some of which (e.g., paid family leave) had never before been enacted nationally in the U.S.
The first part was passed as the $1.9 trillion American Rescue Plan Act of 2021, and was signed into law in March 2021. The other two parts were reworked into different bills over the course of extensive negotiations. Aspects of the AJP's infrastructure goals were diverted into the Infrastructure Investment and Jobs Act, which was signed into law in November 2021. Other AJP priorities (e.g., climate change remediation, home health care reform, etc.) were then merged with the AFP to form the Build Back Better Act. The bill passed the Democratic-controlled House of Representatives but struggled to gain the support of Democrats Joe Manchin of West Virginia and Kyrsten Sinema of Arizona in the evenly divided Senate, with unified Republican opposition. Manchin and Sinema negotiated the reduction of Build Back Better Act's size, scope, and cost significantly with Biden and Democratic congressional leaders, but Manchin, widely viewed as the key swing vote needed to pass the bill in the Senate, ultimately rejected it over the procedural tactics used. Continued negotiations between Manchin and Senate Majority Leader Chuck Schumer eventually resulted in the Inflation Reduction Act of 2022, which was signed into law in August 2022, and incorporated some of the Build Back Better Act's climate change, healthcare, and tax reform proposals while excluding its social safety net proposals.
Answer: The three components of the Build Back Better plan are:
- American Rescue Plan
- American Jobs Plan
- American Families Plan
Question: What is fractional-reserve banking?
Answer: Fractional-reserve banking is the system of banking operating in almost all countries worldwide, under which banks that take deposits from the public are required to hold a proportion of their deposit liabilities in liquid assets as a reserve, and are at liberty to lend the remainder to borrowers. Bank reserves are held as cash in the bank or as balances in the bank's account at the central bank. The country's central bank determines the minimum amount that banks must hold in liquid assets, called the "reserve requirement" or "reserve ratio". Most commercial banks hold more than this minimum amount as excess reserves.

Bank deposits are usually of a relatively short-term duration, and may be "at call", while loans made by banks tend to be longer-term, resulting in a risk that customers may at any time collectively wish to withdraw cash out of their accounts in excess of the bank reserves. The reserves only provide liquidity to cover withdrawals within the normal pattern. Banks and the central bank expect that in normal circumstances only a proportion of deposits will be withdrawn at the same time, and that reserves will be sufficient to meet the demand for cash. However, banks may find themselves in a shortfall situation when depositors wish to withdraw more funds than the reserves held by the bank. In that event, the bank experiencing the liquidity shortfall may borrow short-term funds in the interbank lending market from banks with a surplus. In exceptional situations, such as during an unexpected bank run, the central bank may provide funds to cover the short-term shortfall as lender of last resort.
Question: How many conversations do the authors gather utterances from?
Evidence: We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
We first gather abusive utterances from 600K conversations with US-based customers.
Answer: 600K.
Question: What does the author analyze from the data in Table 1?
Evidence: As described in Section SECREF6 various syntax features were used namely, Part-of-Speech tags, brown clusters of TweetNLP project. However, these didn't perform well in cross validation. Hence, they were dropped from the final system. While performing grid-search as mentioned in Section SECREF14 , keeping all the lexicon based features same, choice of combination of emoji vector and word vectors are varied to minimize cross validation metric. Table TABREF16 describes the results for experiments conducted with different combinations of word vectors. Emoji embeddings BIBREF16 give better results than using plain GloVe and Edinburgh embeddings. Edinburgh embeddings outperform GloVe embeddings in Joy and Sadness category but lag behind in Anger and Fear category. The official submission comprised of the top-performing model for each emotion category. This system ranked 3 for the entire test dataset and 2 for the subset of the test data formed by taking every instance with a gold emotion intensity score greater than or equal to 0.5. Post competition, experiments were performed on ensembling diverse models for improving the accuracy. An ensemble obtained by averaging the results of the top 2 performing models outperforms all the individual models.
Emoji embeddings (Eisner et al., 2016) give better results than using plain GloVe and Edinburgh embeddings. Edinburgh embeddings outperform GloVe embeddings in Joy and Sadness category but lag behind in Anger and Fear category.
Answer: Emoji embeddings (Eisner et al., 2016) give better results than using plain GloVe and Edinburgh embeddings. Edinburgh embeddings outperform GloVe embeddings in Joy and Sadness category but lag behind in Anger and Fear category.
Question: What shape are Philodendron leaves?
Evidence: The leaves are usually large and imposing, often lobed or deeply cut, and may be more or less pinnate. They can also be oval, spear-shaped, or in many other possible shape variations. The leaves are borne alternately on the stem. A quality of philodendrons is that they do not have a single type of leaf on the same plant. Instead, they have juvenile leaves and adult leaves, which can be drastically different from one another. The leaves of seedling philodendrons are usually heart-shaped early in the life of the plant. But after it has matured past the seedling stage, the leaves will acquire the typical juvenile leaf's shape and size. Later in the philodendron's life, it starts producing adult leaves, a process called metamorphosis. Most philodendrons go through metamorphosis gradually; there is no immediately distinct difference between juvenile and adult leaves. Aside from being typically much bigger than the juvenile leaves, the shape of adult leaves can be significantly different. In fact, considerable taxonomic difficulty has occurred in the past due to these differences, causing juvenile and adult plants to mistakenly be classified as different species.
Answer: They are spear-shaped
Question: How many variants of the PrintNightmare existed?
Evidence: PrintNightmare was a critical security vulnerability affecting the Microsoft Windows operating system. The vulnerability occurred within the print spooler service. There were two variants, one permitting remote code execution (CVE-2021-34527), and the other leading to privilege escalation (CVE-2021-1675). A third vulnerability (CVE-2021-34481) was announced July 15, 2021, and upgraded to remote code execution by Microsoft in August.

On July 6, 2021, Microsoft started releasing out-of-band (unscheduled) patches attempting to address the vulnerability. Due to its severity, Microsoft released patches for Windows 7 and Windows Server 2012, despite being out of their mainstream support period. The patches resulted in some printers ceasing to function. Researchers have noted that the vulnerability has not been fully addressed by the patches. After the patch is applied, only administrators account on Windows print server, will be able to install printer drivers, as part of the vulnerability related to the ability of non-administrators to install printer drivers on the system, such as shared printers on system without sharing password protection.

The organization which discovered the vulnerability, Sangfor, published a proof of concept in a public GitHub repository. Apparently published in error, or as a result of a miscommunication between the researchers and Microsoft, the proof of concept was deleted shortly after. However, several copies have since appeared online.
Answer: There are two variants of the PrintNightmare, one permitting remote code execution and the other one leading to privilege escalation.
Question: Describe how you would approach a corporate rebrand. Share your objectives, timeline, required budget and resources, and how you would measure your success.
Answer: You should approach a rebrand by partnering with a creative agency to develop a polished, scalable version of a company story, which would likely be a 4-6 month project.

Objective: Develop the corporate story — who you are as a company, your vision and mission, what you believe, what you do and how you do it — while also partnering with various teams across the business to ensure the corporate story is married with the product story, differentiators, and the solutions and value you provide to customers. The result should be an authentic representation of the company. 

Timeline: A rebrand project would include 6-8 weeks of discovery (conducting a formal audit of the company's current brand and interviews with team members, customers and partners), leading to multiple weeks of reviews, eventually resulting in a brand identity and brand book to share the updated brand with your employee base. 

Resources and budget: Estimated $300,000 - 500,000 (cost varies widely and depends on the size of the firm and scope of project). In order for this project to be successful, secure upfront executive buy-in and alignment, and time from the executive team starting at the discovery phase through reviews and the final debut.

Measurement: Unfortunately, it’s difficult to assign a quantitative metric to measure the success of a rebrand and messaging project, so put qualitative measurements in place for the short-term, assessing how well you captured how your customers think of your company, and how well you captured the vision for the future. In the long-term, invest in brand awareness surveys and track NPS scores.
Question: What system do they build?
Evidence: BioASQ is a semantic indexing, question answering (QA) and information extraction challenge BIBREF0 . We participated in Task B of the challenge which is concerned with biomedical QA. More specifically, our system participated in Task B, Phase B: Given a question and gold-standard snippets (i.e., pieces of text that contain the answer(s) to the question), the system is asked to return a list of answer candidates.
The fifth BioASQ challenge is taking place at the time of writing. Five batches of 100 questions each were released every two weeks. Participating systems have 24 hours to submit their results. At the time of writing, all batches had been released.
The questions are categorized into different question types: factoid, list, summary and yes/no. Our work concentrates on answering factoid and list questions. For factoid questions, the system's responses are interpreted as a ranked list of answer candidates. They are evaluated using mean-reciprocal rank (MRR). For list questions, the system's responses are interpreted as a set of answers to the list question. Precision and recall are computed by comparing the given answers to the gold-standard answers. F1 score, i.e., the harmonic mean of precision and recall, is used as the official evaluation measure .
Most existing biomedical QA systems employ a traditional QA pipeline, similar in structure to the baseline system by weissenborn2013answering. They consist of several discrete steps, e.g., named-entity recognition, question classification, and candidate answer scoring. These systems require a large amount of resources and feature engineering that is specific to the biomedical domain. For example, OAQA BIBREF1 , which has been very successful in last year's challenge, uses a biomedical parser, entity tagger and a thesaurus to retrieve synonyms.
Our system, on the other hand, is based on a neural network QA architecture that is trained end-to-end on the target task. We build upon FastQA BIBREF2 , an extractive factoid QA system which achieves state-of-the-art results on QA benchmarks that provide large amounts of training data. For example, SQuAD BIBREF3 provides a dataset of $\approx 100,000$ questions on Wikipedia articles. Our approach is to train FastQA (with some extensions) on the SQuAD dataset and then fine-tune the model parameters on the BioASQ training set.
Note that by using an extractive QA network as our central component, we restrict our system's responses to substrings in the provided snippets. This also implies that the network will not be able to answer yes/no questions. We do, however, generalize the FastQA output layer in order to be able to answer list questions in addition to factoid questions.
We build upon FastQA, an extractive factoid QA system which achieves state-of-the-art results on QA benchmarks that provide large amounts of training data.
Answer: An extractive factoid QA system.
Question: Help me select what to wear for a beach party with my friends and what clothes to bring in my backpack so I can change for dinner. The weather will be hot during the day and cool at night. 

Here are my options: A winter coat, a bathing suit, sandals, a short sleeve button down shirt, a hat, sunglasses, underwear, socks, sneakers, a rain coat, a tuxedo, sweatpants, jeans, a tank top, a pair of khakis
Answer: Since you will be at the beach and it will be hot you will want a bathing suit, sandals, a hat, sunglasses and a tank top. 

In you backpack you should bring a short sleeve button down shirt, underwear, socks, sneakers and jeans to change into for dinner.
Question: Extract all of the dates mentioned in this paragraph and list them using bullets in the format {Date} - {Description}
Evidence: Ilya (or Ilia) Smirin (Hebrew: איליה יוליביץ' סמירין; Russian: Илья Юльевич Смирин, romanized: Ilya Yulievich Smirin; born January 21, 1968) is a Byelorussian SSR-born and an Israeli chess player. He was awarded the title of Grandmaster by FIDE in 1990.
Answer: • January 21, 1968 - This is when Ilya Smirin was born
• 1990 - This is when Ilya Smirin was awarded the title of Grandmaster by FIDE
Question: What is special about Luis Miguel's music?
Answer: Luis Miguel is a Puerto Rican born Mexican singer. His nickname among the Latin American music industry and fan base is 'El Sol de México'. Luis Miguel is famous for taking legacy bolero and mariachi songs and modernizing them into mainstream pop romantic hits. Luis Miguel also has an unusual vocal range that spans three octaves. The combination of Luis Miguel's rare vocal talent along with the beautiful composition of the songs that he sings makes his music special.
Question: Given the following text, what is special about Prime Numbers?
Evidence: A prime number (or a prime) is a natural number greater than 1 that is not a product of two smaller natural numbers. A natural number greater than 1 that is not prime is called a composite number. For example, 5 is prime because the only ways of writing it as a product, 1 × 5 or 5 × 1, involve 5 itself. However, 4 is composite because it is a product (2 × 2) in which both numbers are smaller than 4. Primes are central in number theory because of the fundamental theorem of arithmetic: every natural number greater than 1 is either a prime itself or can be factorized as a product of primes that is unique up to their order.

The property of being prime is called primality. A simple but slow method of checking the primality of a given number 
�
n, called trial division, tests whether 
�
n is a multiple of any integer between 2 and 
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{\sqrt {n}}. Faster algorithms include the Miller–Rabin primality test, which is fast but has a small chance of error, and the AKS primality test, which always produces the correct answer in polynomial time but is too slow to be practical. Particularly fast methods are available for numbers of special forms, such as Mersenne numbers. As of December 2018 the largest known prime number is a Mersenne prime with 24,862,048 decimal digits.

There are infinitely many primes, as demonstrated by Euclid around 300 BC. No known simple formula separates prime numbers from composite numbers. However, the distribution of primes within the natural numbers in the large can be statistically modelled. The first result in that direction is the prime number theorem, proven at the end of the 19th century, which says that the probability of a randomly chosen large number being prime is inversely proportional to its number of digits, that is, to its logarithm.

Several historical questions regarding prime numbers are still unsolved. These include Goldbach's conjecture, that every even integer greater than 2 can be expressed as the sum of two primes, and the twin prime conjecture, that there are infinitely many pairs of primes having just one even number between them. Such questions spurred the development of various branches of number theory, focusing on analytic or algebraic aspects of numbers. Primes are used in several routines in information technology, such as public-key cryptography, which relies on the difficulty of factoring large numbers into their prime factors. In abstract algebra, objects that behave in a generalized way like prime numbers include prime elements and prime ideals.
Answer: A prime number is a natural number great than one that is only divisible by itself and one. e.g. 5 is a prime number as it is only divisible by 5 and 1, but 4 is not as 4 is divisible by 4,2 and 1