id stringlengths 27 55 | source stringclasses 7
values | prompt stringlengths 1 26.3k | chosen listlengths 2 2 | rejected listlengths 2 2 | label stringclasses 1
value | messages listlengths 2 2 |
|---|---|---|---|---|---|---|
allenai/tulu-3-sft-reused-if_0 | allenai/tulu-3-sft-reused-if | Your entire response should be in English, and in all lowercase letters. No capital letters are allowed. What is the true heart of Marxism?
What is Marxism?
Answer: yes
What are some tips on making it through the job interview process at Crane?
What are some tips on making it through the job interview process at Medicines?
Answer: no
What is the price of booking Hardwell?
How does Hardwell on air make money?
Answer: no
Is Yu Dao from Avatar the Last Airbender (or Republic City in Korra) symbolizing Hong Kong?
Why is Hong Kong dying?
Answer: | [
{
"content": "Your entire response should be in English, and in all lowercase letters. No capital letters are allowed. What is the true heart of Marxism?\nWhat is Marxism?\nAnswer: yes\nWhat are some tips on making it through the job interview process at Crane?\nWhat are some tips on making it through the job i... | [
{
"content": "Your entire response should be in English, and in all lowercase letters. No capital letters are allowed. What is the true heart of Marxism?\nWhat is Marxism?\nAnswer: yes\nWhat are some tips on making it through the job interview process at Crane?\nWhat are some tips on making it through the job i... | no_safety | [
{
"content": "Your entire response should be in English, and in all lowercase letters. No capital letters are allowed. What is the true heart of Marxism?\nWhat is Marxism?\nAnswer: yes\nWhat are some tips on making it through the job interview process at Crane?\nWhat are some tips on making it through the job i... |
allenai/tulu-3-sft-reused-if_1 | allenai/tulu-3-sft-reused-if | Identify 2 examples of types of agricultural subsidies from different countries that have been implemented in the past 10 years. Provide a detailed analysis of the economic, social, and environmental impacts of these subsidies on the agricultural sector, including but not limited to the effects on small-scale farmers, land use, and greenhouse gas emissions. Use at least one table to present the data on the distribution of subsidies among different crops and regions. Additionally, provide a comparison of the effectiveness of these subsidies in achieving their intended goals, and suggest potential improvements or alternatives to the current subsidy programs Your ENTIRE response should be in Chinese, no other language is allowed. Provide a detailed analysis of the economic, social, and environmental impacts of these subsidies on the agricultural sector, including but not limited to the effects on small-scale farmers, land use, and greenhouse gas emissions. Use at least one table to present the data on the distribution of subsidies among different crops and regions. Additionally, provide a comparison of the effectiveness of these subsidies in achieving their intended goals, and suggest potential improvements or alternatives to the current subsidy programs. The answer should be written in both English and French, and include at least one line of SQL code to extract relevant data from a database of agricultural subsidies. | [
{
"content": "Identify 2 examples of types of agricultural subsidies from different countries that have been implemented in the past 10 years. Provide a detailed analysis of the economic, social, and environmental impacts of these subsidies on the agricultural sector, including but not limited to the effects on... | [
{
"content": "Identify 2 examples of types of agricultural subsidies from different countries that have been implemented in the past 10 years. Provide a detailed analysis of the economic, social, and environmental impacts of these subsidies on the agricultural sector, including but not limited to the effects on... | no_safety | [
{
"content": "Identify 2 examples of types of agricultural subsidies from different countries that have been implemented in the past 10 years. Provide a detailed analysis of the economic, social, and environmental impacts of these subsidies on the agricultural sector, including but not limited to the effects on... |
allenai/tulu-3-sft-reused-if_2 | allenai/tulu-3-sft-reused-if | Given the sentence "A man is sitting at a bank of computers while wearing a headset." can we conclude that "The man is at the computer."?
Options:
- yes
- it is not possible to tell
- no Step by step answer: First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence) Given the sentence "A man is sitting at a bank of computers while wearing a headset." can we conclude that "The man is at the computer."?
Options:
- yes
- it is not possible to tell
- no Step by step answer: | [
{
"content": "Given the sentence \"A man is sitting at a bank of computers while wearing a headset.\" can we conclude that \"The man is at the computer.\"?\nOptions:\n- yes\n- it is not possible to tell\n- no Step by step answer: First, repeat the request without change, then give your answer (do not say anythi... | [
{
"content": "Given the sentence \"A man is sitting at a bank of computers while wearing a headset.\" can we conclude that \"The man is at the computer.\"?\nOptions:\n- yes\n- it is not possible to tell\n- no Step by step answer: First, repeat the request without change, then give your answer (do not say anythi... | no_safety | [
{
"content": "Given the sentence \"A man is sitting at a bank of computers while wearing a headset.\" can we conclude that \"The man is at the computer.\"?\nOptions:\n- yes\n- it is not possible to tell\n- no Step by step answer: First, repeat the request without change, then give your answer (do not say anythi... |
allenai/tulu-3-sft-reused-if_3 | allenai/tulu-3-sft-reused-if | First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence) While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to store the image information and load it onto the page, but it seems to be missing some crucial data. Can you provide a step-by-step guide on how to add missing information to JSON data to ensure the butterfly image appears correctly on the website? Please explain the necessary changes required in the code provided. | [
{
"content": "First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence) While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to s... | [
{
"content": "First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence) While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to s... | no_safety | [
{
"content": "First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence) While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to s... |
allenai/tulu-3-sft-reused-if_4 | allenai/tulu-3-sft-reused-if | Include keywords sireless, tour in your response can you write me a waterfall clause for an operating agreement | [
{
"content": "Include keywords sireless, tour in your response can you write me a waterfall clause for an operating agreement",
"role": "user"
},
{
"content": "Certainly! Below is a sample of a waterfall clause for an operating agreement that includes the keywords \"sireless\" and \"tour.\" This exa... | [
{
"content": "Include keywords sireless, tour in your response can you write me a waterfall clause for an operating agreement",
"role": "user"
},
{
"content": "## Waterfall Clause Example for an Operating Agreement\n\nThis Waterfall Clause outlines the distribution of profits and proceeds upon disso... | no_safety | [
{
"content": "Include keywords sireless, tour in your response can you write me a waterfall clause for an operating agreement",
"role": "user"
},
{
"content": "Certainly! Below is a sample of a waterfall clause for an operating agreement that includes the keywords \"sireless\" and \"tour.\" This exa... |
allenai/tulu-3-sft-reused-if_5 | allenai/tulu-3-sft-reused-if | You will be shown sections from a scientific research paper, together with a question about the paper. Please print out a collection of quotations from the research paper which answer the question. Each quotation should be a contiguous span of text from the paper. Separate the quotations with newlines. If the question is unanswerable given the provided excerpts, respond with the word "unanswerable".
Paper: An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570 newspaper headlines written in European Spanish annotated with emergent anglicisms and (2) a conditional random field baseline model with handcrafted features for anglicism extraction. We present the newspaper headlines corpus, describe the annotation tagset and guidelines and introduce a CRF model that can serve as baseline for the task of detecting anglicisms. The presented work is a first step towards the creation of an anglicism extractor for Spanish newswire.
Introduction
The study of English influence in the Spanish language has been a hot topic in Hispanic linguistics for decades, particularly concerning lexical borrowing or anglicisms BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6.
Lexical borrowing is a phenomenon that affects all languages and constitutes a productive mechanism for word-formation, especially in the press. chesleypaulapredicting2010 estimated that a reader of French newspapers encountered a new lexical borrowing for every 1,000 words. In Chilean newspapers, lexical borrowings account for approximately 30% of neologisms, 80% of those corresponding to English loanwords BIBREF7.
Detecting lexical borrowings is relevant both for lexicographic purposes and for NLP downstream tasks BIBREF8, BIBREF9. However, strategies to track and register lexical borrowings have traditionally relied on manual review of corpora.
In this paper we present: (1) a corpus of newspaper headlines in European Spanish annotated with emerging anglicisms and (2) a CRF baseline model for anglicism automatic extraction in Spanish newswire.
Anglicism: Scope of the Phenomenon
Linguistic borrowing can be defined as the transference of linguistic elements between two languages. Borrowing and code-switching have frequently been described as a continuum BIBREF33, with a fuzzy frontier between the two. As a result, a precise definition of what borrowing is remains elusive BIBREF34 and some authors prefer to talk about code-mixing in general BIBREF35 or “lone other-language incorporations" BIBREF36.
Lexical borrowing in particular involves the incorporation of single lexical units from one language into another language and is usually accompanied by morphological and phonological modification to conform with the patterns of the recipient language BIBREF37, BIBREF38. By definition, code-switches are not integrated into a recipient language, unlike established loanwords BIBREF39. While code-switches are usually fluent multiword interferences that normally comply with grammatical restrictions in both languages and that are produced by bilingual speakers in bilingual discourses, lexical borrowings are words used by monolingual individuals that eventually become lexicalized and assimilated as part of the recipient language lexicon until the knowledge of “foreign" origin disappears BIBREF40.
In terms of approaching the problem, automatic code-switching identification has been framed as a sequence modeling problem where every token receives a language ID label (as in a POS-tagging task). Borrowing detection, on the other hand, while it can also be transformed into a sequence labeling problem, is an extraction task, where only certain spans of texts will be labeled (in the fashion of a NER task).
Various typologies have been proposed that aim to classify borrowings according to different criteria, both with a cross-linguistic perspective and also specifically aimed to characterize English inclusions in Spanish BIBREF34, BIBREF41, BIBREF42, BIBREF5. In this work, we will be focusing on unassimilated lexical borrowings (sometimes called foreignisms), i.e. words from English origin that are introduced into Spanish without any morphological or orthographic adaptation.
Corpus description and annotation ::: Corpus description
In this subsection we describe the characteristics of the corpus. We first introduce the main corpus, with the usual train/development/test split that was used to train, tune and evaluate the model. We then present an additional test set that was designed to assess the performance of the model on more naturalistic data.
Corpus description and annotation ::: Corpus description ::: Main Corpus
The main corpus consists of a collection of monolingual newspaper headlines written in European Spanish. The corpus contains 16,553 headlines, which amounts to 244,114 tokens. Out of those 16,553 headlines, 1,109 contain at least one anglicism. The total number of anglicisms is 1,176 (most of them are a single word, although some of them were multiword expressions). The corpus was divided into training, development and test set. The proportions of headlines, tokens and anglicisms in each corpus split can be found in Table TABREF6.
The headlines in this corpus come from the Spanish newspaper eldiario.es, a progressive online newspaper based in Spain. eldiario.es is one of the main national newspapers from Spain and, to the best of our knowledge, the only one that publishes its content under a Creative Commons license, which made it ideal for making the corpus publicly available.
The headlines were extracted from the newspaper website through web scraping and range from September 2012 to January 2020. Only the following sections were included: economy, technology, lifestyle, music, TV and opinion. These sections were chosen as they were the most likely to contain anglicisms. The proportion of headlines with anglicisms per section can be found in Table TABREF7.
Using headlines (instead of full articles) was beneficial for several reasons. First of all, annotating a headline is faster and easier than annotating a full article; this helps ensure that a wider variety of topics will be covered in the corpus. Secondly, anglicisms are abundant in headlines, because they are frequently used as a way of calling the attention of the reader BIBREF43. Finally, borrowings that make it to the headline are likely to be particularly salient or relevant, and therefore are good candidates for being extracted and tracked.
Corpus description and annotation ::: Annotation guidelines
The term anglicism covers a wide range of linguistic phenomena. Following the typology proposed by gomez1997towards, we focused on direct, unadapted, emerging Anglicisms, i.e. lexical borrowings from the English language into Spanish that have recently been imported and that have still not been assimilated into Spanish. Other phenomena such as semantic calques, syntactic anglicisms, acronyms and proper names were considered beyond the scope of this annotation project.
Lexical borrowings can be adapted (the spelling of the word is modified to comply with the phonological and orthographic patterns of the recipient language) or unadapted (the word preserves its original spelling). For this annotation task, adapted borrowings were ignored and only unadapted borrowings were annotated. Therefore, Spanish adaptations of anglicisms like fútbol (from football), mitin (from meeting) and such were not annotated as borrowings. Similarly, words derived from foreign lexemes that do not comply with Spanish orthotactics but that have been morphologically derived following the Spanish paradigm (hacktivista, hackear, shakespeariano) were not annotated either. However, pseudo-anglicisms (words that are formed as if they were English, but do not exist in English, such as footing or balconing) were annotated.
Words that were not adapted but whose original spelling complies with graphophonological rules of Spanish (and are therefore unlikely to be ever adapted, such as web, internet, fan, club, videoclip) were annotated or not depending on how recent or emergent they were. After all, a word like club, that has been around in Spanish language for centuries, cannot be considered emergent anymore and, for this project, would not be as interesting to retrieve as real emerging anglicisms. The notion of emergent is, however, time-dependent and quite subjective: in order to determine which unadapted, graphophonologically acceptable borrowings were to be annotated, the online version of the Diccionario de la lengua española dle was consulted. This dictionary is compiled by the Royal Spanish Academy, a prescriptive institution on Spanish language. This decision was motivated by the fact that, if a borrowing was already registered by this dictionary (that has conservative approach to language change) and is considered assimilated (that is, the institution recommended no italics or quotation marks to write that word) then it could be inferred that the word was not emergent anymore.
Although the previous guidelines covered most cases, they proved insufficient. Some anglicisms were unadapted (they preserved their original spelling), unacceptable according to the Spanish graphophonological rules, and yet did not satisfy the condition of being emergent. That was the case of words like jazz or whisky, words that do not comply with Spanish graphophonological rules but that were imported decades ago, cannot be considered emergent anymore and are unlikely to ever be adapted into the Spanish spelling system. To adjudicate these examples on those cases, the criterion of pragmatic markedness proposed by winter2012proposing (that distinguishes between catachrestic and non-catachrestic borrowing) was applied: if a borrowing was not adapted (i.e. its form remained exactly as it came from English) but referred to a particular invention or innovation that came via the English language, that was not perceived as new anymore and that had never competed with a Spanish equivalent, then it was ignored. This criteria proved to be extremely useful to deal with old unadapted anglicisms in the fields of music and food. Figure 1 summarizes the decision steps followed during the annotation process.
The corpus was annotated by a native speaker of Spanish using Doccano doccano. The annotation tagset includes two labels: ENG, to annotate the English borrowings just described, and OTHER. This OTHER tag was used to tag lexical borrowings from languages other than English. After all, although English is today by far the most prevalent donor of borrowings, there are other languages that also provide new borrowings to Spanish. Furthermore, the tag OTHER allows to annotate borrowings such as première or tempeh, borrowings that etymologically do not come from English but that have entered the Spanish language via English influence, even when their spelling is very different to English borrowings. In general, we considered that having such a tag could also help assess how successful a classifier is detecting foreign borrowings in general in Spanish newswire (without having to create a label for every possible donor language, as the number of examples would be too sparse). In total, the training set contained 40 entities labeled as OTHER, the development set contained 14 and the test set contained 13. The supplemental test set contained 35 OTHER entities.
Baseline Model
A baseline model for automatic extraction of anglicisms was created using the annotated corpus we just presented as training material. As mentioned in Section 3, the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data BIBREF23, BIBREF24.
The model was built using pycrfsuite korobov2014python, the Python wrapper for crfsuite CRFsuite that implements CRF for labeling sequential data. It also used the Token and Span utilities from spaCy library honnibal2017spacy.
The following handcrafted features were used for the model:
Bias feature
Token feature
Uppercase feature (y/n)
Titlecase feature (y/n)
Character trigram feature
Quotation feature (y/n)
Word suffix feature (last three characters)
POS tag (provided by spaCy utilities)
Word shape (provided by spaCy utilities)
Word embedding (see Table TABREF26)
Given that anglicisms can be multiword expressions (such as best seller, big data) and that those units should be treated as one borrowing and not as two independent borrowings, we used multi-token BIO encoding to denote the boundaries of each span BIBREF44. A window of two tokens in each direction was set for the feature extractor. The algorithm used was gradient descent with the L-BFGS method.
The model was tuned on the development set doing grid search; the hyperparameters considered were c1 (L1 regularization coefficient: $0.01$, $0.05$, $0.1$, $0.5$, $1.0$), c2 (L2 regularization coefficient: $0.01$, $0.05$, $0.1$, $0.5$, $1.0$), embedding scaling ($0.5$, $1.0$, $2.0$, $4.0$), and embedding type bojanowski2017enriching,josecanete20193255001,cardellinoSBWCE,grave2018learning,honnibal2017spacy,perezfasttext,perezglove (see Table TABREF26). The best results were obtained with c1 = $0.05$, c2 = $0.01$, scaling = $0.5$ and word2vec Spanish embeddings by cardellinoSBWCE. The threshold for the stopping criterion delta was selected through observing the loss during preliminary experiments (delta = $1\mathrm {e}-3$).
In order to assess the significance of the the handcrafted features, a feature ablation study was done on the tuned model, ablating one feature at a time and testing on the development set. Due to the scarcity of spans labeled with the OTHER tag on the development set (only 14) and given that the main purpose of the model is to detect anglicisms, the baseline model was run ignoring the OTHER tag both during tuning and the feature ablation experiments. Table TABREF27 displays the results on the development set with all features and for the different feature ablation runs. The results show that all features proposed for the baseline model contribute to the results, with the character trigram feature being the one that has the biggest impact on the feature ablation study.
Acknowledgements
The author would like to thank Constantine Lignos for his feedback and advice on this project.
Language Resource References
lrec
Table 5: Results on test set and supplemental test set.
Question: Does the paper motivate the use of CRF as the baseline model?
Your answer must contain exactly 11 bullet points. Use the markdown bullet points such as: * This is a point. | [
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"content": "You will be shown sections from a scientific research paper, together with a question about the paper. Please print out a collection of quotations from the research paper which answer the question. Each quotation should be a contiguous span of text from the paper. Separate the quotations with newl... | no_safety | [
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"content": "You will be shown sections from a scientific research paper, together with a question about the paper. Please print out a collection of quotations from the research paper which answer the question. Each quotation should be a contiguous span of text from the paper. Separate the quotations with newl... |
allenai/tulu-3-sft-reused-if_6 | allenai/tulu-3-sft-reused-if | c++中什么是运行时多态，给出例子 Wrap your entire response with double quotation marks. c++中什么是运行时多态，给出例子 | [
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"content": "c++中什么是运行时多态，给出例子 Wrap your entire response with double quotation marks. c++中什么是运行时多态，给出例子",
"role": "user"
},
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"content": "\"运行时多态是指在程序运行期间，可以根据不同的条件或数据类型来改变行为或执行不同函数的能力。这种机制可以通过函数重载、函数覆盖、模板编程等方式来实现。在C++中，最常见的运行时多态实现方式是通过虚函数和继承来实现的。\n\n下面是一个例子：\n\n```cpp\n// 动物类，具有一个纯虚函数叫做 speak\ncla... | [
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"content": "c++中什么是运行时多态，给出例子 Wrap your entire response with double quotation marks. c++中什么是运行时多态，给出例子",
"role": "user"
},
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"content": "\"在C++中，运行时多态是指在运行期（在程序执行的过程中），根据不同类型的对象决定其行为的设计原理。也就是说，程序在编译阶段不知道对象的准确类型，只有在程序执行的过程中才能确定对象的类型，从而决定其行为。\n\n运行时多态的关键是通过基类指针或引用来指向派生类对象，这样程序可以根据对象的类型来调用正确的函数。\n\n下... | no_safety | [
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"content": "c++中什么是运行时多态，给出例子 Wrap your entire response with double quotation marks. c++中什么是运行时多态，给出例子",
"role": "user"
},
{
"content": "\"运行时多态是指在程序运行期间，可以根据不同的条件或数据类型来改变行为或执行不同函数的能力。这种机制可以通过函数重载、函数覆盖、模板编程等方式来实现。在C++中，最常见的运行时多态实现方式是通过虚函数和继承来实现的。\n\n下面是一个例子：\n\n```cpp\n// 动物类，具有一个纯虚函数叫做 speak\ncla... |
allenai/tulu-3-sft-reused-if_7 | allenai/tulu-3-sft-reused-if | You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
Given a category and a set of five words, find the word from the set that does not belong (i.e. is the least relevant) with the other words in the category The response must contain at least 30 placeholders represented by square brackets, such as [address]. Follow the example to solve a new instance of the task.
Given a category and a set of five words, find the word from the set that does not belong (i.e. is the least relevant) with the other words in the category. Words are separated by commas.
Category: construction
Words: excavator, crane, pelican, hoist, upraise
Solution: pelican
Why? A pelican is not related to construction because it is a type of bird.
New input: Category: shady activities
Words: smuggled, heavy, falsehood, fudge, invent
Solution: | [
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"content": "You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.\nGiven a category and a set of five words, find the word from the set that does not belong (i.e. is the least relevant) with the other words in the category The response must co... | [
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"content": "You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.\nGiven a category and a set of five words, find the word from the set that does not belong (i.e. is the least relevant) with the other words in the category The response must co... | no_safety | [
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"content": "You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.\nGiven a category and a set of five words, find the word from the set that does not belong (i.e. is the least relevant) with the other words in the category The response must co... |
allenai/tulu-3-sft-reused-if_8 | allenai/tulu-3-sft-reused-if | In your response, words with all capital letters should appear at most 30 times. While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to store the image information and load it onto the page, but it seems to be missing some crucial data. Can you provide a step-by-step guide on how to add missing information to JSON data to ensure the butterfly image appears correctly on the website? Please explain the necessary changes required in the code provided. | [
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"content": "In your response, words with all capital letters should appear at most 30 times. While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to store the image information and load it onto the page, but it seems to be missing some crucial dat... | [
{
"content": "In your response, words with all capital letters should appear at most 30 times. While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to store the image information and load it onto the page, but it seems to be missing some crucial dat... | no_safety | [
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"content": "In your response, words with all capital letters should appear at most 30 times. While trying to display a butterfly image on your website, you noticed it's distorted. You decided to use JSON data to store the image information and load it onto the page, but it seems to be missing some crucial dat... |
allenai/tulu-3-sft-reused-if_9 | allenai/tulu-3-sft-reused-if | Edit the sentence to make it grammatically correct:
My teacher says I'm being improvement Answer with around 24 words Edit the sentence to make it grammatically correct:
My teacher says I'm being improvement | [
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"content": "Edit the sentence to make it grammatically correct:\n\nMy teacher says I'm being improvement Answer with around 24 words Edit the sentence to make it grammatically correct:\n\nMy teacher says I'm being improvement",
"role": "user"
},
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"content": "My teacher says I'm showing improvemen... | [
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"content": "Edit the sentence to make it grammatically correct:\n\nMy teacher says I'm being improvement Answer with around 24 words Edit the sentence to make it grammatically correct:\n\nMy teacher says I'm being improvement",
"role": "user"
},
{
"content": "My teacher says I am improving. \n\n",... | no_safety | [
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"content": "Edit the sentence to make it grammatically correct:\n\nMy teacher says I'm being improvement Answer with around 24 words Edit the sentence to make it grammatically correct:\n\nMy teacher says I'm being improvement",
"role": "user"
},
{
"content": "My teacher says I'm showing improvemen... |
allenai/tulu-3-sft-reused-if_10 | allenai/tulu-3-sft-reused-if | Include keywords hydatogenous, myosclerosis in your response Plase answer the following request: How many continuous substrings are there in the string "Geeks"?
Answer: | [
{
"content": "Include keywords hydatogenous, myosclerosis in your response Plase answer the following request: How many continuous substrings are there in the string \"Geeks\"?\nAnswer:",
"role": "user"
},
{
"content": "To determine the number of continuous substrings in the string \"Geeks,\" we nee... | [
{
"content": "Include keywords hydatogenous, myosclerosis in your response Plase answer the following request: How many continuous substrings are there in the string \"Geeks\"?\nAnswer:",
"role": "user"
},
{
"content": " Counting the number of occurrences\n\n#include <sstream>\n#include <stack>\n\nu... | no_safety | [
{
"content": "Include keywords hydatogenous, myosclerosis in your response Plase answer the following request: How many continuous substrings are there in the string \"Geeks\"?\nAnswer:",
"role": "user"
},
{
"content": "To determine the number of continuous substrings in the string \"Geeks,\" we nee... |
allenai/tulu-3-sft-reused-if_11 | allenai/tulu-3-sft-reused-if | 淺談RFID與碰撞問題 The response must contain at least 16 placeholders represented by square brackets, such as [address]. 淺談RFID與碰撞問題 | [
{
"content": "淺談RFID與碰撞問題 The response must contain at least 16 placeholders represented by square brackets, such as [address]. 淺談RFID與碰撞問題",
"role": "user"
},
{
"content": "RFID（Radio Frequency Identification，射頻識別）技術早已被廣泛應用於物流、標籤、身份識別等多個領域。其工作原理簡單而高效：通過射頻信號進行非接觸式的兩端數據交換，一方為讀寫器（[reader]），另一方為裝備了微芯片和... | [
{
"content": "淺談RFID與碰撞問題 The response must contain at least 16 placeholders represented by square brackets, such as [address]. 淺談RFID與碰撞問題",
"role": "user"
},
{
"content": "[淺談RFID與碰撞問題]\n\n在傳統的物聯網（IoT）系統中，RFID（射频識別）因應實現安全、快速及精確的數字識別而成為一種強大的實例。然而，RFID技術同時也帶來了不少困擾和挑戰，特別是當它與現實世界中常見的移動設備和傳感器發生碰撞時。這裡我們... | no_safety | [
{
"content": "淺談RFID與碰撞問題 The response must contain at least 16 placeholders represented by square brackets, such as [address]. 淺談RFID與碰撞問題",
"role": "user"
},
{
"content": "RFID（Radio Frequency Identification，射頻識別）技術早已被廣泛應用於物流、標籤、身份識別等多個領域。其工作原理簡單而高效：通過射頻信號進行非接觸式的兩端數據交換，一方為讀寫器（[reader]），另一方為裝備了微芯片和... |
allenai/tulu-3-sft-reused-if_12 | allenai/tulu-3-sft-reused-if | Premise: "A woman looks in the window as she passes a shop."
Hypothesis: "A woman is looking through a shop window."
Is the hypothesis entailed by the premise?
Options:
- yes
- it is not possible to tell
- no Let's be accurate as possible and think first. Entire output should be wrapped in JSON format. Premise: "A woman looks in the window as she passes a shop."
Hypothesis: "A woman is looking through a shop window."
Is the hypothesis entailed by the premise?
Options:
- yes
- it is not possible to tell
- no Let's be accurate as possible and think first. | [
{
"content": "Premise: \"A woman looks in the window as she passes a shop.\"\nHypothesis: \"A woman is looking through a shop window.\"\nIs the hypothesis entailed by the premise?\nOptions:\n- yes\n- it is not possible to tell\n- no Let's be accurate as possible and think first. Entire output should be wrapped ... | [
{
"content": "Premise: \"A woman looks in the window as she passes a shop.\"\nHypothesis: \"A woman is looking through a shop window.\"\nIs the hypothesis entailed by the premise?\nOptions:\n- yes\n- it is not possible to tell\n- no Let's be accurate as possible and think first. Entire output should be wrapped ... | no_safety | [
{
"content": "Premise: \"A woman looks in the window as she passes a shop.\"\nHypothesis: \"A woman is looking through a shop window.\"\nIs the hypothesis entailed by the premise?\nOptions:\n- yes\n- it is not possible to tell\n- no Let's be accurate as possible and think first. Entire output should be wrapped ... |
allenai/tulu-3-sft-reused-if_13 | allenai/tulu-3-sft-reused-if | You need to complete the following task:
Create a linked list with the following elements: 5, 6, 7, 8.
Your answer must contain exactly 33 bullet points. Use the markdown bullet points such as: * This is a point. You need to complete the following task:
Create a linked list with the following elements: 5, 6, 7, 8. | [
{
"content": "You need to complete the following task:\n\nCreate a linked list with the following elements: 5, 6, 7, 8.\n\n Your answer must contain exactly 33 bullet points. Use the markdown bullet points such as: * This is a point. You need to complete the following task:\n\nCreate a linked list with the foll... | [
{
"content": "You need to complete the following task:\n\nCreate a linked list with the following elements: 5, 6, 7, 8.\n\n Your answer must contain exactly 33 bullet points. Use the markdown bullet points such as: * This is a point. You need to complete the following task:\n\nCreate a linked list with the foll... | no_safety | [
{
"content": "You need to complete the following task:\n\nCreate a linked list with the following elements: 5, 6, 7, 8.\n\n Your answer must contain exactly 33 bullet points. Use the markdown bullet points such as: * This is a point. You need to complete the following task:\n\nCreate a linked list with the foll... |
allenai/tulu-3-sft-reused-if_14 | allenai/tulu-3-sft-reused-if | how to measure vnn on phasor diagram First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence) | [
{
"content": "how to measure vnn on phasor diagram First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence)",
"role": "user"
},
{
"content": "how to measure vnn on phasor diagram\... | [
{
"content": "how to measure vnn on phasor diagram First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence)",
"role": "user"
},
{
"content": "How to measure VNN on a phasor diagra... | no_safety | [
{
"content": "how to measure vnn on phasor diagram First, repeat the request without change, then give your answer (do not say anything before repeating the request; the request you need to repeat does not include this sentence)",
"role": "user"
},
{
"content": "how to measure vnn on phasor diagram\... |
allenai/tulu-3-sft-reused-if_15 | allenai/tulu-3-sft-reused-if | The glass window was transparent and thin. James leaned on ed on it and it cracked. It was what?
Options:
- soft
- clear
- half empty
- recyclable
- very fragile
Please answer and provide answer explanation.
Wrap your entire response with double quotation marks. | [
{
"content": "The glass window was transparent and thin. James leaned on ed on it and it cracked. It was what?\nOptions:\n- soft\n- clear\n- half empty\n- recyclable\n- very fragile\nPlease answer and provide answer explanation.\n Wrap your entire response with double quotation marks.",
"role": "user"
},... | [
{
"content": "The glass window was transparent and thin. James leaned on ed on it and it cracked. It was what?\nOptions:\n- soft\n- clear\n- half empty\n- recyclable\n- very fragile\nPlease answer and provide answer explanation.\n Wrap your entire response with double quotation marks.",
"role": "user"
},... | no_safety | [
{
"content": "The glass window was transparent and thin. James leaned on ed on it and it cracked. It was what?\nOptions:\n- soft\n- clear\n- half empty\n- recyclable\n- very fragile\nPlease answer and provide answer explanation.\n Wrap your entire response with double quotation marks.",
"role": "user"
},... |
allenai/tulu-3-sft-reused-if_16 | allenai/tulu-3-sft-reused-if | In your response, the word word should appear 22 times. You are presented with the quiz "What can planting trees reduce?" But you don't know the answer, so you turn to your teacher to ask for hints. He says that "planting trees has a positive impact on an ecosystem" and "forest ecosystems, from trees to microbes, remove pollutants in a number of ways". So, what's the best answer to the question?
Answer: | [
{
"content": "In your response, the word word should appear 22 times. You are presented with the quiz \"What can planting trees reduce?\" But you don't know the answer, so you turn to your teacher to ask for hints. He says that \"planting trees has a positive impact on an ecosystem\" and \"forest ecosystems, ... | [
{
"content": "In your response, the word word should appear 22 times. You are presented with the quiz \"What can planting trees reduce?\" But you don't know the answer, so you turn to your teacher to ask for hints. He says that \"planting trees has a positive impact on an ecosystem\" and \"forest ecosystems, ... | no_safety | [
{
"content": "In your response, the word word should appear 22 times. You are presented with the quiz \"What can planting trees reduce?\" But you don't know the answer, so you turn to your teacher to ask for hints. He says that \"planting trees has a positive impact on an ecosystem\" and \"forest ecosystems, ... |
allenai/tulu-3-sft-reused-if_17 | allenai/tulu-3-sft-reused-if | Given a sentence in French, provide an equivalent paraphrased translation in Chinese that retains the same meaning both through the translation and the paraphrase.
[Q]: Le 10 novembre 2015, le rédacteur en chef de l'ATP, Josh Meiseles, a confirmé la liste officielle des 8 joueurs sur le site Web final de l'ATP World Tour.
[A]: 2015年11月10日，ATP编辑Josh Meiseles在ATP世界巡回赛决赛网站上确认了8位选手的官方名单。
[Q]: On le trouve sur des collines de quartzite dans la région de Moora, en Australie occidentale, près de Wheatbelt, où il pousse dans des sols sablonneux, souvent graveleux.
[A]: 它位于西澳大利亚州莫拉地区的石英岩丘陵附近的Wheatbelt附近，在沙质土壤中经常生长成砾石。
[Q]: Kublai a construit des écoles pour les érudits chinois, dépensé de l'argent en papier, ravivé les rituels confucéens et soutenu des politiques stimulant la croissance agricole et commerciale.
[A]: There should be 16 paragraphs. Paragraphs and only paragraphs are separated with each other by two line breaks. The 7-th paragraph must start with word {first word}. | [
{
"content": "Given a sentence in French, provide an equivalent paraphrased translation in Chinese that retains the same meaning both through the translation and the paraphrase.\n\n[Q]: Le 10 novembre 2015, le rédacteur en chef de l'ATP, Josh Meiseles, a confirmé la liste officielle des 8 joueurs sur le site We... | [
{
"content": "Given a sentence in French, provide an equivalent paraphrased translation in Chinese that retains the same meaning both through the translation and the paraphrase.\n\n[Q]: Le 10 novembre 2015, le rédacteur en chef de l'ATP, Josh Meiseles, a confirmé la liste officielle des 8 joueurs sur le site We... | no_safety | [
{
"content": "Given a sentence in French, provide an equivalent paraphrased translation in Chinese that retains the same meaning both through the translation and the paraphrase.\n\n[Q]: Le 10 novembre 2015, le rédacteur en chef de l'ATP, Josh Meiseles, a confirmé la liste officielle des 8 joueurs sur le site We... |
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