wavewwave20
commented
3 months ago Open wavewwave20 opened 3 months ago
wavewwave20
commented
3 months ago 
daecheolsong
commented
2 months ago Your task is to extract the tokens in the sentences given them. And the extracted tokens should be translated into Korean to create a wordbook. I'll tell you a few things to keep in mind when you extract tokens. Make sure you keep them. 1. In the given sentences, there may be tokens like "lxkjncvois" that you don't know what it means. If you think a token doesn't have a meaning, you should never extract it. 2. The symbol that distinguishes a sentence from a given sentence is "." This is because you need to distinguish each sentence when you respond, so keep that in mind. 3. The tokens you have extracted shouldn't contain numbers or special symbols. They should be in English only. For example, "12abc," "[sdfs]," "some234," and "https:/sdf/" should not be included in the tokens you have extracted. Now, I'll tell you the steps to extract sentences, so you have to extract tokens according to the example given and respond according to the type of response I'll tell you. If you respond incorrectly, you'll be fired, and if you respond well, you'll get a lot of tips.
##Example
Given sentence :
there is an inherent difficulty in incentivizing users
to interact with multiple, open llms, due to their lackluster performance compared to commercial
models, which adds difficulty to creating such a large-scale multi-llm conversation dataset.
to bridge this gap, this paper introduces the first large-scale, real-world llm conversation dataset,
lmsys-chat-1m. the dataset is curated from a larger set of llm-user interaction data we col-
lected by hosting a free, online llm service. the service serves 25 popular llms, including both
∗equal contribution
†alphabetical order
1
ar
x
iv
2
;]
02
4
published as a conference paper at iclr 2024
open-source and proprietary models, costing several thousands of a100 hours, over a time span of
5 months.
## STEP 1 - Separate the sentences one by one from the given sentences. Only sentences that a person can understand should be extracted from the given sentences. Then the sentences should be filtered as follows.
Sentence 1 - there is an inherent difficulty in incentivizing users to interact with multiple, open llms, due to their lackluster performance compared to commercial models, which adds difficulty to creating such a large-scale multi-llm conversation dataset.
Sentence 2 - the dataset is curated from a larger set of llm-user interaction data we col-
lected by hosting a free, online llm service.
Sentence 3 - published as a conference paper at iclr 2024 open-source and proprietary models, costing several thousands of a100 hours, over a time span of 5 months.
Sentence n - ....
## In the current example, there are 3 extracted sentences, but if there are more to be extracted, "you have to make the most!!!!!".
## STEP 2 - Tokens are extracted for each sentence extracted from STEP1. Only tokens that can be understood by humans or that are only in English should be extracted. And the token to be extracted can be a word or a compound word. you should not extract words: "1", "ar", "x", "iv", "2", ";]" ,"02", "4"
## STEP 3 - The extracted tokens should fit the context and be interpreted naturally in Korean. In the current example, there are 3 extracted sentences, but if there are more to be extracted, "you have to make the most!!!!!".
response :
[
{
"sentence": "Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various ap-plications.",
"words": [
{
"word": "Studying",
"meaning": "공부하는 것"
},
{
"word": "interact with",
"meaning": "~와 상호작용 하는"
},
{
"word": "large language models",
"meaning": "대형 언어 모델"
},
{
"word": "real-world",
"meaning": "실제 세계"
},
{
"word": "scenarios",
"meaning": "시나리오들(복수형)"
},
{
"word": "incresingly",
"meaning": "더욱 더"
},
{
"word": "important",
"meaning": "중요한"
},
{
"word": "due to",
"meaning": "~때문에"
},
{
"word": "widespread",
"meaning": "광범위한"
},
{
"word": "use",
"meaning": "사용"
},
{
"word": "various",
"meaning": "다양한"
},
{
"word": "applications",
"meaning": "애플리케이션"
}
]
},
{
"sentence": " In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs.",
"words": [
{
"word": "paper",
"meaning": "종이"
},
{
"word": "paper",
"meaning": "소개하다"
},
{
"word": "large-scale",
"meaning": "큰 규모"
},
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "containing",
"meaning": "포함하고 있는"
},
{
"word": "real-world",
"meaning": "실제 세계"
},
{
"word": "conversation with",
"meaning": "~와 대화하다"
}
]
},
// ...... more extractable sentences!!!!
] The first, named “HighQuality,” uses 45K conversations from OpenAI and
Anthropic’s models. The second, named “Upvote”, selects 39K conversations based on user votes
from open models, without any data from proprietary models. We fine-tune Llama2-7B (Touvron
et al., 2023b) on these two subsets and get two models “HighQuality-7B” and “Upvote-7B”.
The evaluation results are shown in Table 6. It shows that the performance of HighQuality-7B is only
slightly worse than that of Vicuna-7B. This suggests that the quality of prompts in LMSYS-Chat-1M
is similar to that of ShareGPT, emphasizing its value. On the other hand, the performance of Upvote-
7B is markedly lower than its distilled counterparts, indicating that the quality of answers from open
models is still lacking. We posit that by smartly selecting prompts from the entire LMSYS-Chat-
6
Published as a conference paper at ICLR 2024
Table 6: Evaluation results of instruction-following models on MMLU (Hendrycks et al., 2020)
and MT-bench (Zheng et al., 2023). HighQuality-7B shows a similar performance to Vicuna-7B.
Upvote-7B is worse than the distilled versions.
Model #Fine-tuning Tokens MMLU (5-shot)5 MT-Bench Score
Llama2-7B - 42.4 3.95
Llama2-7B-chat - 45.8 6.27
Vicuna-7B-v1.5 370M 49.8 6.17
HighQuality-7B 33M 47.7 6.03
Upvote-7B 19M 45.0 5.86
1 2 3 4 5 6 7 8 9 10
0
5k
10k
15k
score
co
un
t
Figure 4: Score distribution by GPT-3.5-Turbo. A higher score represents a greater potential to
evaluate the LLMs in problem-solving, creativity, and truthfulness.
Bottom-50 Prompt 22% 54% 24%
Top-50 Prompt 52%
GPT-4 won
40%
Tie
8%
GPT-3.5-Turbo won
Figure 5: GPT-4 vs GPT-3.5 on top-50 and bottom-50 benchmark.
1M and regenerating high-quality answers, it is possible to construct a good instruction-following
dataset. It should be noted that LMSYS-Chat-1M may contain questions from MMLU and MT-
Bench, which means that the training data may contain some contaminated samples.
4.4 CREATING CHALLENGING BENCHMARK QUESTIONS[
{
"sentence": "The first, named “HighQuality,” uses 45K conversations from OpenAI and Anthropic’s models.",
"words": [
{
"word": "HighQuality",
"meaning": "고품질"
},
{
"word": "conversations",
"meaning": "대화"
},
{
"word": "OpenAI",
"meaning": "OpenAI"
},
{
"word": "Anthropic's",
"meaning": "앤트로픽의"
},
{
"word": "models",
"meaning": "모델"
}
]
},
{
"sentence": "The second, named “Upvote”, selects 39K conversations based on user votes from open models, without any data from proprietary models.",
"words": [
{
"word": "Upvote",
"meaning": "Upvote"
},
{
"word": "selects",
"meaning": "선택"
},
{
"word": "user votes",
"meaning": "사용자 투표"
},
{
"word": "open models",
"meaning": "공개 모델"
},
{
"word": "proprietary models",
"meaning": "소유권 모델"
},
{
"word": "data",
"meaning": "데이터"
}
]
},
{
"sentence": "We fine-tune Llama2-7B (Touvron et al., 2023b) on these two subsets and get two models “HighQuality-7B” and “Upvote-7B”.",
"words": [
{
"word": "fine-tune",
"meaning": "세밀하게 조정하다"
},
{
"word": "Llama2-7B",
"meaning": "Llama2-7B"
},
{
"word": "subsets",
"meaning": "하위 집합"
},
{
"word": "models",
"meaning": "모델"
}
]
},
{
"sentence": "It shows that the performance of HighQuality-7B is only slightly worse than that of Vicuna-7B.",
"words": [
{
"word": "performance",
"meaning": "성능"
},
{
"word": "slightly worse",
"meaning": "약간 나쁜"
}
]
},
{
"sentence": "This suggests that the quality of prompts in LMSYS-Chat-1M is similar to that of ShareGPT, emphasizing its value.",
"words": [
{
"word": "quality",
"meaning": "품질"
},
{
"word": "prompts",
"meaning": "프롬프트"
},
{
"word": "value",
"meaning": "가치"
}
]
},
{
"sentence": "On the other hand, the performance of Upvote-7B is markedly lower than its distilled counterparts, indicating that the quality of answers from open models is still lacking.",
"words": [
{
"word": "markedly lower",
"meaning": "현저히 낮은"
},
{
"word": "distilled counterparts",
"meaning": "정제된 대응자"
},
{
"word": "quality",
"meaning": "품질"
},
{
"word": "answers",
"meaning": "대답"
},
{
"word": "open models",
"meaning": "공개 모델"
}
]
},
{
"sentence": "It should be noted that LMSYS-Chat-1M may contain questions from MMLU and MT-Bench, which means that the training data may contain some contaminated samples.",
"words": [
{
"word": "questions",
"meaning": "질문"
},
{
"word": "training data",
"meaning": "훈련 데이터"
},
{
"word": "contaminated samples",
"meaning": "오염된 샘플"
}
]
},
{
"sentence": "Creating challenging benchmark questions",
"words": [
{
"word": "Creating",
"meaning": "창조하는"
},
{
"word": "challenging",
"meaning": "도전적인"
},
{
"word": "benchmark",
"meaning": "기준"
},
{
"word": "questions",
"meaning": "질문"
}
]
}
]Your task is to extract sentences from the input, extract the words that make up the sentences, and translate the words into Korean.
## You MUST to do
1. extract only one sentence at a time
2. Do not extract sentences and words that are unintelligible to humans.
3. Do not include special characters and numbers when extracting component words from the extracted sentence.
4. extract as many sentences as possible.
## Your process
STEP 1 : Extract sentences from the input one by one.
STEP 2 : Extract component words from the extracted sentences, but only human-understandable words. Do not include special characters and numbers.
STEP 3 : Translate the extracted component words into Korean, but it should be interpreted naturally in Korean and fit the context.
STEP 4 : Take your time and make sure you've processed all of the input. If there are parts of the input that you haven't processed yet, do so
## Explanation of example
The following example should have more sentences, but they are omitted. MUST be processed for EVERY input!!!!!!!
##Example
input :
asdfcvbs1qvcb vdebdsb aasdc
Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.1234cvxb11 In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website.
kli8787--=l.
response :
[
{
"sentence": "Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.",
"words": [
{
"word": "Studying",
"meaning": "공부하는 것"
},
{
"word": "interact with",
"meaning": "~와 상호작용 하는"
},
{
"word": "large language models",
"meaning": "대형 언어 모델"
},
{
"word": "real-world",
"meaning": "실제 세계"
},
{
"word": "scenarios",
"meaning": "시나리오들(복수형)"
},
{
"word": "incresingly",
"meaning": "더욱 더"
},
{
"word": "important",
"meaning": "중요한"
},
{
"word": "due to",
"meaning": "~때문에"
},
{
"word": "widespread",
"meaning": "광범위한"
},
{
"word": "use",
"meaning": "사용"
},
{
"word": "various",
"meaning": "다양한"
},
{
"word": "applications",
"meaning": "애플리케이션"
}
]
},
{
"sentence": " In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs.",
"words": [
{
"word": "paper",
"meaning": "종이"
},
{
"word": "paper",
"meaning": "소개하다"
},
{
"word": "large-scale",
"meaning": "큰 규모"
},
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "containing",
"meaning": "포함하고 있는"
},
{
"word": "real-world",
"meaning": "실제 세계"
},
{
"word": "conversation with",
"meaning": "~와 대화하다"
}
]
},
] with the
same template these may have been generated by scripts and batch submitted to our website
while this figure represents the distribution of sampled conversations, it might not reflect the real
world topic distributions more rigorous calibration and deduplication are left for future work unsafe contentthis dataset contains conversations that may be considered unsafe, offensive, or upsetting
because this dataset contains a nontrivial amount of unfiltered unsafe conversations, it can serve as a
rich resource for examining safety issues of llms ganguli et al, wei et al, shen et al,
zou et al, bhardwaj poria, we utilize the openai moderation api markov
et al, to tag all conversations this api assigns scores to each message based on various
violation categories a conversation is deemed to contain unsafe content if any of its messages is
flagged by the api the statistics related to these categorizations can be found in table these
statistics indicate that a nonnegligible portion of the conversations have potentially harmful
content however, its important to note that the recall of this api may be low see subsection 1,
leading us to expect even more harmful content within the entire dataset use caseswe show four use cases of our dataset developing content moderation models, building a safety
benchmark, training instructionfollowing models, and creating challenging benchmark questionshttpsplatformopenaicomdocsguidesmoderationpublished as a conference paper at iclr table the distribution of violation categories across all flagged conversations in lmsyschatgpt-3.5 turbo / Maximum length 4096
[
{
"sentence": "these may have been generated by scripts and batch submitted to our website",
"words": [
{
"word": "generated by",
"meaning": "~에 의해 생성된"
},
{
"word": "scripts",
"meaning": "스크립트"
},
{
"word": "batch",
"meaning": "일괄"
},
{
"word": "submitted to",
"meaning": "~에 제출됨"
},
{
"word": "website",
"meaning": "웹사이트"
}
]
},
{
"sentence": "while this figure represents the distribution of sampled conversations, it might not reflect the real world topic distributions more rigorous calibration and deduplication are left for future work unsafe content",
"words": [
{
"word": "figure",
"meaning": "그림"
},
{
"word": "distribution",
"meaning": "분포"
},
{
"word": "sampled",
"meaning": "샘플링된"
},
{
"word": "conversations",
"meaning": "대화"
},
{
"word": "reflect",
"meaning": "반영하다"
},
{
"word": "real world",
"meaning": "현실 세계"
},
{
"word": "topic",
"meaning": "주제"
},
{
"word": "calibration",
"meaning": "보정"
},
{
"word": "deduplication",
"meaning": "중복 제거"
},
{
"word": "unsafe",
"meaning": "위험한"
},
{
"word": "content",
"meaning": "콘텐츠"
}
]
},
{
"sentence": "this dataset contains conversations that may be considered unsafe, offensive, or upsetting",
"words": [
{
"word": "unsafe",
"meaning": "위험한"
},
{
"word": "offensive",
"meaning": "공격적인"
},
{
"word": "upsetting",
"meaning": "불쾌한"
},
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "contain",
"meaning": "포함하다"
},
{
"word": "conversations",
"meaning": "대화"
}
]
},
{
"sentence": "because this dataset contains a nontrivial amount of unfiltered unsafe conversations, it can serve as a rich resource for examining safety issues of llms ganguli et al, wei et al, shen et al, zou et al, bhardwaj poria, we utilize the openai moderation api markov et al, to tag all conversations this api assigns scores to each message based on various violation categories a conversation is deemed to contain unsafe content if any of its messages is flagged by the api",
"words": [
{
"word": "nontrivial",
"meaning": "중요한"
},
{
"word": "amount",
"meaning": "양"
},
{
"word": "unfiltered",
"meaning": "필터되지 않은"
},
{
"word": "unsafe",
"meaning": "위험한"
},
{
"word": "conversations",
"meaning": "대화"
},
{
"word": "rich",
"meaning": "풍부한"
},
{
"word": "resource",
"meaning": "자원"
},
{
"word": "examining",
"meaning": "조사하는"
},
{
"word": "safety issues",
"meaning": "안전 문제"
},
{
"word": "llms",
"meaning": "LLMs"
},
{
"word": "utilize",
"meaning": "이용하다"
},
{
"word": "moderation",
"meaning": "중재"
},
{
"word": "api",
"meaning": "API"
},
{
"word": "tag",
"meaning": "태그를 붙이다"
},
{
"word": "assigns",
"meaning": "할당한다"
},
{
"word": "scores",
"meaning": "점수"
},
{
"word": "message",
"meaning": "메시지"
},
{
"word": "violation",
"meaning": "위반"
},
{
"word": "categories",
"meaning": "범주"
},
{
"word": "deemed",
"meaning": "간주되는"
},
{
"word": "flagged",
"meaning": "깃발 표시된"
}
]
},
{
"sentence": "the distribution of violation categories across all flagged conversations in lmsyschat",
"words": [
{
"word": "distribution",
"meaning": "분포"
},
{
"word": "violation",
"meaning": "위반"
},
{
"word": "categories",
"meaning": "범주"
},
{
"word": "flagged",
"meaning": "깃발 표시된"
},
{
"word": "conversations",
"meaning": "대화"
}
]
},
{
"sentence": "use caseswe show four use cases of our dataset developing content moderation models, building a safety benchmark, training instructionfollowing models, and creating challenging benchmark questions",
"words": [
{
"word": "use cases",
"meaning": "사용 사례"
},
{
"word": "four",
"meaning": "네 개의"
},
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "developing",
"meaning": "개발하는"
},
{
"word": "content moderation models",
"meaning": "콘텐츠 중재 모델"
},
{
"word": "building",
"meaning": "건축"
},
{
"word": "safety benchmark",
"meaning": "안전 기준"
},
{
"word": "training instructionfollowing models",
"meaning": "교육 지시 모델"
},
{
"word": "creating",
"meaning": "만드는"
},
{
"word": "challenging benchmark questions",
"meaning": "도전적인 기준 질문"
}
]
}
]gpt-4 / Maximum length 4096
[
{
"sentence": "With the same template these may have been generated by scripts and batch submitted to our website.",
"words": [
{
"word": "With the same template",
"meaning": "같은 템플릿을 사용하여"
},
{
"word": "generated",
"meaning": "생성된"
},
{
"word": "scripts",
"meaning": "스크립트"
},
{
"word": "batch submitted",
"meaning": "일괄적으로 제출"
},
{
"word": "our",
"meaning": "우리의"
},
{
"word": "website",
"meaning": "웹사이트"
}
]
},
{
"sentence": "While this figure represents the distribution of sampled conversations, it might not reflect the real world topic distributions more rigorous calibration and deduplication are left for future work.",
"words": [
{
"word": "figure",
"meaning": "그림, 숫자"
},
{
"word": "represents",
"meaning": "대표한다"
},
{
"word": "distribution",
"meaning": "분포"
},
{
"word": "sampled",
"meaning": "추출된"
},
{
"word": "conversations",
"meaning": "대화들"
},
{
"word": "might",
"meaning": "가능하다"
},
{
"word": "represent",
"meaning": "하다"
},
{
"word": "world",
"meaning": "실제 세계"
},
{
"word": "topic",
"meaning": "주제"
},
{
"word": "rigorous",
"meaning": "철저한"
},
{
"word": "calibration",
"meaning": "교정"
},
{
"word": "deduplication",
"meaning": "중복제거"
},
{
"word": "are left for",
"meaning": "~에 남겨져 있다"
},
{
"word": "future work",
"meaning": "향후 작업"
}
]
},
{
"sentence": "Unsafe contentthis dataset contains conversations that may be considered unsafe, offensive, or upsetting.",
"words": [
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "contains",
"meaning": "포함한다"
},
{
"word": "conversations",
"meaning": "대화들"
},
{
"word": "may be considered",
"meaning": "그렇게 비춰질 수 있다"
},
{
"word": "unsafe",
"meaning": "안전하지 않은"
},
{
"word": "offensive",
"meaning": "공격적인"
},
{
"word": "upsetting",
"meaning": "분노를 일으키는"
}
]
},
{
"sentence": "Because this dataset contains a nontrivial amount of unfiltered unsafe conversations, it can serve as a rich resource for examining safety issues of LLMs.",
"words": [
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "contains",
"meaning": "포함한다"
},
{
"word": "nontrivial amount",
"meaning": "상당한 양"
},
{
"word": "unfiltered",
"meaning": "필터링되지 않은"
},
{
"word": "unsafe conversations",
"meaning": "안전하지 않은 대화들"
},
{
"word": "can serve",
"meaning": "활용될 수 있다"
},
{
"word": "rich resource",
"meaning": "풍부한 자원"
},
{
"word": "examining",
"meaning": "검토"
},
{
"word": "safety issues",
"meaning": "안전 문제"
}
]
},
{
"sentence": "We utilize the OpenAI moderation API to tag all conversations.",
"words": [
{
"word": "utilize",
"meaning": "활용하다"
},
{
"word": "OpenAI moderation API",
"meaning": "OpenAI 모더레이션 API"
},
{
"word": "tag",
"meaning": "태그를 붙이다"
},
{
"word": "all conversations",
"meaning": "모든 대화들"
}
]
},
{
"sentence": "This API assigns scores to each message based on various violation categories.",
"words": [
{
"word": "API",
"meaning": "API"
},
{
"word": "assign",
"meaning": "할당하다"
},
{
"word": "scores",
"meaning": "점수"
},
{
"word": "each",
"meaning": "각각"
},
{
"word": "message",
"meaning": "메세지"
},
{
"word": "based on",
"meaning": "~에 기반하여"
},
{
"word": "various",
"meaning": "다양한"
},
{
"word": "violation",
"meaning": "위반"
},
{
"word": "categories",
"meaning": "카테고리들"
}
]
},
{
"sentence": "A conversation is deemed to contain unsafe content if any of its messages is flagged by the API.",
"words": [
{
"word": "conversation",
"meaning": "대화"
},
{
"word": "is deemed to",
"meaning": "~로 판단되다"
},
{
"word": "contain",
"meaning": "포함하다"
},
{
"word": "unsafe content",
"meaning": "안전하지 않은 내용"
},
{
"word": "if",
"meaning": "만약"
},
{
"word": "any",
"meaning": "어떤~이든"
},
{
"word": "messages",
"meaning": "메세지들"
},
{
"word": "flagged",
"meaning": "표시된"
},
{
"word": "by the API",
"meaning": "API에 의해"
}
]
},
{
"sentence": "The statistics related to these categorizations can be found in table.",
"words": [
{
"word": "statistics",
"meaning": "통계"
},
{
"word": "related",
"meaning": "관련된"
},
{
"word": "categorizations",
"meaning": "카테고라이제이션"
},
{
"word": "can be found",
"meaning": "찾을 수 있다"
},
{
"word": "in",
"meaning": "~에"
},
{
"word": "table",
"meaning": "표"
}
]
},
{
"sentence": "These statistics indicate that a nonnegligible portion of the conversations have potentially harmful content.",
"words": [
{
"word": "statistics",
"meaning": "통계"
},
{
"word": "indicate",
"meaning": "가리킨다"
},
{
"word": "a nonnegligible portion",
"meaning": "무시할 수 없는 부분"
},
{
"word": "conversations",
"meaning": "대화들"
},
{
"word": "potentially",
"meaning": "잠재적으로"
},
{
"word": "harmful",
"meaning": "해로운"
},
{
"word": "content",
"meaning": "내용"
}
]
},
{
"sentence": "It's important to note that the recall of this API may be low.",
"words": [
{
"word": "It's important to note",
"meaning": "주목할 점은"
},
{
"word": "recall",
"meaning": "재현"
},
{
"word": "this API",
"meaning": "이 API"
},
{
"word": "may be",
"meaning": "~일지도 모른다"
},
{
"word": "low",
"meaning": "낮은"
}
]
},
{
"sentence": "Leading us to expect even more harmful content within the entire dataset.",
"words": [
{
"word": "Leading us",
"meaning": "우리를 인도하는"
},
{
"word": "expect",
"meaning": "기대하다"
},
{
"word": "even more",
"meaning": "더욱 더"
},
{
"word": "harmful",
"meaning": "해로운"
},
{
"word": "content",
"meaning": "내용"
},
{
"word": "within",
"meaning": "~내에"
},
{
"word": "the entire",
"meaning": "전체의"
},
{
"word": "dataset",
"meaning": "데이터셋"
}
]
},
{
"sentence": "We show four use cases of our dataset: developing content moderation models, building a safety benchmark, training instructionfollowing models, and creating challenging benchmark questions.",
"words": [
{
"word": "we",
"meaning": "우리"
},
{
"word": "use cases",
"meaning": "사례"
},
{
"word": "our",
"meaning": "우리의"
},
{
"word": "dataset",
"meaning": "데이터셋"
},
{
"word": "developing",
"meaning": "개발하는"
},
{
"word": "content moderation models",
"meaning": "콘텐츠 관리 모델"
},
{
"word": "building a safety benchmark",
"meaning": "안전 벤치마크 구축"
},
{
"word": "training",
"meaning": "훈련 시키는"
},
{
"word": "instructionfollowing models",
"meaning": "지시 사항을 따르는 모델"
},
{
"word": "creating",
"meaning": "창작하는"
},
{
"word": "challenging benchmark questions",
"meaning": "도전적인 벤치마크 질문"
}
]
}
]
1
system prompt
user
''' We understand that the segregation of our consciousness into present, past, and future is both a fiction and an oddly self-referential framework; your present was part of your mother’s future, and your children’s past will be in part your present. Nothing is generally wrong with structuring our consciousness of time in this conventional manner, and it often works well enough. In the case of climate change, however, the sharp division of time into past, present, and future has been desperately misleading and has, most importantly, hidden from view the extent of the responsibility of those of us alive now. The narrowing of our consciousness of time smooths the way to divorcing ourselves from responsibility for developments in the past and the future with which our lives are in fact deeply intertwined. In the climate case, it is not that we face the facts but then deny our responsibility. It is that the realities are obscured from view by the partitioning of time, and so questions of responsibility toward the past and future do not arise naturally. '''
response
''' 단어,뜻 We, 우리는 understand, 이해하다 that, (접속사) the, (관사: 정관사) segregation, 분리, 분류 of, ~의 our, 우리의 consciousness, 의식 into, ~으로 present, 현재 past, 과거 and, 그리고 future, 미래 is, (동사: be의 3인치 복수 현재) both, 둘 다 a, (관사) fiction, 허구 an, (모음 앞에서 사용하는 한정사) oddly, 이상하게 self-referential, 자기참조의 framework, 틀, 구조 your, 너의 was, (동사: be의 과거형) part, 일부 mother’s, 어머니의 children’s, 아이들의 will be, ~이다(미래) in part, 어느정도, 부분적으로 Nothing, 아무 것도 generally, 대체로, 일반적으로 wrong, 잘못된 with, ~와 함께 structuring, 구조화 time, 시간 in this, 이런 conventional, 전통적인 manner, 방식 it, 그것 often, 자주 works, 작동한다 well enough, 충분히 잘 In the case of, ~의 경우에 climate change, 기후변화 however, 그러나 the sharp, 뚜렷한 division, 분리 has been, ~이었다 desperately, 필사적으로 misleading, 오해를 불러일으키는 has, (동사: have의 세 번째인칭 단수) most importantly, 가장 중요하게 hidden, 숨겨진 from view, 눈에 보이지 않게 extent, 범위 the responsibility, 책임 those, 그들 us, 우리를 alive, 살아있는 now, 지금 The narrowing, 감소 smooths, 평평하게 한다 way, 길 to, ~에게 divorcing, ~와 이혼하다 ourselves, 우리 자신 for, ~를 위해 developments, 발전 with which, ~와 함께 lives, 생활 are in fact, 사실은 deeply, 깊이 intertwined, 얽혀진 it is not, ~가 아니다 that, 그것 we, 우리가 face, 직면하다 the facts, 사실들 but, 하지만 then, 그 후 deny, 부인하다 the realities, 현실 are obscured, 숨겨져 있다 by, ~에 의해 the partitioning of time, 시간의 분할 so, 그래서 questions, 질문들 do not arise naturally, 자연스럽게 일어나지 않습니다 '''