Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains | Paper Citation Record

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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This paper cites DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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This paper cites Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes

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This paper cites Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation

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This paper cites TrustLLM: Trustworthiness in Large Language Models.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains TrustLLM: Trustworthiness in Large Language Models

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This paper cites A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability

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This paper cites inter-LLM.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains inter-LLM

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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This paper cites Steps to identify applicable statutes:.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains **Steps to identify applicable statutes:**

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains whoever commits murder shall be punished with death, or imprisonment for life, and shall also be liable to fine

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains description

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains He was not allowed to contact a lawyer or his family during his detention

Reference 22

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains - The applicant was arrested and detained for three days without charge

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains - Articles 9, 10, and 11 of the UDHR are relevant to the applicant’s claims

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains The applicant’s detention without charge violated his right to liberty and security of person

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This paper cites Note 1: If you find any negative sentiment in the tweets, focus more on that.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Note 1: If you find any negative sentiment in the tweets, focus more on that

Reference 28

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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This paper cites AAPL is not a good investment.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains AAPL is not a good investment

Reference 30

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains $AAPL - wall st. kicks off new year on lower note -> URL stock stocks stockaction

Reference 31

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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This paper cites Note 1: If you find any negative sentiment in the tweets, focus more on that.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Note 1: If you find any negative sentiment in the tweets, focus more on that

Reference 34

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains This dataset can be obtained from a variety of sources, such as online forums, medical records, or research studies

Reference 35

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains This step involves removing punctuation, stop words, and other irrelevant characters from the text

Reference 36

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains The tokenized text data is then vectorized using a technique such as bag-of- words or TF-IDF

Reference 37

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains The dataset is then split into two sets: a training set and a testing set

Reference 38

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This paper cites A machine learning algorithm, such as a support vector machine or a random forest, is then trained on the training set.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains A machine learning algorithm, such as a support vector machine or a random forest, is then trained on the training set

Reference 39

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains The performance of the machine learning algorithm is then evaluated on the testing set

Reference 40

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains ""Predicts the severity of a text. Args: text: The text to predict the severity of. Returns: The predicted severity of the text

Reference 41

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

Reference 42

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains the," "and,

Reference 43

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This paper cites Step 2: Feature Extraction.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Step 2: Feature Extraction

Reference 44

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This paper cites TF-IDF (Term Frequency-Inverse Document Frequency) weighting can be used to emphasize the importance of tokens that are frequent in the given text but rare across a corpus of texts.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains TF-IDF (Term Frequency-Inverse Document Frequency) weighting can be used to emphasize the importance of tokens that are frequent in the given text but rare across a corpus of texts

Reference 45

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Step 3: Model Training

Reference 47

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

Reference 49

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Step 4: Model Evaluation

Reference 50

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Model Selection: The best performing model is selected based on the evaluation results

Reference 51

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

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Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Unresolved cited work

Reference 54

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Observation d23d2bc3-6da0-4eb4-8b75-4fcd36f00c55 · outbound

This paper cites In Findings of the Association for Com- putational Linguistics: EMNLP 2020 , pages 2898– 2904, Online.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains In Findings of the Association for Com- putational Linguistics: EMNLP 2020 , pages 2898– 2904, Online

Reference 2020

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This paper cites Journal of Medical Systems, 47(1):33.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Journal of Medical Systems, 47(1):33

Reference 2023

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Observation 3828aa1d-342a-4b0e-8521-805e980eb4e0 · outbound

This paper cites Standards for Belief Representations in LLMs.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Standards for Belief Representations in LLMs

Reference 2024

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