ICSFSurvey
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Icsfsurvey
Overview :
ICSFSurvey is a research study investigating the internal consistency and self-feedback of large language models. It offers a cohesive perspective on the self-assessment and self-updating mechanisms of LLMs, encompassing theoretical frameworks, systematic classifications, evaluation methods, and future research directions.
Target Users :
This product is suitable for researchers, developers, and professionals interested in the self-assessment and self-updating mechanisms of large language models (LLMs). It provides in-depth insights and resources for understanding and improving LLMs.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 44.4K
Use Cases
Researchers use ICSFSurvey to understand the internal mechanisms of LLMs and design improvement strategies.
Developers leverage the code and data from this survey to create new assessment tools for LLMs.
Educators can utilize ICSFSurvey as a teaching material to help students grasp advanced concepts of LLMs.
Features
Provide a theoretical framework for internal consistency, explaining reasoning deficits and hallucination phenomena in LLMs.
Explore self-feedback mechanisms, including self-assessment and self-updating to enhance model responses or the models themselves.
Conduct systematic classification research based on tasks and workflows related to self-feedback mechanisms.
Summarize evaluation methods and benchmarks for assessing the effectiveness of self-feedback.
Investigate key considerations, such as the true efficacy of self-feedback, proposing hypotheses like the sandglass evolution of internal consistency.
Outline future research directions for internal consistency and self-feedback in LLMs.
How to Use
Visit the GitHub page to explore an overview and resources related to ICSFSurvey.
Read the README.md file for guidance on usage and contributions.
Browse the code/, data/, figures/, and other folders for experimental code, statistics, and illustrations.
Check the papers/ folder for a comprehensive list of relevant publications.
Contribute by submitting issues or pull requests to suggest improvements or add relevant papers.
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