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Good folks from 
Good folks from 
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@amazon
amazon
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, 
, 
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@Stanford
Stanford
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, and other great institutions have released “A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models!”
, and other great institutions have released “A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models!”
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This comprehensive survey examines over 32 cutting-edge techniques to combat hallucination in Large Language Models (LLMs). As LLMs become increasingly integral to our daily operations, addressing their tendency to generate ungrounded content is crucial.
This comprehensive survey examines over 32 cutting-edge techniques to combat hallucination in Large Language Models (LLMs). As LLMs become increasingly integral to our daily operations, addressing their tendency to generate ungrounded content is crucial.
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Retrieval-Augmented Generation (RAG) Innovations:
Retrieval-Augmented Generation (RAG) Innovations:
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- Pre-generation retrieval using LLM-Augmenter with Plug-and-Play modules
- Pre-generation retrieval using LLM-Augmenter with Plug-and-Play modules
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- Real-time verification through the EVER framework implementing three-stage validation
- Real-time verification through the EVER framework implementing three-stage validation
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- Post-generation refinement via the RARR system for automated attribution
- Post-generation refinement via the RARR system for automated attribution
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Advanced Decoding Strategies:
Advanced Decoding Strategies:
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- Context-Aware Decoding (CAD) utilizing contrastive output distribution
- Context-Aware Decoding (CAD) utilizing contrastive output distribution
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- DoLa's innovative approach of contrasting logit differences between transformer layers
- DoLa's innovative approach of contrasting logit differences between transformer layers
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Knowledge Integration Methods:
Knowledge Integration Methods:
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- The RHO framework leveraging entity representations and relation predicates
- The RHO framework leveraging entity representations and relation predicates
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- FLEEK's intelligent fact verification system using curated knowledge graphs
- FLEEK's intelligent fact verification system using curated knowledge graphs
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Novel Loss Functions:
Novel Loss Functions:
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- Text Hallucination Regularization (THR) derived from mutual information
- Text Hallucination Regularization (THR) derived from mutual information
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- The mFACT metric for evaluating faithfulness in multilingual contexts
- The mFACT metric for evaluating faithfulness in multilingual contexts
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This research provides a structured taxonomy for categorizing these mitigation techniques, offering valuable insights for practitioners and researchers working with LLMs.
This research provides a structured taxonomy for categorizing these mitigation techniques, offering valuable insights for practitioners and researchers working with LLMs.
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What are your thoughts on hallucination mitigation in LLMs?
What are your thoughts on hallucination mitigation in LLMs?
Good folks from @amazon, @Stanford, and other great institutions have released “A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models!”

This comprehensive survey examines over 32 cutting-edge techniques to combat hallucination in Large Language Models (LLMs). As LLMs become increasingly integral to our daily operations, addressing their tendency to generate ungrounded content is crucial.

Retrieval-Augmented Generation (RAG) Innovations:
- Pre-generation retrieval using LLM-Augmenter with Plug-and-Play modules
- Real-time verification through the EVER framework implementing three-stage validation
- Post-generation refinement via the RARR system for automated attribution

Advanced Decoding Strategies:
- Context-Aware Decoding (CAD) utilizing contrastive output distribution
- DoLa's innovative approach of contrasting logit differences between transformer layers

Knowledge Integration Methods:
- The RHO framework leveraging entity representations and relation predicates
- FLEEK's intelligent fact verification system using curated knowledge graphs

Novel Loss Functions:
- Text Hallucination Regularization (THR) derived from mutual information
- The mFACT metric for evaluating faithfulness in multilingual contexts

This research provides a structured taxonomy for categorizing these mitigation techniques, offering valuable insights for practitioners and researchers working with LLMs.

What are your thoughts on hallucination mitigation in LLMs?
👀
John6666
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