awesome-hallucination-detection
List of papers on hallucination detection in LLMs.
https://github.com/EdinburghNLP/awesome-hallucination-detection
Last synced: 18 days ago
JSON representation
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Overviews, Surveys, and Shared Tasks
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[Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning](https://people.ece.ubc.ca/amesbah/resources/papers/cedar-icse23.pdf)
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
- llm-hallucination-survey
- here
- Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models
- Mitigating LLM Hallucinations: a multifaceted approach
- LLM Powered Autonomous Agents
- How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
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Measuring Hallucinations in LLMs
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[Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning](https://people.ece.ubc.ca/amesbah/resources/papers/cedar-icse23.pdf)
- Vectara LLM Hallucination Leaderboard
- TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
- UQLM: Uncertainty Quantification for Language Models
- AnyScale - Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper
- Arthur.ai - Hallucination Experiment
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[Do Androids Know They're Only Dreaming of Electric Sheep?](https://arxiv.org/abs/2312.17249)
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Open Source Models for Measuring Hallucinations
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[Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning](https://people.ece.ubc.ca/amesbah/resources/papers/cedar-icse23.pdf)
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Definitions and Notes
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Extrinsic and Intrinsic Hallucinations
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Papers and Summaries
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[Correction with Backtracking Reduces Hallucination in Summarization](https://arxiv.org/abs/2310.16176)
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Taxonomies
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[Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning](https://people.ece.ubc.ca/amesbah/resources/papers/cedar-icse23.pdf)
- Internal Consistency and Self-Feedback in Large Language Models: A Survey - Feedback framework.
- The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models - error Hallucination, Relation-error Hallucination, Incompleteness Hallucination, Outdatedness Hallucination, Overclaim Hallucination, Unverifiability Hallucination.
- A Survey of Hallucination in “Large” Foundation Models - specific LLMs*.
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Programming Languages
Categories
Sub Categories
[Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning](https://people.ece.ubc.ca/amesbah/resources/papers/cedar-icse23.pdf)
22
Extrinsic and Intrinsic Hallucinations
2
[Do Androids Know They're Only Dreaming of Electric Sheep?](https://arxiv.org/abs/2312.17249)
1
[Correction with Backtracking Reduces Hallucination in Summarization](https://arxiv.org/abs/2310.16176)
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Keywords
llm
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awesome-list
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large-language-models
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reading-list
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generative-ai
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hallucinations
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factual-consistency
1
inconsistency-detection
1
summarization
1
text-generation
1
ai-evaluation
1
ai-safety
1
confidence-estimation
1
confidence-score
1
hallucination
1
hallucination-detection
1
hallucination-evaluation
1
hallucination-mitigation
1
llm-evaluation
1
llm-hallucination
1
llm-safety
1
uncertainty-estimation
1
uncertainty-quantification
1