Hallucinations are inevitable in LLMs because they cannot learn all computable functions according to learning theory.
In-Context Pretraining: Language Modeling Beyond Document Boundaries
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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HSAP introduces a hierarchical framework and sequence-aware algorithm with JIT-optimized NCCL communication to enable correct causal attention computation on hybrid-context packed sequences without limiting parallelism.
The paper surveys hallucination in LLMs with an innovative taxonomy, factors, detection methods, benchmarks, mitigation strategies, and open research directions.
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.
citing papers explorer
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Hallucination is Inevitable: An Innate Limitation of Large Language Models
Hallucinations are inevitable in LLMs because they cannot learn all computable functions according to learning theory.
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HSAP: A Hierarchical Sequence-aware Parallelism for Hybrid-Context Generative Models
HSAP introduces a hierarchical framework and sequence-aware algorithm with JIT-optimized NCCL communication to enable correct causal attention computation on hybrid-context packed sequences without limiting parallelism.
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A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
The paper surveys hallucination in LLMs with an innovative taxonomy, factors, detection methods, benchmarks, mitigation strategies, and open research directions.
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The Faiss library
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.