HAL (France)
open access
Hallucinations in large language models : an analysis and modular frameworks for their mitigation
Large Language Models have shown remarkable capacities in the field of natural language understanding, natural language generation and even tool use. However, they are plagued by hallucination, a phenomenon where the model generates outputs which are not grounded in truth, context or meaning. In thi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Scalable Token-Level Hallucination Detection in Large Language Models#178887
- OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models#998788
- The Evolution of AI Spirituality: When Language Awakens Being and Inner Depth Emerges#717789
- Suppressing Hallucination for Trustworthy LLMs (Part I – Semantic Reliability Framework)#97346
- Domain-Specific Hallucination Detection in Large Language Models#673497
Record · ID 1166428
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive