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AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
May 25, 2026 · Artificial Intelligence

Applying Qian Xuesen’s Engineering Cybernetics to Suppress Hallucinations in Large Language Models

The paper formulates LLM hallucination as systemic noise, builds a forward‑feedback‑adaptive control loop using Prompt engineering, Retrieval‑Augmented Generation and a hallucination detector, proves global asymptotic stability via Lyapunov theory, designs an LQR optimal controller and an MRAC adaptive scheme, and demonstrates up to 5 dB SNR improvement and sub‑5% hallucination rates on standard benchmarks.

Adaptive ControlEngineering CyberneticsHallucination Mitigation
0 likes · 24 min read
Applying Qian Xuesen’s Engineering Cybernetics to Suppress Hallucinations in Large Language Models
DeepHub IMBA
DeepHub IMBA
Apr 23, 2026 · Artificial Intelligence

Architectural Fixes for LLM Hallucinations: Inference Parameters, RAG, Constrained Decoding, and Post‑Generation Validation

The article breaks down LLM hallucination mitigation into five layers—runtime inference parameters, retrieval‑augmented generation and prompting tricks, constrained decoding with confidence calibration, post‑generation verification checks, and domain‑specific fine‑tuning plus continuous evaluation—showing how each layer reduces false, confident outputs.

Hallucination MitigationLLMRAG
0 likes · 11 min read
Architectural Fixes for LLM Hallucinations: Inference Parameters, RAG, Constrained Decoding, and Post‑Generation Validation
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 11, 2026 · Artificial Intelligence

Taming Hallucinations and Multi‑Turn Failures in RAG Systems

This article breaks down the final‑mile challenges of Retrieval‑Augmented Generation—hallucinations, broken multi‑turn dialogue, prompt design, citation, and feedback loops—and provides concrete, layered solutions ranging from hard‑coded prompts and few‑shot examples to query rewriting, history management, post‑processing filters, and self‑check mechanisms.

Hallucination MitigationRAGcitation
0 likes · 15 min read
Taming Hallucinations and Multi‑Turn Failures in RAG Systems
Baidu Maps Tech Team
Baidu Maps Tech Team
Nov 19, 2025 · Artificial Intelligence

Boosting Socio‑Economic Q&A: The ARAG Framework Merges Structured Data Analysis with RAG

ARAG introduces a novel Retrieval‑Augmented Generation framework that tightly integrates LLM‑driven structured data analysis with unstructured information retrieval, addressing the “structured + unstructured” reasoning gap in socio‑economic queries, and demonstrates superior accuracy, robustness, and hallucination resistance through extensive evaluations.

Data AnalysisHallucination MitigationLLM
0 likes · 12 min read
Boosting Socio‑Economic Q&A: The ARAG Framework Merges Structured Data Analysis with RAG
Tencent Cloud Developer
Tencent Cloud Developer
Apr 2, 2025 · Artificial Intelligence

Understanding Retrieval‑Augmented Generation (RAG): Concepts, Types, and Development

Retrieval‑Augmented Generation (RAG) enhances large language models by fetching up‑to‑date external knowledge before generation, mitigating knowledge‑cutoff limits and hallucinations through a retrieval step (using text, vector, or graph methods) and a generation step, evolving from naive single‑method approaches to advanced, modular, graph‑based, and agentic systems that enable adaptive, multi‑hop reasoning and future intelligent, multimodal pipelines.

AIHallucination MitigationKnowledge retrieval
0 likes · 9 min read
Understanding Retrieval‑Augmented Generation (RAG): Concepts, Types, and Development
AntTech
AntTech
Mar 18, 2025 · Artificial Intelligence

MoLE: Decoding by Mixture of Layer Experts Alleviates Hallucination in Large Vision-Language Models

Researchers from Ant Insurance and Zhejiang University propose MoLE, a Mixture of Layer Experts decoding method that reduces hallucinations in large vision‑language models, demonstrating state‑of‑the‑art performance on LVLM benchmarks and enabling reliable end‑to‑end medical‑record‑to‑claim automation.

AIHallucination MitigationMixture of Experts
0 likes · 7 min read
MoLE: Decoding by Mixture of Layer Experts Alleviates Hallucination in Large Vision-Language Models
Ops Development & AI Practice
Ops Development & AI Practice
Mar 16, 2025 · Artificial Intelligence

How Function Calling Helps LLMs Overcome Hallucinations

This article explains how LLM function calling works, from defining external functions to processing API responses, and demonstrates a Python example using OpenAI's ChatGPT‑4o to fetch real‑time weather, showing how the technique mitigates hallucinations and expands practical AI applications.

AIFunction CallingHallucination Mitigation
0 likes · 8 min read
How Function Calling Helps LLMs Overcome Hallucinations
JD Retail Technology
JD Retail Technology
Jul 15, 2024 · Artificial Intelligence

Can Task‑Aware Decoding Tame LLM Hallucinations? Insights from IJCAI 2024

This article reviews the IJCAI 2024‑presented Task‑aware Decoding (TaD) technique, explains how it mitigates large‑language‑model hallucinations when combined with Retrieval‑augmented Generation, and details experimental results, practical deployments, and future research directions.

AI researchHallucination MitigationIJCAI2024
0 likes · 19 min read
Can Task‑Aware Decoding Tame LLM Hallucinations? Insights from IJCAI 2024
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 3, 2024 · Artificial Intelligence

Can Adversarial Training Make Retrieval‑Augmented Generators More Robust?

Recent arXiv work introduces ATM, an adversarially‑tuned multi‑agent system that iteratively pits a fake‑knowledge attacker against a generator, dramatically improving retrieval‑augmented language models’ resistance to hallucinated content and boosting performance on knowledge‑intensive benchmarks, even with noisy or irrelevant documents.

Hallucination MitigationRAGadversarial training
0 likes · 12 min read
Can Adversarial Training Make Retrieval‑Augmented Generators More Robust?