hallucinations

16 items

ARTICLE↑ trendingHacker News (AI)·8h ago

Trump's new AI order – hallucinations aren't just for LLMs

The article discusses Trump's new executive order on AI, drawing a parallel between the "hallucinations" of large language models (LLMs) and certain political statements. It explores the implications of governmental policy on AI and the public perception of truth in the digital age. The publication questions the consistency and veracity of information emanating from different sources, whether technological or political.

regulation AI policy hallucinations politics

RESEARCHarXiv CS.CL·20h ago

Evaluating Hallucinations in Domain-Adapted Large Language Models

This study investigates hallucinations in domain-adapted Large Language Models, specifically Llama-2 fine-tuned with the Lamini dataset. It found that while the model excels in training-similar tasks, its ability to reason about and recall new domain-specific information is limited, leading to hallucinations and a tendency for over-generation.

Llama-2 hallucinations Domain Adaptation large language models

ARTICLE↑ trendingHacker News (AI)·14d ago

OpenAI admits AI hallucinations are mathematically inevitable (Sept. 2025)

OpenAI has admitted that AI hallucinations are not merely engineering flaws but are mathematically inevitable, a fundamental limitation of current AI models. This revelation challenges the perception that these issues can be entirely eliminated through further development.

limitations hallucinations mathematics AI

ARTICLE↑ trendingReddit r/MachineLearning·5/6/2026

Stop letting LLMs edit your .bib [D]

The author expresses shock at the frequent hallucinated citations by LLMs in academic papers, leading to incorrect author lists. They question the lack of respect for research and the need for harsher penalties, asking if others are experiencing the same issue.

LLMs citations hallucinations AI ethics

RESEARCHarXiv CS.CL·5/7/2026

Not All That Is Fluent Is Factual: Investigating Hallucinations of Large Language Models in Academic Writing

This study investigates hallucinations in Large Language Models (ChatGPT, Grok, Gemini, Copilot) when generating academic content, using 80 prompts across four categories. A novel weighted metric, the Hallucination Index (HI), was introduced to measure factual accuracy and reference validity.

academic writing AI quality Model Evaluation hallucinations

RESEARCHarXiv CS.CL·19d ago

Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification

This research examines how various lower-bit quantization levels impact LLaMA-3.1's performance in qualitative analysis, noting that low-bit models often produce hallucinations. It proposes a quantization-aware multi-pass prompt verification method to enhance accuracy by systematically reducing hallucinations and filtering unreliable content.

model performance Qualitative Analysis LLMs hallucinations

RESEARCHarXiv CS.CL·4/20/2026

Why Fine-Tuning Encourages Hallucinations and How to Fix It

Large language models often hallucinate facts, a problem exacerbated by supervised fine-tuning (SFT) which degrades pre-trained knowledge. This research proposes a self-distillation SFT method, inspired by continual learning, to mitigate hallucinations by regularizing output-distribution drift while effectively acquiring new factual information.

hallucinations large language models Fine-tuning Continual Learning

ARTICLEDEV.to AI·24d ago

Your AI Agent Hallucinates Tailwind Classes. Here's the Fix

AI agents often generate non-existent Tailwind CSS classes because they lack project-specific configuration context, leading to "hallucinations". The `tailwind-context-resolver-mcp` tool is introduced as a solution to provide agents with knowledge of the project's resolved design system.

Tailwind CSS code generation hallucinations AI development

DOCDEV.to AI·5/6/2026

The "Logic Span": Using OpenTelemetry to Trace Hallucinations

This content introduces the "Logic Span" method, which leverages OpenTelemetry to trace and debug hallucinations in Large Language Models (LLMs). By wrapping each "Thought" or "Reasoning Step" in a dedicated OTel Span, developers can identify exactly where an LLM's logic diverges from its intended plan, treating hallucinations like a stack trace.

hallucinations observability Debugging OpenTelemetry

ARTICLEDEV.to AI·29d ago

Prompt AI Coding Assistants to Build Production-Ready Agents: 8 Essential Patterns

This article discusses the importance of using essential patterns when prompting AI coding assistants to build production-ready agents. By specifying architecture decisions, one can prevent failures like hallucinations and token waste, which often occur silently until production.

prompt engineering code generation hallucinations AI development

RESEARCHarXiv CS.LG·4/28/2026

KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning

KARL is a novel framework designed to mitigate hallucinations in large language models by enabling them to appropriately abstain from questions beyond their knowledge. It achieves this through a Knowledge-Boundary-Aware Reward that dynamically estimates the model's knowledge and a Two-Stage RL Training Strategy that prevents excessive caution.

reinforcement learning hallucinations AI safety LLM

RESEARCHarXiv CS.CL·20d ago

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering

This paper argues that current Uncertainty Quantification (UQ) methods for LLMs are essentially unsupervised clustering algorithms, measuring internal consistency rather than external correctness. Consequently, these methods fail to detect "confident hallucinations" and may create a deceptive sense of safety when deploying LLMs in high-stakes domains.

LLMs Uncertainty Quantification hallucinations AI safety

ARTICLEDEV.to AI·5/2/2026

RAG Series (1): Why LLMs Need External Memory

This content explains that LLM hallucinations and knowledge cutoffs arise because their knowledge is frozen at training time. This prevents them from accessing new or internal data, leading them to invent answers or admit ignorance.

LLMs AI limitations RAG hallucinations

ARTICLEDEV.to AI·24d ago

Day 27: What GPT-4 Hallucinating 'Amritavati' Taught Me About Building Health AI for India

The experience with GPT-4 hallucinating a fictional drug, "Amritavati," highlights the dangers of simply translating AI for healthcare in India. The GoDavaii project aims to develop health AI that understands Indian cultural and linguistic nuances, verifying home remedies and adapting to diverse symptom expressions.

India Healthcare language hallucinations

ARTICLEDEV.to AI·4/10/2026

Citation Needed: Structured data extraction workflows

Este artigo explora a construção de um fluxo de trabalho utilizando modelos de linguagem generativos para verificar se um texto fornece evidências para suas afirmações, útil para auto-crítica ou detecção de alucinações. A tarefa exige um grau de compreensão de leitura e rigor que apenas modelos de linguagem maiores e de fronteira podem abordar, superando as capacidades de pipelines de PNL tradicionais.

structured data LLMs Workflow hallucinations

ARTICLEDeepLearning.AI (YouTube)·27d ago

Why AI keeps lying to you

The article explores why AI models, particularly large language models, frequently produce inaccurate or fabricated information. It explains that this phenomenon, often called "hallucination" or "lying," stems from their probabilistic nature and training data, rather than deliberate deception.

AI accuracy AI limitations hallucinations