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hallucination-leaderboard discussion
hallucination-leaderboard reviews and mentions
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Why do chatbots hallucinate, and how to stop yours
Even with your content in front of it, a model slips now and then. Vectara’s hallucination leaderboard has models summarise more than 7,700 articles using only what each one says. On the version last updated on 22 September 2026, read on 23 September 2026, the best model still added something the article didn’t say in 1.8% of its summaries, and the rest of the top ten did so in 3.1% to 5.1%. Grounding a bot in your content cuts the problem down. It doesn’t remove it, so a support bot also needs a way to say no.
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RAG Is Not Enough: What Retrieval Still Gets Wrong in 2026
This is the trust-calibration trap, and it's the actual second-order effect of RAG adoption. Before RAG, users treated chatbot output the way you'd treat a stranger's confident opinion at a bar: interesting, unverified, check it yourself. After RAG, users treat the same output the way they'd treat a peer-reviewed footnote, because it has the visual furniture of one. The Vectara Hallucination Leaderboard, which has run continuously since 2023 and now scores over 7,700 documents across law, medicine, and finance, still shows meaningful spread among 2026's frontier models on grounded summarization, Gemini 2.0 Flash at roughly 0.7%, GPT-4o near 1.5%, Claude Opus around 10% (Vectara, 2026). Even the best-behaved model on the board is wrong some fraction of the time it's handed the exact right document and told to just summarize it faithfully. Retrieval was never the whole problem. It was always partly a generation problem, and generation didn't get fixed, it got a better alibi.
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Amazon Bedrock Automated Reasoning Checks: Eliminate Hallucinations with AI
Vectara Hallucination Leaderboard (updated September 2025)
- Qué es RAG: guÃa completa sobre generación aumentada por recuperación en 2026
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Testing AI: How to Effectively Evaluate LLMs
Vectara's Hallucination Leaderboard, which benchmarks LLMs for factual consistency in summarisation tasks, found that even frontier reasoning models, including GPT-5, Claude Sonnet 4.5, Grok-4, and DeepSeek-R1, all exhibited hallucination rates exceeding 10% on their updated, more challenging benchmark. The recently released Gemini-3-pro demonstrated a 13.6% hallucination rate and did not make the top-25 list.
- Choosing the Right LLM for Cognee: Local Ollama Setup
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GPT-5.2
GPT-5.2 just added to Vectara Hallucination Leaderboard. Definitely an improvement over GPT-5.1 - congrats to the team
https://github.com/vectara/hallucination-leaderboard
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The AI-Native GraphDB + GraphRAG + Graph Memory Landscape & Market Catalog
Vectara - vectara.com/
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AI assistants misrepresent news content 45% of the time
Hallucination Leaderboard
https://github.com/vectara/hallucination-leaderboard
If the figures in this leaderboard are to be trusted, many frontier and near-frontier models are already better than a median white-collar worker in this aspect.
- Amazon Bedrock Guardrails Automated Reasoning Checks: Minimiza las Alucinaciones en IA con Verificación Matemática
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A note from our sponsor - SaaSHub
www.saashub.com | 6 Oct 2026
Stats
vectara/hallucination-leaderboard is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of hallucination-leaderboard is Python.
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