HallucinationBuster
AI research tool where YOU choose which publishers to trust
HallucinationBuster launched on March 17, 2026 with 5 votes, #1,928 of 23,380 launches that month and more than 75% of that year's launches.
What it does
ChatGPT fabricates 20-56% of academic citations. HallucinationBuster takes a different approach: it searches real academic databases, lets you pick which publishers and years to include, then processes ONLY your approved papers. AI-powered topic clustering, relevance scoring, and RAG chat, all grounded in verified sources with clickable DOIs. You control the sources. The AI never sees a paper you didn't approve. Free tier available, no credit card needed.
ChatGPT gave you fake citations? HallucinationBuster searches real academic databases instead of an AI
AI chatbots like ChatGPT and Gemini fabricate citations — studies show over 50% of AI-generated references contain errors or point to papers that don't exist. HallucinationBuster is the cure: it searches real academic databases, verifies every paper with a working DOI, lets you choose publishers (IEEE, Springer, Nature, ACM) and publication years, clusters results into topics, and lets you chat with your verified sources. Free tier available at hallucinationbuster.comfrom hallucinationbuster.com
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Open-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
Hallucination-Proof AI Course Creator2025 · minicoursegenerator.com · ▲63Turn PDFs into courses with AI without irrelevant additions
- FAFactual AI Q&A – Answers based on Huberman Lab transcripts2022 · huberman.rile.yt · ▲120
This is a quick prototype I built for semantic search and factual question answering using embeddings and GPT-3. It tries to solve the LLM hallucination issue by guiding it only to answer questions from the given context instead of making things up. If you ask something not covered in an episode, it should say that it doesn't know rather than providing a plausible, but potentially incorrect response. It uses Whisper to transcribe, text-embedding-ada-002 to embed, Pinecone.io to search, and text-davinci-003 to generate the answer. More examples and explanations here:…

dreamGPT: What if LLM hallucinations were a feature and not a bug?2023 · github.com · ▲34The first GPT-based solution that uses hallucinations from LLMs for divergent thinking to generate new and novel ideas. Hallucinations are often seen as a negative thing, but what if they could be used for our advantage? dreamGPT is here to show you how. The goal of dreamGPT is to explore as many possibilities as possible, as opposed to most other GPT-based solutions which are focused on solving specific problems.
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