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Recent discussions and studies have focused on the concept of AI 'hallucinations', where large language models (LLMs) generate plausible but factually incorrect responses due to unfamiliar examples during finetuning. Researchers are exploring ways to prevent these hallucinations and create more robust LLM systems, including proposing models that express uncertainty and conservative reward models.
Can we get LLMs to "hedge" and express uncertainty rather than hallucinate? For this we first have to understand why hallucinations happen. In new work led by @katie_kang_ we propose a model of hallucination that leads to a few solutions, including conservative reward models 🧵👇 https://t.co/GwIDQ8ZqKT
On Preventing Hallucinations And Creating More Robust LLM Systems As we put more LLM apps in production, we find that preventing hallucinations is the biggest problem to overcome. Unlike humans, they don't know what they don't know. There are multiple ways to prevent… https://t.co/HjYVJZ8nDZ
[LG] Unfamiliar Finetuning Examples Control How Language Models Hallucinate https://t.co/hsiGDxwjwa This study reveals the mechanisms behind large language models (LLMs) generating plausible but factually incorrect responses when queried on unfamiliar concepts. It was… https://t.co/i53y1oNRuS
