Paper uses learning theory to formally prove LLMs cannot eliminate hallucination when used as general problem solvers.
Key Takeaways
Hallucination is defined as inconsistencies between a computable LLM and a computable ground truth function.
LLMs cannot learn all computable functions; hallucination is therefore mathematically unavoidable, not just an engineering gap.
Real-world LLMs face additional constraints from provable time complexity, making certain task classes especially hallucination-prone.
The paper analyzes existing mitigators (RAG, RLHF, etc.) through this formal framework, assessing their theoretical efficacy limits.
Implications point toward scoping LLM deployments away from tasks requiring complete coverage of computable functions.
Hacker News Comment Review
No substantive HN discussion yet. One commenter raised the legal and liability consequences if the impossibility result holds, but no technical rebuttals or validations have appeared.
