Editing Openai/688732eb-4d44-8000-bd19-b8f67fe5a227 (section)

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* Concept: Add a factuality verification layer on top of the LLM output.
* Implementation: 1. After the model generates a response, the text is passed through a fact-checker module (e.g., knowledge graph or web search). 2. Any unverified claim is either flagged or replaced with “I cannot verify this.” 3. Models like RAG (Retrieval-Augmented Generation) already do a basic version of this, but it needs to be mandatory and rigorous.

===== - Concept: Force the model to produce an “uncertainty score” for each statement. =====
* Implementation: - Use probabilistic calibration (temperature scaling, Bayesian inference, or ensemble models). - If uncertainty > threshold, the response is prefixed with “I might be wrong about this” or the model is required to ask clarifying questions instead of making assumptions.

===== - Problem: Current RLHF (Reinforcement Learning with Human Feedback) sometimes rewards “confident-sounding” answers rather than truthful ones. =====
* Solution: 1. Adjust reward models to penalize plausible but unverifiable claims. 2. Use truthfulQA-style datasets that train the model to say “I don’t know” instead of guessing. 3. Use adversarial training where a secondary AI tries to detect misleading statements.

===== - Concept: Reduce training on purely text-prediction corpora that mix facts and opinions without distinction. =====
* Implementation: - Include structured data sources (Wikidata, Freebase) during pretraining. - Label ambiguous content as “opinion” vs. “fact” so the model learns to differentiate.

===== - Concept: Before outputting, the model could generate two answers: - Initial answer (candidate). - Self-critique: "Does this answer have unverifiable claims?" - Then, the system outputs only the revised, self-checked answer. =====
* Technical Basis: This is similar to chain-of-thought self-verification (already tested in some models, with strong results for factual accuracy).

===== - Require the model to cite sources or explain how it arrived at a claim, especially for critical answers (medical, legal, cybersecurity). =====
* If it cannot cite a source, it should explicitly say: “This is a prediction, not a confirmed fact.”