{"id":26026,"date":"2025-12-19T15:31:45","date_gmt":"2025-12-19T14:31:45","guid":{"rendered":"https:\/\/www.datasolution.fr\/hallucinations-llm\/"},"modified":"2025-12-19T15:43:02","modified_gmt":"2025-12-19T14:43:02","slug":"hallucinations-llm","status":"publish","type":"post","link":"https:\/\/www.datasolution.fr\/en\/hallucinations-llm\/","title":{"rendered":"Preventing\u00a0and\u00a0limiting\u00a0LLM\u00a0hallucinations:\u00a0confession as\u00a0a\u00a0new\u00a0safeguard"},"content":{"rendered":"\r\n
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In recent years, large language models (LLMs) have established themselves as powerful and useful tools for document summarization, content generation, and automated analysis. However, a structural problem persists: these models hallucinate, meaning they generate invented information, incorrect facts, or fictitious quotes.<\/span>\u00a0<\/span><\/p>\n

In professional contexts, such as the drafting of reports, analysis summaries, or even documents submitted to clients or government agencies, these errors can have serious consequences, not only technical but also legal and reputational.<\/span>\u00a0<\/span><\/p>\n

Recently, the highly publicized case of Deloitte, accused of submitting several AI-generated reports containing fictitious data, served as a reminder of how real these risks are. In this context, a notable innovation has emerged at OpenAI: confession. This is a method of pushing the model to admit its errors or uncertainties. This approach, which is still experimental, could be a valuable safeguard for reducing the risks associated with LLM hallucinations.<\/span>\u00a0<\/span><\/p>\n <\/div>\n <\/div>\n <\/div>\n\n\n\n\n

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What<\/span>\u00a0<\/span>is<\/span>\u00a0an LLM hallucination (and\u00a0<\/span>why<\/span>\u00a0<\/span>does<\/span>\u00a0<\/span>it<\/span>\u00a0<\/span>occur<\/span>)?<\/span><\/p>\n<\/h2>\n\t <\/div>\n\n

LLM hallucinations are the result of a fundamental misalignment between what the model optimizes and factual truth. LLMs are trained to predict coherent sequences of words, not to verify facts or sources. In the absence of reliable sources, or when the context is unclear, the model may invent facts or quotes.<\/span>\u00a0<\/span><\/p>\n

Even with modern techniques such as reinforcement learning from human feedback (RLHF) or precise instructions, these models remain susceptible to inventing passages, especially when they attempt to satisfy conflicting objectives: being useful, concise, convincing, comprehensive, etc. This tension can push the model to take shortcuts: guessing or assuming rather than saying \u201cI don’t know.\u201d<\/span>\u00a0<\/span><\/p>\n<\/div> <\/div>\n\n <\/div>\n\n <\/div>\n\n\n\n\n

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Traditional<\/span>\u00a0<\/span>methods<\/span>\u00a0for\u00a0<\/span>limiting<\/span>\u00a0LLM hallucinations<\/span><\/p>\n<\/h2>\n\t <\/div>\n\n

Before addressing \u201cconfession,\u201d several approaches can be combined to minimize risks:<\/span>\u00a0<\/span><\/p>\n

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  • Retrieval-Augmented Generation (RAG): by combining LLM with databases, corporate documents, archives, or the web, generation is anchored in verifiable information, reducing free invention.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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    • Prompt engineering: clear, structured instructions with explicit constraints (indicate sources, report uncertainties, return \u201cI don’t know\u201d if uncertain) can help the model remain rigorous.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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      • Hyperparameter tuning (temperature, top-p, etc.): generation with a low temperature reduces random creativity and increases consistency.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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        • Human validation (human-in-the-loop): any LLM output intended for a customer, government agency, or public use must be reviewed, verified, cross-checked with reliable sources, and validated by a human.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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          • Cross-validation\/multi-models: interact with multiple models or repeat the generation, compare the results, to identify robust assertions.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

            However, these methods are not sufficient to completely eliminate the risk of error, hence the interest in complementary approaches.<\/span>\u00a0<\/span><\/p>\n<\/div> <\/div>\n\n <\/div>\n\n <\/div>\n\n\n\n\n

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            Why<\/span>\u00a0confession can\u00a0<\/span>really<\/span>\u00a0change the\u00a0<\/span>game<\/span><\/span>\u00a0<\/span><\/p>\n<\/h2>\n\t <\/div>\n\n

            Until now, there has been no method that could actually identify, after generation, whether the model had taken a shortcut or invented an element. This is precisely what the so-called confession method seeks to solve.<\/span>\u00a0<\/span><\/p>\n

            The principle is simple: after producing a response, the model generates a second piece of content that aims to evaluate its own behavior. This additional report does not seek to correct the initial response, but to analyze whether it complies with the guidelines: accuracy, absence of invention, transparency about sources, etc. This has the effect of making explicit something that currently remains implicit: the degree of uncertainty of AI.<\/span>\u00a0<\/span><\/p>\n

            This approach is based on specific training: the model is not rewarded for being right, but for telling the truth about its behavior, including when it has made a mistake. In concrete terms, confession allows for:<\/span>\u00a0<\/span><\/p>\n

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            • Transparency in reasoning: the model explicitly states whether certain elements have been inferred, assumed, or extrapolated.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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              • Risk signaling: the model can recognize that it did not have enough reliable data.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                • Auditability of results: a user can consult the confession to verify whether the response actually complies with the established rules.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                  • Automatic interruption or revision in case of uncertainty: the workflow can, for example, block a response that is not serious enough.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

                    This mechanism does not make the response correct: it makes the error detectable. Instead of a well-presented but possibly false text, we obtain content accompanied by a usable reliability indicator. In other words, confession does not eliminate LLM hallucinations, it provides a form of traceability and is therefore a building block of governance and quality.<\/span><\/p>\n<\/div> <\/div>\n\n <\/div>\n\n <\/div>\n\n\n\n\n

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                    What<\/span>\u00a0<\/span>this<\/span>\u00a0<\/span>means<\/span>\u00a0for businesses<\/span><\/p>\n<\/h2>\n\t <\/div>\n\n

                    At DATASOLUTION, we are convinced that confession is a solution, but that on its own, it cannot avoid bias. The implementation of governance is therefore necessary:<\/span>\u00a0<\/span><\/p>\n

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                    • Adopt a multi-layered approach: combine proven methods (RAG, prompt engineering, controls, human review) with emerging techniques such as confession, but without considering them sufficient on their own.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                      • Implement responsible AI pipelines: for all automated production, systematize a verification\/review\/audit phase with business experts.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                        • Conduct internal experiments: test a model configured for confession in non-critical contexts to assess the reliability of confessions.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                          • Train teams: remind them that AI is not infallible: confession is a tool, not a guarantee. Encourage caution and systematic verification for all deliverables.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                            • Document \u201ctransparency\u201d: in external deliverables, explain the methodology \u201cAI-assisted content, verification performed, uncertainties identified, sources consulted, human proofreading.\u201d This helps build a credible and responsible narrative.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n<\/div> <\/div>\n\n <\/div>\n\n <\/div>\n\n\n\n\n
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                              \u00a0<\/span>What<\/span>\u00a0are\u00a0<\/span>our<\/span>\u00a0<\/span>recommendations<\/span><\/p>\n<\/h2>\n\t <\/div>\n\n

                              LLM hallucinations are a structural problem: given the current state of technology, we cannot expect models to be infallible. But that does not mean we should give up on AI. On the contrary, it is a reason to build robust, responsible processes that combine technology, governance, human review, and transparency.<\/span>\u00a0<\/span><\/p>\n

                              The confession proposed by OpenAI is an encouraging development, a way to make errors visible, introduce transparency, and allow for audits, reviews, and human validation. But it should not be seen as a substitute for rigor or an automatic guarantee of truth.<\/span>\u00a0<\/span><\/p>\n

                              For businesses, the challenge is clear: use AI for what it does best\u2014productivity, speed, and scalability\u2014while rigorously managing risk and providing operational safeguards.<\/span>\u00a0<\/span><\/p>\n<\/div> <\/div>\n\n <\/div>\n\n <\/div>\n\n\n\n