Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesAdaptersDebuggingDeploymentEvaluationFallbacksLangSmithRun LLMs locallyModel comparisonPrivacyPydantic compatibilitySafetyAmazon Comprehend Moderation ChainConstitutional chainHugging Face prompt injection identificationLogical Fallacy chainModeration chainMoreGuidesSafetyLogical Fallacy chainOn this pageLogical Fallacy chainThis example shows how to remove logical fallacies from model output.Logical Fallacies‚ÄãLogical fallacies are flawed reasoning or false arguments that can undermine the validity of a model's outputs. Examples include circular reasoning, false
dichotomies, ad hominem attacks, etc.  Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However,
This example shows how to remove logical fallacies from model output.
This example shows how to remove logical fallacies from model output. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesAdaptersDebuggingDeploymentEvaluationFallbacksLangSmithRun LLMs locallyModel comparisonPrivacyPydantic compatibilitySafetyAmazon Comprehend Moderation ChainConstitutional chainHugging Face prompt injection identificationLogical Fallacy chainModeration chainMoreGuidesSafetyLogical Fallacy chainOn this pageLogical Fallacy chainThis example shows how to remove logical fallacies from model output.Logical Fallacies‚ÄãLogical fallacies are flawed reasoning or false arguments that can undermine the validity of a model's outputs. Examples include circular reasoning, false
dichotomies, ad hominem attacks, etc.  Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However,