When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation
arXiv:2602.11908v3 Announce Type: replace-cross Abstract: LLMs are widely used, yet they remain prone to factual errors that erode user trust and limit adoption in high-risk settings. One approach to mitigate this risk is to equip models with uncertainty estimation mechanisms that abstain when confidence is low. However, this binary "all-or-nothing" approach is excessively restrictive in long-form settings, often discarding valuable information. We introduce Selective Abstraction (SA), a framework that enables LLMs to trade specificity for reliability by selectively reducing the detail of unce
The increasing deployment of LLMs in high-stakes environments necessitates methods for improving reliability and trust, especially as their capabilities expand into complex, long-form generation.
Improving the reliability and trustworthiness of LLMs, particularly in avoiding factual errors, is critical for their broader adoption in enterprise, sensitive industries, and agentic systems, directly impacting their commercial viability and societal integration.
LLMs can now be equipped with a mechanism to selectively abstract information, trading perfect specificity for increased reliability, rather than defaulting to complete abstention when confidence is low, thus enhancing their utility in generative tasks.
- · LLM developers
- · Enterprises adopting AI
- · AI Safety researchers
- · Users of long-form AI generation
- · Platforms reliant on unchecked LLM specificity
- · Traditional content generation with high error rates
Increased trust and broader application of LLMs in critical domains due to enhanced reliability.
Accelerated development of AI agents capable of more nuanced and context-aware information generation, reducing the need for constant human oversight.
Potential for new regulatory frameworks and industry standards that mandate reliability mechanisms in critical AI applications, shifting market advantages.
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Read at arXiv cs.CL