arXiv:2504.07385v3 Announce Type: replace Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness. Meanwhile, using LLMs themselves as evaluators without external grounding remains unreliable for objective tasks, as they systematically over-accept incorrect answers, fabricate supporting rationales, and degrade sharply on questions that fall outside their training data. We propose Search-AuGmented Evaluation (SAGE), a framework to
The rapid deployment and increasing reliance on Large Language Models for critical 'objective' tasks like Q&A necessitates more robust and reliable evaluation methods that overcome the limitations of static datasets and ungrounded LLM self-evaluation.
Improved, externally-grounded evaluation methods for LLMs are crucial for developing more trustworthy and less 'hallucinating' AI systems, directly impacting their deployment and utility in professional and critical applications.
The introduction of frameworks like SAGE offers a more dynamic and externally-grounded approach to evaluating LLMs, potentially leading to faster iteration, more accurate benchmarks, and ultimately, more reliable AI.
- · AI developers
- · Enterprises adopting LLMs
- · AI ethics and safety researchers
- · Search engine providers
- · LLM providers relying on simplistic benchmarks
- · Traditional static QA evaluation methodologies
- · Users relying on ungrounded LLM evaluations
More rigorous LLM evaluation standards will accelerate the development of more accurate and less error-prone models.
Increased trust in LLM outputs will lead to wider adoption in complex, objective-oriented workflows, potentially displacing more human-intensive processes.
The integration of real-time search and external data for evaluation could blur the lines between LLM development and real-time knowledge integration, creating new paradigms for AI training and deployment.
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Read at arXiv cs.CL