Do You Need a Frontier Model as a Citation Verifier? Benchmarking Rubric LLMs for Deep-Research Source Attribution
arXiv:2607.08700v1 Announce Type: new Abstract: Reinforcement learning increasingly relies on an LLM judge to score each rubric criterion, and that judge acts as the reward model during training. Before such a signal can be trusted, we need to know how capable the judge must be and how biased it is. We study this calibration question for citation quality in deep-research systems, where a search-grounded LLM must support each claim it writes with a cited source. Citation quality is a structured rubric task in which each attribution-citation pair is judged along two dimensions that require an LL
The proliferation of advanced LLMs necessitates robust evaluation methods, particularly as they are integrated into critical functions like information attribution and reward modeling.
Reliable citation verification for LLMs directly impacts their trustworthiness and utility in deep-research systems, influencing future AI development and application safety.
The understanding of how to reliably benchmark and calibrate LLMs for complex, structured tasks like source attribution is being refined, impacting development methodologies.
- · AI safety researchers
- · Developers of rigorous LLM evaluation frameworks
- · Enterprises deploying LLMs for critical tasks
- · Developers of uncalibrated or unverified LLM judges
- · Users relying on unvalidated LLM outputs for critical decisions
Improved reliability and trustworthiness of LLM-generated information and autonomous agent systems.
Increased investment in specialized benchmarking and evaluation tools for AI, driving new sub-sectors in the AI industry.
The development of a 'citation score' or 'trust score' for AI output becomes a critical metric, shaping adoption and regulation.
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Read at arXiv cs.CL