arXiv:2605.26440v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has outpaced the scalability of traditional evaluation benchmarks, which remain heavily dependent on labor-intensive expert curation. We address this bottleneck with Conv-to-Bench, a multi-stage framework that automatically transforms authentic multi-turn user-assistant dialogues into structured, verifiable requirement checklists. By leveraging the "instructional evolution" found in real-world conversational logs, our approach deconstructs fragmented user intent into consolidated instructions
The rapid advancement of LLMs necessitates more scalable and real-world evaluation methods, as traditional benchmarks are becoming insufficient.
This framework addresses a critical bottleneck in LLM development by enabling more efficient and realistic evaluation, accelerating model improvement and deployment.
LLM evaluation moves from labor-intensive, expert-curated benchmarks to automated, dialogue-driven verification, aligning evaluation with real-world user interaction.
- · AI developers
- · LLM companies
- · AI research institutions
- · Providers of traditional, static AI benchmarks
- · Manual AI evaluation teams
More robust and performant LLMs enter the market more quickly due to improved evaluation methods.
The iterative nature of 'instructional evolution' in testing could lead to LLMs that are inherently better at understanding and adapting to complex, evolving user intent.
This improved evaluation could accelerate the development of truly autonomous AI agents capable of handling multifaceted, dynamic tasks without constant human oversight.
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Read at arXiv cs.CL