RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering
arXiv:2606.19218v1 Announce Type: new Abstract: Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity), and tell a better system from a worse one (discriminative power). On open-ended, opinion-driven question answering, the two are in tension. We introduce RECOM (Reddit Evaluation for Correspondence of Models), a contamination-free evaluation dataset of 15,000 r/AskReddit questions (September 2025), each paired with its authentic community replies, which postdate every
The proliferation of Large Language Models (LLMs) and the increasing reliance on automatic evaluation metrics necessitates a deeper understanding of their limitations and potential biases.
Accurate and reliable evaluation metrics are crucial for the development and deployment of robust AI systems, especially in open-ended tasks where current metrics struggle with validity and discriminative power.
This research highlights a fundamental tension in automated metric design for open-ended QA, forcing a re-evaluation of how LLM performance is assessed and potentially leading to more sophisticated evaluation methodologies.
- · AI researchers focusing on evaluation
- · Developers of transparent AI systems
- · Companies investing in human-centric evaluation
- · Developers relying solely on simplistic automatic metrics
- · Benchmarks that prioritize ease of automation over validity
- · Systems optimized for surface-level textual similarity
Increased scrutiny and refinement of automatic evaluation metrics for LLMs in open-ended generation tasks.
Development of hybrid evaluation frameworks combining automated metrics with more sophisticated human or expert judgment.
A potential slowdown in the pace of LLM development for certain applications until more reliable evaluation methods are widely adopted.
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Read at arXiv cs.CL