Prompt Framing Distorts Count-Based Evaluation of LLM Error Detection: Evidence from Numeric Anchoring
arXiv:2607.01240v1 Announce Type: new Abstract: Count-based F1 is widely used as a proxy for LLM error-detection quality, but this paper shows that it can rise dramatically without a corresponding improvement in span localization, a gap termed F1 Inflation. The paper introduces ErrorBench, a controlled stress-test protocol for prompt-induced count distortion. ErrorBench evaluates six contemporary LLMs under five prompt conditions over 4,290 responses from 143 CoNLL-2014 passages. Under CoNLL-2014 M2-style scoring, anchored prompts produce up to 0.79 points of F1 Inflation, and up to 0.96 under
The rapid advancement and deployment of large language models are creating an urgent need for robust and reliable evaluation methodologies, making the discovery of prompt-induced distortions highly relevant.
This research reveals a critical vulnerability in current LLM evaluation methods, particularly for error detection, suggesting that reported performance metrics might be significantly inflated due to prompt framing.
The understanding of LLM error detection quality is challenged, necessitating a re-evaluation of current benchmarks and potentially leading to new best practices for prompt engineering and model assessment.
- · Researchers developing robust LLM evaluation protocols
- · Companies investing in advanced prompt engineering tools
- · LLM developers focused on true performance gains, not just F1 scores
- · Developers relying solely on simple count-based F1 for error detection
- · Evaluators ignoring prompt framing effects
- · Models optimized purely for easily-gameable metrics
Immediate re-evaluation of LLM error detection capabilities and benchmarks.
Development of more sophisticated and 'anti-fragile' evaluation metrics and prompting strategies to prevent distortion.
Increased skepticism among end-users and enterprise adopters regarding publicized LLM performance claims, leading to more cautious integration strategies.
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Read at arXiv cs.CL