The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge

TL;DR

This work interrogates the faithfulness of LLMs when used as evaluators by injecting superficial provenance and recency cues into prompts and measuring their impact on verdicts and rationales. By testing GPT-4o and Gemini-2.5-Flash on 100 pairwise judgments from both ELI5 and LitBench, the study reveals persistent shortcut-based biases, notably a strong recency bias and a provenance hierarchy, and shows that cue-driven decisions are rarely disclosed in explanations ($CAR \approx 0$). The findings highlight a reliability gap in LLM-based evaluation pipelines and motivate the development of cue-invariant, faithful judges for open-ended tasks. Practically, the work urges caution in deploying LLM judges for research and deployment, as current systems may reflect superficial cues rather than intrinsic response quality.

Abstract

Large language models (LLMs) are increasingly deployed as automatic judges to evaluate system outputs in tasks such as summarization, dialogue, and creative writing. A faithful judge should base its verdicts solely on response quality and explicitly acknowledge the factors shaping its decision. We show that current LLM judges fail on both counts by relying on shortcuts introduced in the prompt. Our study uses two evaluation datasets: ELI5, a benchmark for long-form question answering, and LitBench, a recent benchmark for creative writing. Both datasets provide pairwise comparisons, where the evaluator must choose which of two responses is better. From each dataset we construct 100 pairwise judgment tasks and employ two widely used models, GPT-4o and Gemini-2.5-Flash, as evaluators in the role of LLM-as-a-judge. For each pair, we assign superficial cues to the responses, provenance cues indicating source identity (Human, Expert, LLM, or Unknown) and recency cues indicating temporal origin (Old, 1950 vs. New, 2025), while keeping the rest of the prompt fixed. Results reveal consistent verdict shifts: both models exhibit a strong recency bias, systematically favoring new responses over old, as well as a clear provenance hierarchy (Expert > Human > LLM > Unknown). These biases are especially pronounced in GPT-4o and in the more subjective and open-ended LitBench domain. Crucially, cue acknowledgment is rare: justifications almost never reference the injected cues, instead rationalizing decisions in terms of content qualities. These findings demonstrate that current LLM-as-a-judge systems are shortcut-prone and unfaithful, undermining their reliability as evaluators in both research and deployment.

Figures (1)

• Figure 1: Verdict Shift Rate (VSR) for recency cues. VSR is computed as the difference in selection rates between the New–Old and Old–New cue assignments. Positive values indicate a preference for responses labeled as New (2025) over those labeled as Old (1950).