GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs
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| Format: | Preprint |
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2025
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| _version_ | 1866916002022817792 |
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| author | Song, Mingyang Zheng, Mao Luo, Xuan |
| author_facet | Song, Mingyang Zheng, Mao Luo, Xuan |
| contents | Pairwise LLM-as-a-judge evaluation asks the judge to identify the \emph{better} of two candidate answers. We study a one-line modification that asks for the \emph{worse} answer instead and recovers the preference by elimination, a procedure we call Goal-Reversed Prompting (GRP). GRP introduces no extra inference rounds, composes with any prompt template (direct, chain-of-thought, or Arena-Hard SOP), and leaves the rest of the evaluation pipeline untouched. Two observations motivate the reversal. Reverse reasoning is a recurring strategy in human problem solving, and modern instruction-tuned judges exhibit a positive-leaning bias that asking for the worse answer can counteract. On JudgeBench under a strict consistency protocol that counts a judgment as correct only when both response orderings agree with the gold preference, GRP improves all three closed-source judges we test across both response-pair sources. With GPT-4o-generated pairs, the Arena-Hard SOP baseline improves from 61.71\% to 66.23\% for GPT-4o (+4.52) and from 60.00\% to 66.00\% for Claude-3.5-Sonnet (+6.00), with the largest absolute gains on Reasoning and Mathematics. The lift persists when response pairs come from Claude-3.5-Sonnet and when the SOP scaffolding is stripped to a minimal direct-prompting template, suggesting that goal reversal acts on the underlying judging behavior rather than on a particular rubric. Stronger judges benefit more than weaker ones, suggesting that goal reversal exposes additional reasoning capacity rather than compensating for its absence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs Song, Mingyang Zheng, Mao Luo, Xuan Computation and Language Pairwise LLM-as-a-judge evaluation asks the judge to identify the \emph{better} of two candidate answers. We study a one-line modification that asks for the \emph{worse} answer instead and recovers the preference by elimination, a procedure we call Goal-Reversed Prompting (GRP). GRP introduces no extra inference rounds, composes with any prompt template (direct, chain-of-thought, or Arena-Hard SOP), and leaves the rest of the evaluation pipeline untouched. Two observations motivate the reversal. Reverse reasoning is a recurring strategy in human problem solving, and modern instruction-tuned judges exhibit a positive-leaning bias that asking for the worse answer can counteract. On JudgeBench under a strict consistency protocol that counts a judgment as correct only when both response orderings agree with the gold preference, GRP improves all three closed-source judges we test across both response-pair sources. With GPT-4o-generated pairs, the Arena-Hard SOP baseline improves from 61.71\% to 66.23\% for GPT-4o (+4.52) and from 60.00\% to 66.00\% for Claude-3.5-Sonnet (+6.00), with the largest absolute gains on Reasoning and Mathematics. The lift persists when response pairs come from Claude-3.5-Sonnet and when the SOP scaffolding is stripped to a minimal direct-prompting template, suggesting that goal reversal acts on the underlying judging behavior rather than on a particular rubric. Stronger judges benefit more than weaker ones, suggesting that goal reversal exposes additional reasoning capacity rather than compensating for its absence. |
| title | GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.06139 |