{"slug": "adapting-llm-judges-to-observed-human-annotation-policy-through-soft-prompt", "title": "Adapting LLM Judges to Observed Human Annotation Policy through Soft Prompt Tuning", "summary": "Soft prompt tuning consistently raises open-source LLM judges' agreement with human annotations, according to an arXiv paper (2611.07126v1) trained on the FairBotBench benchmark and evaluated on bias, toxicity, and personalization. The hybrid approach, which combines textual prompts with learned continuous prompt embeddings, beat untuned baselines, and adapted Qwen 3.5 9B judges surpassed both hosted reference judges across all three dimensions. The result matters because lightweight open-source judges are cheaper and easier to deploy than proprietary models but typically show lower agreement with human ratings.", "body_md": "arXiv:2611.07126v1 Announce Type: new \nAbstract: Large Language Models (LLMs) are increasingly employed as automated judges for evaluating chatbot responses. While proprietary models can achieve high agreement with human annotators, they remain costly and difficult to deploy in practical settings. Lightweight open-source LLM judges provide an attractive alternative but typically exhibit lower agreement with human ratings. In this paper, we investigate whether soft prompt tuning can align open-source LLM judges more closely with human annotation policy. We train soft prompts on the FairBotBench benchmark and evaluate the adapted judges across the dimensions of bias, toxicity, and personalization. Our findings demonstrate that hybrid soft-prompt tuning, which combines textual prompts with learned continuous prompt embeddings, consistently improves agreement with human annotations over the corresponding untuned baselines. For Qwen 3.5 9B, the adapted judges surpass both hosted reference judges across all three evaluation dimensions.", "url": "https://wpnews.pro/news/adapting-llm-judges-to-observed-human-annotation-policy-through-soft-prompt", "canonical_source": "https://arxiv.org/abs/2611.07126", "published_at": "2026-11-06 05:00:00+00:00", "updated_at": "2026-11-06 05:19:32.252335+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety"], "entities": ["Qwen 3.5 9B", "FairBotBench", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/adapting-llm-judges-to-observed-human-annotation-policy-through-soft-prompt", "markdown": "https://wpnews.pro/news/adapting-llm-judges-to-observed-human-annotation-policy-through-soft-prompt.md", "text": "https://wpnews.pro/news/adapting-llm-judges-to-observed-human-annotation-policy-through-soft-prompt.txt", "jsonld": "https://wpnews.pro/news/adapting-llm-judges-to-observed-human-annotation-policy-through-soft-prompt.jsonld"}}