← 研踪 PaperTrailarXiv:2611.10285 · v1
Conformal Fair Decoding for Large Language Model Recommenders
首次提交 2026-11-07 06:31
版本更新 2026-11-07 06:31
雷达观测 2026-11-10 10:11
摘要
Large language models (LLMs) can generate personalized recommendations without task-specific training, but changing a protected attribute while holding interaction history fixed can alter the semantic content of recommended items. We study fairness control when model weights and full-vocabulary logits are unavailable. We propose Fair- Rec, an item-level decoding framework that uses an offline-calibrated semantic-disparity threshold and controls candidate selection as recommendation lists are generated. A hard variant filters candidates against the threshold; a soft variant trades off candidate likelihood and disparity while adapting a penalty using observed violations. Under an explicit uniform enforceability assumption, we establish a pathwise O(1/T) upper bound on the excess empirical violation rate of the soft decoder. Experiments on MovieLens-1M and Amazon Movies & TV using Qwen3-8B and Llama3.1-8B-Instruct show that the soft variant lowers measured counterfactual disparity relative to zero- =shot recommendation, UP5, and FACTER, while retaining competitive HR@10 and NDCG@10.
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v1 · 2026-11-07 06:31 · 最新观测版本
Conformal Fair Decoding for Large Language Model Recommenders
Large language models (LLMs) can generate personalized recommendations without task-specific training, but changing a protected attribute while holding interaction history fixed can alter the semantic content of recommended items. We study fairness control when model weights and full-vocabulary logits are unavailable. We propose Fair- Rec, an item-level decoding framework that uses an offline-calibrated semantic-disparity threshold and controls candidate selection as recommendation lists are generated. A hard variant filters candidates against the threshold; a soft variant trades off candidate likelihood and disparity while adapting a penalty using observed violations. Under an explicit uniform enforceability assumption, we establish a pathwise O(1/T) upper bound on the excess empirical violation rate of the soft decoder. Experiments on MovieLens-1M and Amazon Movies & TV using Qwen3-8B and Llama3.1-8B-Instruct show that the soft variant lowers measured counterfactual disparity relative to zero- =shot recommendation, UP5, and FACTER, while retaining competitive HR@10 and NDCG@10.
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