Preprocessing Choices in Fine-Tuning Data Matter: Utility and Demographic Sensitivity in LLM-Based Recommendation
Read the original on arXiv Computation and Language →The paper examines how different preprocessing steps in fine‑tuning data influence the performance and fairness of a generative large language model recommender system. Using a controlled pipeline that includes supervised fine‑tuning, catalog‑constrained decoding, and prompt augmentation, the authors evaluate three preprocessing choices—item‑title representation, demographic balancing, and auxiliary item‑knowledge enrichment—on the MovieLens‑1M dataset. Results show that catalog constraints eliminate out‑of‑catalog items, prompt augmentation improves consistency and utility, and that demographic balancing and knowledge enrichment can reduce utility gaps for certain groups while preserving overall performance, though gender balancing may lower utility without improving gender indicators.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.