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Unintended Bias in Language Model-driven Conversational Recommendation. (arXiv:2201.06224v2 [cs.IR] UPDATED)
Jan. 20, 2022, 2:10 a.m. | Tianshu Shen, Jiaru Li, Mohamed Reda Bouadjenek, Zheda Mai, Scott Sanner
cs.CL updates on arXiv.org arxiv.org
Conversational Recommendation Systems (CRSs) have recently started to
leverage pretrained language models (LM) such as BERT for their ability to
semantically interpret a wide range of preference statement variations.
However, pretrained LMs are well-known to be prone to intrinsic biases in their
training data, which may be exacerbated by biases embedded in domain-specific
language data(e.g., user reviews) used to fine-tune LMs for CRSs. We study a
recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS
to investigate how unintended …
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