BLaIR is a new benchmark and 570M-review dataset showing that LLM performance rankings on recommendation tasks have little correlation with rankings on general embedding benchmarks like MTEB.
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Embedding a trainable graph message-passing network within the LoRA bottleneck of an LLM improves recommendation accuracy over prior collaborative-alignment methods at minimal parameter cost.
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Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
BLaIR is a new benchmark and 570M-review dataset showing that LLM performance rankings on recommendation tasks have little correlation with rankings on general embedding benchmarks like MTEB.
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GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation
Embedding a trainable graph message-passing network within the LoRA bottleneck of an LLM improves recommendation accuracy over prior collaborative-alignment methods at minimal parameter cost.