Can Small Language Models be Good Reasoners for Sequential Recommendation?

arXiv:2403.04260v1 Announce Type: cross
Abstract: Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations empowered by LLMs. Firstly, user behavior patterns are often complex, and relying solely on one-step reasoning from LLMs may lead to incorrect or task-irrelevant responses. Secondly, the prohibitively resource requirements of LLM (e.g., ChatGPT-175B) are overwhelmingly high and impractical for …

abstract arxiv behavior capabilities challenges cs.cl cs.ir cs.lg good however language language models large language large language models llms patterns recommendation recommendations small small language models type