In recent times, there has been a shift towards adapting sequential recommendation to LLM paradigm to harness the capabilities of LLMs. These methods typically formulate recommendation data into natural language and train the model to forecast the subsequent item in an auto-regressive manner. Despite their notable success, the significant computational burden during inference poses a major challenge to their practical implementation. In this study, we aim to streamline current LLM-based recommendation models and introduce a straightforward yet highly effective model Lite-LLM4Rec. The primary objective of Lite-LLM4Rec is to ensure efficient inference for the sequential recommendation task. Lite-LLM4Rec circumvents the step-by-step beam search decoding by employing a direct item projection head to produce ranking scores in one step. This design arises from our empirical finding that beam search decoding is ultimately unnecessary for sequential recommendations. Additionally, Lite-LLM4Rec introduces a hierarchical LLM structure crafted to efficiently handle the extensive contextual information of items and redundant computation issue, thus diminishing computational overhead while enjoying the power of LLMs. Experiments on four publicly available datasets validate the efficacy of Lite-LLM4Rec in enhancing both performance and inference efficiency (notably 46.8% performance improvement and 99.48% efficiency improvement on ML-1m) compared to existing LLM-based methods. Our implementations are available at: https://github.com/HanbingWang2001/Lite-LLM4Rec-PyTorch.

Conversational question answering (ConvQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. These inputs are easy for human beings to understand given a conversation history, but hard for a machine to interpret, which can degrade ConvQA performance. To address this problem, we propose a reinforcement learning (RL) based model, CoRnNet, which utilizes question reformulations generated by large language models (LLMs) to improve ConvQA performance. CoRnNet adopts a teacher-student architecture where a teacher model learns question representations using human writing reformulations, and a student model to mimic the teacher model’s output via reformulations generated by LLMs. The learned question representation is then used by a RL model to locate the correct answer in a KG. Extensive experimental results show that CoRnNet outperforms state-of-the-art ConvQA models.