Sequential recommender systems, which leverage historical interactions to deliver targeted recommendations, have been significantly advanced by large language models (LLMs). However, LLM-based generative sequential recommendation often faces two key challenges: the lack of collaborative knowledge and the limited controllability over the generated content. In this paper, we propose a simple Bi-Tuning framework with collaborative information for controllable Large Language Model-based Sequential Recommendation (Laser). Specifically, Bi-Tuning works through incorporating learnable virtual tokens at both the prefix and suffix of the input text, where the prefix tokens enable the adaptation of LLMs with collaborative information, while the suffix token transforms the LLM output into item/user embeddings for similarity comparison, thereby facilitating controllable recommendations. Furthermore, we introduce an MoE-based querying transformer that selectively activates experts to extract relevant information from varying collaborative signals of frozen ID-based recommenders into the prefix, coupled with a multi-task loss function incorporating the MoE load-balancing objective. Finally, a two-phase training strategy is employed to progressively obtain high-quality item and user embeddings through the learnable suffix. Experiments on real-world datasets show that Laser effectively adapts LLMs for sequential recommendation, outperforming state-of-the-art baselines.

“Relation Extraction (RE) task aims to discover the semantic relation that holds between two entitiesand contributes to many applications such as knowledge graph construction and completion. Reinforcement Learning (RL) has been widely used for RE task and achieved SOTA results, whichare mainly designed with rewards to choose the optimal actions during the training procedure,to improve RE’s performance, especially for low-resource conditions. Recent work has shownthat offline or online RL can be flexibly formulated as a sequence understanding problem andsolved via approaches similar to large-scale pre-training language modeling. To strengthen theability for understanding the semantic signals interactions among the given text sequence, thispaper leverages Transformer architecture for RL-based RE methods, and proposes a genericframework called Transformer Enhanced RL (TERL) towards RE task. Unlike prior RL-basedRE approaches that usually fit value functions or compute policy gradients, TERL only outputsthe best actions by utilizing a masked Transformer. Experimental results show that the proposedTERL framework can improve many state-of-the-art RL-based RE methods.”