2 citations · 4 across the 18 of their papers we have counts for
4 papers · 1 filter
Do Recommenders Need to Think in Words? Latent Reasoning for Efficient LLM-Based Recommendation
Hongyang Su, Beibei Kong, Lei Cheng +2
LLM-based recommender systems increasingly generate chain-of-thought (CoT) text before each item prediction to activate the LLM's reasoning capacity, costing tens to hundreds of ex…
Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation
Hongyang Su, Beibei Kong, Lei Cheng +3
Conversational news recommendation requires grounding each suggestion in a rapidly evolving article corpus while addressing implicit user intents that lack explicit retrievable key…
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm
Yuanzhen Xie, Xinzhou Jin, Tao Xie +7
In-context learning of large-language models (LLMs) has achieved remarkable success in the field of natural language processing, while extensive case studies reveal that the single…
OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
Yuanzhen Xie, Tao Xie, Mingxiong Lin +7
In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there…