11 papers
Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents
Ziang Ye, Wentao Shi, Lei Chen +7
Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dead-end trajectories. Agents m…
CAST: Game Solvers as Turn-Level Teachers for LLM Agents
Yu Wang, Yi-Kai Zhang, Wentao Shi +8
Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR)…
Intuition-Guided Latent Reasoning for LLM-Based Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender syst…
Look Before You Leap: Autonomous Exploration for LLM Agents
Ziang Ye, Wentao Shi, Yuxin Liu +6
Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-…
Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders
Wentao Shi, Qifan Wang, Chen Chen +7
Reinforcement learning (RL) effectively optimizes Large Language Model (LLM)-based recommenders by contrasting positive and negative items. Empirically, training with beam-search n…
Verifiable Reasoning for LLM-based Generative Recommendation
Xinyu Lin, Hanqing Zeng, Hanchao Yu +8
Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing…
Assistant-Guided Mitigation of Teacher Preference Bias in LLM-as-a-Judge
Zhuo Liu, Moxin Li, Xun Deng +2
LLM-as-a-Judge employs large language models (LLMs), such as GPT-4, to evaluate the quality of LLM-generated responses, gaining popularity for its cost-effectiveness and strong ali…
MPT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Taowen Wang, Yiyang Liu, James Chenhao Liang +11
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization cap…
EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
Li Yang, Qifan Wang, Jianfeng Chi +7
Product attribute value extraction involves identifying the specific values associated with various attributes from a product profile. While existing methods often prioritize the d…
Direct Multi-Turn Preference Optimization for Language Agents
Wentao Shi, Mengqi Yuan, Junkang Wu +2
Adapting Large Language Models (LLMs) for agent tasks is critical in developing language agents. Direct Preference Optimization (DPO) is a promising technique for this adaptation w…
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Jaehyung Kim, Yuning Mao, Rui Hou +7
Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversa…