42 citations · 86 across the 65 of their papers we have counts for
21 papers · 1 filter
MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedback
Shihao Cai, Chongming Gao, Haoyan Liu +4
The powerful reasoning and generative capabilities of large language models (LLMs) have inspired researchers to apply them to reasoning-based recommendation tasks, which require in…
FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis
Fengbin Zhu, Xiang Yao Ng, Ziyang Liu +19
Deep Research (DR) agents, powered by advanced Large Language Models (LLMs), have recently garnered increasing attention for their capability in conducting complex research tasks.…
Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning
Wuqiang Zheng, Yiyan Xu, Xinyu Lin +3
With the rapid and continuous increase in academic publications, identifying high-quality research has become an increasingly pressing challenge. While recent methods leveraging La…
Heterogeneous User Modeling for LLM-based Recommendation
Honghui Bao, Wenjie Wang, Xinyu Lin +4
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation. A key cha…
MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning
Xiaoyuan Li, Moxin Li, Wenjie Wang +4
Recent progress in Multi-modal Large Language Models (MLLMs) has enabled step-by-step multi-modal mathematical reasoning by performing visual operations based on the textual instru…
SASFT: Sparse Autoencoder-guided Supervised Finetuning to Mitigate Unexpected Code-Switching in LLMs
Boyi Deng, Yu Wan, Baosong Yang +3
Large Language Models (LLMs) have impressive multilingual capabilities, but they suffer from unexpected code-switching, also known as language mixing, which involves switching to u…
VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation
Ziang Ye, Yang Zhang, Wentao Shi +3
Graphical User Interface (GUI) agents powered by Large Vision-Language Models (LVLMs) have emerged as a revolutionary approach to automating human-machine interactions, capable of…
IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation
Zijie Lin, Yang Zhang, Xiaoyan Zhao +3
Large Language Models (LLMs) have shown strong potential for recommendation by framing item prediction as a token-by-token language generation task. However, existing methods treat…
NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Xiaoyan Zhao, Juntao You, Yang Zhang +5
Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users…
Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders
Boyi Deng, Yu Wan, Yidan Zhang +2
The mechanisms behind multilingual capabilities in Large Language Models (LLMs) have been examined using neuron-based or internal-activation-based methods. However, these methods o…
Reinforced Latent Reasoning for LLM-based Recommendation
Yang Zhang, Wenxin Xu, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, sparking growing interest in their application to preference reas…
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation
Xiaoyuan Li, Keqin Bao, Yubo Ma +6
Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning s…
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition
Yiyan Xu, Wuqiang Zheng, Wenjie Wang +5
Personalized image generation has emerged as a promising direction in multimodal content creation. It aims to synthesize images tailored to individual style preferences (e.g., colo…
Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance
Fengbin Zhu, Junfeng Li, Liangming Pan +5
Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce…
Personalized Generation In Large Model Era: A Survey
Yiyan Xu, Jinghao Zhang, Alireza Salemi +6
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents t…
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
Yilun Qiu, Xiaoyan Zhao, Yang Zhang +5
Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personaliza…
Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection
Yan Weng, Fengbin Zhu, Tong Ye +3
Retrieval-Augmented Generation (RAG), which integrates external knowledge into Large Language Models (LLMs), has proven effective in enabling LLMs to produce more accurate and reli…
Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment
Moxin Li, Yuantao Zhang, Wenjie Wang +4
Multi-Objective Alignment (MOA) aims to align LLMs' responses with multiple human preference objectives, with Direct Preference Optimization (DPO) emerging as a prominent approach.…
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
Xiaoyuan Li, Moxin Li, Rui Men +6
Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models tru…
Order-agnostic Identifier for Large Language Model-based Generative Recommendation
Xinyu Lin, Haihan Shi, Wenjie Wang +4
Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning…
Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation
Yi Fang, Wenjie Wang, Yang Zhang +4
Aligning Large Language Models (LLMs) with recommendation tasks represents an emerging paradigm in recommendation domain, exhibiting promising performance overall. However, these a…