6 citations · 7 across the 10 of their papers we have counts for
10 papers
CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning
Eric Onyame, Akash Ghosh, Subhadip Baidya +3
While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning appli…
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
Linfeng Du, Ye Yuan, Zichen Zhao +8
Large language models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval augmentation provides a lightweight alt…
MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models
Jun Feng, Zixin Wang, Zhentao Zhang +5
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in visual mathematical reasoning across various existing benchmarks. However, these benchmarks ar…
DeepShop: A Benchmark for Deep Research Shopping Agents
Yougang Lyu, Xiaoyu Zhang, Lingyong Yan +3
Web agents for online shopping have shown great promise in automating user interactions across e-commerce platforms. Benchmarks for assessing such agents do not reflect the complex…
Unifying Search and Recommendation with Dual-View Representation Learning in a Generative Paradigm
Jujia Zhao, Wenjie Wang, Chen Xu +3
Recommender systems and search engines serve as foundational elements of online platforms, with the former delivering information proactively and the latter enabling users to seek…
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs
Zixiao Wang, Duzhen Zhang, Ishita Agrawal +3
Previous approaches to persona simulation large language models (LLMs) have typically relied on learning basic biographical information, or using limited role-play dialogue dataset…
QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling
Yumeng Wang, Xiuying Chen, Suzan Verberne
In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This lead…
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Chengrui Huang, Zhengliang Shi, Yuntao Wen +4
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to en…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi, Shen Gao, Xiuyi Chen +7
Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively sele…