activity
20242026
most citedShopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2026

SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans

Hansi Zeng, Zoey Li, Yifan Gao +7

Research Agents enable models to gather information from the web using tools to answer user queries, requiring them to dynamically interleave internal reasoning with tool use. Whil…

cs.CL2026

Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning

Fengran Mo, Yifan Gao, Sha Li +7

Large Language Models (LLMs) have become a popular interface for human-AI interaction, supporting information seeking and task assistance through natural, multi-turn dialogue. To r…

cs.CL2025

UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations

Fengran Mo, Yifan Gao, Chuan Meng +9

The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing c…

cs.LG20242 cited

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

Yilun Jin, Zheng Li, Chenwei Zhang +19

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are com…

cs.CL2024

RNR: Teaching Large Language Models to Follow Roles and Rules

Kuan Wang, Alexander Bukharin, Haoming Jiang +9

Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models t…