2 citations · 3 across the 16 of their papers we have counts for
13 papers · 1 filter
SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization
Qi Zhang, Zhaopeng Feng, Xiaonan Shi +8
Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, cur…
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents
Zhaopeng Feng, Liangcai Su, Zhen Zhang +16
As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…
Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Xiaotian Zhang, Yuan Wang, Zhaopeng Feng +6
Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. D…
CP-Router: An Uncertainty-Aware Router Between LLM and LRM
Jiayuan Su, Fulin Lin, Zhaopeng Feng +7
Recent advances in Large Reasoning Models (LRMs) have significantly improved long-chain reasoning capabilities over Large Language Models (LLMs). However, LRMs often produce unnece…
MT: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Zhaopeng Feng, Yupu Liang, Shaosheng Cao +7
Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cross-lingual information access, an…
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning
Zhaopeng Feng, Shaosheng Cao, Jiahan Ren +7
Large-scale reinforcement learning (RL) methods have proven highly effective in enhancing the reasoning abilities of large language models (LLMs), particularly for tasks with verif…
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling
Zhaopeng Feng, Jiahan Ren, Jiayuan Su +3
Process reward models (PRMs) have shown success in complex reasoning tasks for large language models (LLMs). However, their application to machine translation (MT) remains underexp…
M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation
Zhaopeng Feng, Jiayuan Su, Jiamei Zheng +5
Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments. However, in the fiel…
EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization
Zhaopeng Feng, Zijie Meng, Zuozhu Liu
Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instr…
Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level
Zhaopeng Feng, Ruizhe Chen, Yan Zhang +2
General-purpose Large Language Models (LLMs) like GPT-4 have achieved remarkable advancements in machine translation (MT) by leveraging extensive web content. On the other hand, tr…
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
Zhaopeng Feng, Yan Zhang, Hao Li +7
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, careful evaluations by human reveal that the translations produced by LLMs still…
DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs
Zijie Meng, Yan Zhang, Zhaopeng Feng +1
Large language models (LLMs) have shown impressive performance in reasoning benchmarks with the emergence of Chain-of-Thought (CoT), particularly in multi-choice question (MCQ). Ho…
How Well Do Text Embedding Models Understand Syntax?
Yan Zhang, Zhaopeng Feng, Zhiyang Teng +2
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability o…