1 citations · 1 across the 7 of their papers we have counts for
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Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation
Yanzhi Tian, Cunxiang Wang, Zeming Liu +5
Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Network Services, literature etc.…
IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
Bosi Wen, Yilin Niu, Cunxiang Wang +6
Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imposed in input instructions. Nume…
MVSS: A Unified Framework for Multi-View Structured Survey Generation
Yinqi Liu, Yueqi Zhu, Yongkang Zhang +7
Scientific surveys require not only summarizing large bodies of literature, but also organizing them into clear and coherent conceptual structures. However, existing automatic surv…
RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen +4
Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train an…
RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +9
Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack t…
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv +167
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…