29 citations · 46 across the 16 of their papers we have counts for
17 papers · 1 filter
AI Cosmos: An Agentic Infra for Situated Learning
AI Cosmos Team, Jinfeng Zhou, Yuxuan Chen +32
Large language models (LLMs) can improve task performance during learning, yet these gains do not necessarily reflect learners' ability to thereafter apply the knowledge and skills…
ScopeIF: Improving Scope-Aware Precise Instruction-Following in Large Language Models via Graded Reward Modeling
Bosi Wen, Yilin Niu, Xiaoying Ning +3
Precise instruction-following is a fundamental ability of large language models (LLMs), requiring their outputs to strictly satisfy objective constraints in input instructions. In…
MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding
Bosi Wen, Cunxiang Wang, Jiayi Gui +6
Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute, and utilize external tools…
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
Bosi Wen, Yilin Niu, Cunxiang Wang +5
Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback from judge models. However, the r…
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…
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…
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…
Training Language Model to Critique for Better Refinement
Tianshu Yu, Chao Xiang, Mingchuan Yang +8
Large language models (LLMs) have demonstrated remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. However, limit…
HPSS: Heuristic Prompting Strategy Search for LLM Evaluators
Bosi Wen, Pei Ke, Yufei Sun +6
Since the adoption of large language models (LLMs) for text evaluation has become increasingly prevalent in the field of natural language processing (NLP), a series of existing wor…
CharacterBench: Benchmarking Character Customization of Large Language Models
Jinfeng Zhou, Yongkang Huang, Bosi Wen +13
Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs' character c…
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Bosi Wen, Pei Ke, Xiaotao Gu +11
Instruction following is one of the fundamental capabilities of large language models (LLMs). As the ability of LLMs is constantly improving, they have been increasingly applied to…
ToMBench: Benchmarking Theory of Mind in Large Language Models
Zhuang Chen, Jincenzi Wu, Jinfeng Zhou +8
Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language model…
CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models
Jinfeng Zhou, Zhuang Chen, Dazhen Wan +14
In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM is designed for generating Chara…
AlignBench: Benchmarking Chinese Alignment of Large Language Models
Xiao Liu, Xuanyu Lei, Shengyuan Wang +15
Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, the effective evaluation of alignment for emerging Ch…
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation
Pei Ke, Bosi Wen, Zhuoer Feng +9
Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing…
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Hao Zhou, Pei Ke, Zheng Zhang +11
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue dat…