2 citations · 2 across the 21 of their papers we have counts for
12 papers · 1 filter
From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning
Chao Chen, Chengzu Li, Zhiwei Li +2
Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically inf…
When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs
Zhichao Yang, Caiqi Zhang, Ruihan Yang +3
Calibration evaluates whether a model confidence aligns with its empirical accuracy. Existing studies often compare the calibration of different large language models using global…
Confidence Estimation for LLMs in Multi-turn Interactions
Caiqi Zhang, Ruihan Yang, Xiaochen Zhu +5
While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research overwhelmingly focuses on single-turn settings.…
Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Ej Zhou, Caiqi Zhang, Tiancheng Hu +4
Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large Language Models (LLMs). However, t…
11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis
Chengzu Li, Wenshan Wu, Huanyu Zhang +6
For human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large la…
Enriching Patent Claim Generation with European Patent Dataset
Lekang Jiang, Chengzu Li, Stephan Goetz
Drafting patent claims is time-intensive, costly, and requires professional skill. Therefore, researchers have investigated large language models (LLMs) to assist inventors in writ…