127 citations · 168 across the 38 of their papers we have counts for
4 papers · 2 filters
ToolLoop: Closed-Loop Tool-Use Data Synthesis via Decomposed Generation and Dynamic Self-Feedback
Min Zeng, Yuzhou Liu, Zhenyu Cao +5
High-quality tool-use data is critical for training language models to interact effectively with external tools. However, existing synthetic approaches typically follow a generate-…
VISA: Agentic Self-Evolving Data Synthesis for Multimodal Instruction Following
Min Zeng, Guanxin Tan, Libin Cen +5
Multimodal instruction-following models require training data that is accurate, diverse, verifiable, and challenging. Existing synthesis pipelines typically follow a one-pass gener…
Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling
Hongjian Zou, Yue Ge, Qi Ding +2
Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less predictable than that of text-on…
Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Prompts
Yujie Lin, Kunquan Li, Yixuan Liao +2
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. However, their outputs often exhibit social biases,…