1 citations · 1 across the 5 of their papers we have counts for
5 papers
AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution
Zehao Wang, Yihan Zeng, Zidong Gong +5
Post-training via Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) is crucial for enhancing reasoning in Multimodal Large Language Models (MLLMs), yet existing paradigm…
Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector
Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao +5
Hallucination detection is a challenging task for large language models (LLMs), and existing studies heavily rely on powerful closed-source LLMs such as GPT-4. In this paper, we pr…
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
Chenxi Sun, Hongzhi Zhang, Zijia Lin +8
Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequen…
PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples
Zhuo Zhang, Jingyuan Zhang, Jintao Huang +7
Instruction tuning aligns large language models (LLMs) with human intentions but requires diverse, high-quality data that are difficult to collect in privacy-sensitive domains. Fed…
Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models
Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang +4
Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that L…