1 code implementation • 14 Oct 2024 • Jianqiao Lu, Yingjia Wan, Yinya Huang, Jing Xiong, Zhengying Liu, Zhijiang Guo
To address this, we introduce \textsc{FormalAlign}, the first automated framework designed for evaluating the alignment between natural and formal languages in autoformalization.
no code implementations • 9 Oct 2024 • Zeyu Cao, Cheng Zhang, Pedro Gimenes, Jianqiao Lu, Jianyi Cheng, Yiren Zhao
Post-training quantization of Large Language Models (LLMs) has proven effective in reducing the computational requirements for running inference on these models.
no code implementations • 4 Oct 2024 • Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Lingpeng Kong, Ngai Wong
By grouping layers and heads based on their uncertainty, UNComp adaptively compresses both the hidden states and the KV cache.
no code implementations • 3 Oct 2024 • Zixuan Li, Jing Xiong, Fanghua Ye, Chuanyang Zheng, Xun Wu, Jianqiao Lu, Zhongwei Wan, Xiaodan Liang, Chengming Li, Zhenan Sun, Lingpeng Kong, Ngai Wong
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks.
1 code implementation • 20 Jun 2024 • Xiaohan Lin, Qingxing Cao, Yinya Huang, Haiming Wang, Jianqiao Lu, Zhengying Liu, Linqi Song, Xiaodan Liang
In this paper, we propose FVEL, an interactive Formal Verification Environment with LLMs.
no code implementations • 20 Jun 2024 • Zhongshen Zeng, Yinhong Liu, Yingjia Wan, Jingyao Li, Pengguang Chen, Jianbo Dai, Yuxuan Yao, Rongwu Xu, Zehan Qi, Wanru Zhao, Linling Shen, Jianqiao Lu, Haochen Tan, Yukang Chen, Hao Zhang, Zhan Shi, Bailin Wang, Zhijiang Guo, Jiaya Jia
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes.
2 code implementations • 4 Jun 2024 • Jianqiao Lu, Yingjia Wan, Zhengying Liu, Yinya Huang, Jing Xiong, Chengwu Liu, Jianhao Shen, Hui Jin, Jipeng Zhang, Haiming Wang, Zhicheng Yang, Jing Tang, Zhijiang Guo
Autoformalization, the conversion of natural language mathematics into formal languages, offers significant potential for advancing mathematical reasoning.
2 code implementations • 27 May 2024 • Jianqiao Lu, Zhiyang Dou, Hongru Wang, Zeyu Cao, Jianbo Dai, Yingjia Wan, Zhijiang Guo
\textsc{AutoPSV} begins by training a verification model on the correctness of final answers, enabling it to generate automatic process annotations.
1 code implementation • 23 May 2024 • Haiming Wang, Huajian Xin, Zhengying Liu, Wenda Li, Yinya Huang, Jianqiao Lu, Zhicheng Yang, Jing Tang, Jian Yin, Zhenguo Li, Xiaodan Liang
This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels.
1 code implementation • 19 May 2024 • Jianbo Dai, Jianqiao Lu, Yunlong Feng, Dong Huang, Guangtao Zeng, Rongju Ruan, Ming Cheng, Haochen Tan, Zhijiang Guo
Our study analyzed two common benchmarks, HumanEval and MBPP, and found that these might not thoroughly evaluate LLMs' code generation capacities due to limitations in quality, difficulty, and granularity.
1 code implementation • 30 Jan 2024 • Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
no code implementations • 28 Jan 2024 • Jianqiao Lu, Wanjun Zhong, YuFei Wang, Zhijiang Guo, Qi Zhu, Wenyong Huang, Yanlin Wang, Fei Mi, Baojun Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
no code implementations • 9 Oct 2023 • Jianqiao Lu, Wenyong Huang, Nianzu Zheng, Xingshan Zeng, Yu Ting Yeung, Xiao Chen
For SLU, LaSyn improves our E2E baseline by absolute 4. 1% for intent classification accuracy and 3. 8% for slot filling SLU-F1 on SLURP, and absolute 4. 49% and 2. 25% for exact match (EM) and EM-Tree accuracies on STOP respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
no code implementations • 1 Oct 2023 • Jianqiao Lu, Wanjun Zhong, Wenyong Huang, YuFei Wang, Qi Zhu, Fei Mi, Baojun Wang, Weichao Wang, Xingshan Zeng, Lifeng Shang, Xin Jiang, Qun Liu
SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.