134 citations · 189 across the 22 of their papers we have counts for
22 papers
AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction
Ziqiang Cui, Han Shi, Bowei He +8
Multi-Token Prediction (MTP) has emerged as an effective paradigm that augments a shared Large Language Model backbone with auxiliary heads, training the model to predict several f…
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4
Chengwu Liu, Yichun Yin, Ye Yuan +7
Most ATP benchmarks embed the final answer within the formal statement -- a convention we call "Easy Mode" -- a design that simplifies the task relative to what human competitors f…
Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers
Yusheng Zhao, Hourun Li, Bohan Wu +5
The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales quadratically with the sequence…
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification
Chengwu Liu, Ye Yuan, Yichun Yin +7
Chain-of-Thought (CoT) prompting has become the de facto method to elicit reasoning capabilities from large language models (LLMs). However, to mitigate hallucinations in CoT that…
Preparing Lessons for Progressive Training on Language Models
Yu Pan, Ye Yuan, Yichun Yin +6
The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes. Prior…
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Jing Xiong, Jianhao Shen, Ye Yuan +11
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models. However, current ATP benchma…
Reusing Pretrained Models by Multi-linear Operators for Efficient Training
Yu Pan, Ye Yuan, Yichun Yin +4
Training large models from scratch usually costs a substantial amount of resources. Towards this problem, recent studies such as bert2BERT and LiGO have reused small pretrained mod…
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
Chengwu Liu, Jianhao Shen, Huajian Xin +9
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems. Designed t…
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
Siheng Li, Cheng Yang, Yichun Yin +6
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stym…
NewsDialogues: Towards Proactive News Grounded Conversation
Siheng Li, Yichun Yin, Cheng Yang +7
Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce…
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Zhongwei Wan, Yichun Yin, Wei Zhang +5
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs…
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Yufei Huang, Yujia Qin, Huadong Wang +4
Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of t…
bert2BERT: Towards Reusable Pretrained Language Models
Cheng Chen, Yichun Yin, Lifeng Shang +7
In recent years, researchers tend to pre-train ever-larger language models to explore the upper limit of deep models. However, large language model pre-training costs intensive com…
Generate & Rank: A Multi-task Framework for Math Word Problems
Jianhao Shen, Yichun Yin, Lin Li +4
Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-seque…
Integrating Regular Expressions with Neural Networks via DFA
Shaobo Li, Qun Liu, Xin Jiang +5
Human-designed rules are widely used to build industry applications. However, it is infeasible to maintain thousands of such hand-crafted rules. So it is very important to integrat…
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
Yichun Yin, Cheng Chen, Lifeng Shang +3
Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the h…
Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation
Cheng Chen, Yichun Yin, Lifeng Shang +4
Task-agnostic knowledge distillation, a teacher-student framework, has been proved effective for BERT compression. Although achieving promising results on NLP tasks, it requires en…
LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation
Xiaoqi Jiao, Yichun Yin, Lifeng Shang +5
The multilingual pre-trained language models (e.g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks. However, these mod…
Improving Task-Agnostic BERT Distillation with Layer Mapping Search
Xiaoqi Jiao, Huating Chang, Yichun Yin +6
Knowledge distillation (KD) which transfers the knowledge from a large teacher model to a small student model, has been widely used to compress the BERT model recently. Besides the…
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Wei Zhang, Lu Hou, Yichun Yin +4
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks.However, these models are both computation and memory…
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
Yichun Yin, Chenguang Wang, Ming Zhang
Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, w…
TinyBERT: Distilling BERT for Natural Language Understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang +5
Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually c…