5 papers
Rethinking Supervision Granularity: Segment-Level Learning for LLM-Based Theorem Proving
Shuo Xu, Jiakun Zhang, Junyu Lai +2
Automated theorem proving with large language models in Lean 4 is commonly approached through either step-level tactic prediction with tree search or whole-proof generation. These…
LLM-based Automated Theorem Proving Hinges on Scalable Synthetic Data Generation
Junyu Lai, Jiakun Zhang, Shuo Xu +6
Recent advancements in large language models (LLMs) have sparked considerable interest in automated theorem proving and a prominent line of research integrates stepwise LLM-based p…
LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation
Hao Gao, Jingyue Wang, Wenyang Fang +4
Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to prov…
Executing Arithmetic: Fine-Tuning Large Language Models as Turing Machines
Junyu Lai, Jiahe Xu, Yao Yang +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing and reasoning tasks. However, their performance in the fou…
MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models
Jingwei Xu, Junyu Lai, Yunpeng Huang
The pretrain+fine-tune paradigm is foundational for deploying large language models (LLMs) across various downstream applications. Within this framework, Low-Rank Adaptation (LoRA)…