3 citations · 8 across the 9 of their papers we have counts for
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cs.CL2024
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine
Xiaochen Wang, Junqing He, Liang Chen +5
Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answer…
cs.CL2024
FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering
Xiaochen Wang, Junqing He, Zhe yang +4
Large Language Models (LLMs) with chain-of-thought (COT) prompting have demonstrated impressive abilities on simple nature language inference tasks. However, they tend to perform p…
cs.CL2024★ 1 cited
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization
Xiangdi Meng, Damai Dai, Weiyao Luo +7
Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Rece…