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cs.CL2025
Multi-turn Training with Basic Human Feedback Helps Little on LLM Reasoning
Qiang Liu, Wuganjing Song, Zhenzhou Lin +4
The reasoning capabilities of Large Language Models (LLMs) are typically developed through the single-turn reinforcement learning, whereas real-world applications often involve mul…
cs.CL2024★ 4 cited
On-Device Language Models: A Comprehensive Review
Jiajun Xu, Zhiyuan Li, Wei Chen +4
The advent of large language models (LLMs) revolutionized natural language processing applications, and running LLMs on edge devices has become increasingly attractive for reasons…
cs.CL2024
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
Qiaolong Cai, Zhaowei Wang, Shizhe Diao +2
With the increasing popularity of large language models (LLMs), reasoning on basic graph algorithm problems is an essential intermediate step in assessing their abilities to proces…