1 citations · 2 across the 9 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.AI2024
GraphIC: A Graph-Based In-Context Example Retrieval Model for Multi-Step Reasoning
Jiale Fu, Yaqing Wang, Simeng Han +2
In-context learning (ICL) enhances large language models (LLMs) by incorporating demonstration examples, yet its effectiveness heavily depends on the quality of selected examples.…
cs.CL2024
Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with Chain-of-Problems
Stephen Miner, Yoshiki Takashima, Simeng Han +4
Benchmarks are critical for measuring Large Language Model (LLM) reasoning capabilities. Some benchmarks have even become the de facto indicator of such capabilities. However, as L…
cs.CL2024★ 1 cited
Optimizing Language Model's Reasoning Abilities with Weak Supervision
Yongqi Tong, Sizhe Wang, Dawei Li +6
While Large Language Models (LLMs) have demonstrated proficiency in handling complex queries, much of the past work has depended on extensively annotated datasets by human experts.…