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20222026
most citedUnbiased Math Word Problems Benchmark for Mitigating Solving Bias

9 citations · 11 across the 19 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval

Yifan Song, Xingjian Tao, Zhicheng Yang +2

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recent lightweight approaches reduce…

cs.AI2026

Accordion-Thinking: Self-Regulated Step Summaries for Efficient and Readable LLM Reasoning

Zhicheng Yang, Zhijiang Guo, Yinya Huang +5

Scaling test-time compute via long Chain-of-Thought unlocks remarkable gains in reasoning capabilities, yet it faces practical limits due to the linear growth of KV cache and quadr…

cs.AI2024

Proving Theorems Recursively

Haiming Wang, Huajian Xin, Zhengying Liu +8

Recent advances in automated theorem proving leverages language models to explore expanded search spaces by step-by-step proof generation. However, such approaches are usually base…

cs.AI2023

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations

Zhicheng Yang, Yinya Huang, Jing Xiong +4

Large Language Models prompting, such as using in-context demonstrations, is a mainstream technique for invoking LLMs to perform high-performance and solid complex reasoning (e.g.,…

cs.AI2022★ 9 cited

Unbiased Math Word Problems Benchmark for Mitigating Solving Bias

Zhicheng Yang, Jinghui Qin, Jiaqi Chen +1

In this paper, we revisit the solving bias when evaluating models on current Math Word Problem (MWP) benchmarks. However, current solvers exist solving bias which consists of data…

cs.AI2022★ 1 cited

LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning

Zhicheng Yang, Jinghui Qin, Jiaqi Chen +2

Recently, deep learning models have made great progress in MWP solving on answer accuracy. However, they are uninterpretable since they mainly rely on shallow heuristics to achieve…