CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs’ Mathematical Reasoning Capabilities (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent large language models have shown indications of mathematical reasoning ability on competition-level problems. |
| Approach: | They propose a benchmark dataset to enable such analyses using large language models. |
| Outcome: | The proposed model performs better with concepts and hints than with the best model, but it is difficult to verify. |
Similar Papers
Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)
Copied to clipboard
Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen
| Challenge: | Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem. |
| Approach: | They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces. |
| Outcome: | The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems. |
Can Large Language Models Win the International Mathematical Games? (2025.emnlp-main)
Copied to clipboard
Alessio Cocchieri, Luca Ragazzi, Giuseppe Tagliavini, Lorenzo Tordi, Antonella Carbonaro, Gianluca Moro
| Challenge: | Recent advances in large language models (LLMs) have demonstrated strong mathematical reasoning abilities, even in visual contexts. |
| Approach: | They propose a benchmark of 2,183 high-quality mathematical problems in an open-ended format that enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. |
| Outcome: | The new benchmark spans seven age groups and a skill-based taxonomy and enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. |
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)
Copied to clipboard
Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai
| Challenge: | Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation. |
| Approach: | They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs. |
| Outcome: | The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization. |
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated impressive performance across various mathematical reasoning benchmarks. |
| Approach: | They introduce an adversarial grade school math dataset and explore whether LLMs can be more robust when questions are slightly changed. |
| Outcome: | The proposed method generates and verifies each intermediate thought based on its reasoning goal and calculation result. |
Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
Copied to clipboard
| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
| Approach: | They examine the evolution of Large Language Models (LLMs) for solving mathematical problems . they examine the spectrum of LLM-oriented techniques proposed for solving math problems - and their challenges . |
| Outcome: | The survey examines the spectrum of proposed LLM-oriented techniques in solving math problems. |
Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing evaluations focus on final accuracy, neglecting the critical aspect of reasoning capabilities. |
| Approach: | They propose to evaluate LLMs’ abilities to detect and correct reasoning mistakes by using rule-based methods and smaller language models. |
| Outcome: | The proposed model outperforms existing models such as GPT-4o and GPT4 in both accuracy and accuracy, but lacks data contamination and memorization concerns. |
Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past decade. |
| Approach: | They propose a benchmark dataset for evaluating the problem solving abilities of large language models (LLMs) they curate 515 challenging problems from the highly competitive IIT JEE-Advanced exam. |
| Outcome: | The proposed model performs better on open-source and proprietary models than the current model, but with techniques like self-consistency, self-refinement and chain-of-thought prompting. |
FANS: Formal Answer Selection for LLM Natural Language Math Reasoning Using Lean4 (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing frameworks that use Lean4 to enhance LLMs' NL reasoning abilities have been controversial in the field of math reasoning. |
| Approach: | They propose a framework that utilizes Lean4 to enhance LLMs’ NL math reasoning ability by generating a Lean 4 theorem statement and a proof-generating LLM. |
| Outcome: | The proposed framework improves LLMs' NL math reasoning ability by 2% across several math benchmarks and higher further based on reward models or in subfields such as algebra and number theory. |
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing image instruction fine-tuning datasets do not fully exploit visual information to enhance multimodal reasoning capabilities of Large language models (LLMs). |
| Approach: | They propose a LLaVA-based model fine-tuned with MathV360K to bridge this gap by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs. |
| Outcome: | The proposed model improves the multimodal reasoning capabilities of LLaVA-1.5 and demonstrates enhanced generalizability on the MMMU benchmark. |
Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) achieve impressive performance on complex benchmarks yet sometimes fail on basic math reasoning. |
| Approach: | They propose a benchmark to evaluate the efficiency of reasoning in large language models . they formalize the accuracy-verbosity tradeoff and introduce the overthinking score . |
| Outcome: | The proposed model performs well on complex benchmarks but fails on basic math reasoning . the proposed model generates 18 more tokens while achieving lower accuracy . |