Beyond Accuracy: Alignment and Error Detection across Languages in the Bi-GSM8K Math-Teaching Benchmark (2026.findings-eacl)
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| Challenge: | Recent advances in LLMs have significantly improved mathematical problem-solving, with models like GPT-4 achieving human-level performance. |
| Approach: | They propose a bilingual English-Korean dataset enriched with teacher solutions, student solutions, and annotations marking students’ initial errors. |
| Outcome: | The proposed model achieves high agreement with human judgments and lower latency and resource usage than commercial APIs, demonstrating strong computational efficiency. |
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| Challenge: | Large language models (LLMs) achieve impressive performance on complex benchmarks yet sometimes fail on basic math reasoning. |
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