---
title: "Beyond Accuracy — paper by Park et al. (2026)"
description: "Strong math-solving ability in LLMs does not mean they can teach. 95% accuracy. 43 references."
authors: ["Jieun Park", "KyungTae Lim", "Joon-ho Lim"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.85/"
doi: "10.18653/v1/2026.findings-eacl.85"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/park-2026-beyond-accuracy-alignment-error-detection/"
---
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# Beyond Accuracy: Alignment and Error Detection across Languages in the Bi-GSM8K Math-Teaching Benchmark

**Authors:** Jieun Park, KyungTae Lim and Joon-ho Lim · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.85/) · [PDF](https://aclanthology.org/2026.findings-eacl.85.pdf) · [DOI 10.18653/v1/2026.findings-eacl.85](https://doi.org/10.18653/v1/2026.findings-eacl.85) · [OpenAlex](https://openalex.org/W7140124275)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Strong math-solving ability in LLMs does not mean they can teach. Tutoring requires comparing a learner's reasoning with a reference solution and locating where the learner first went wrong. To measure these skills, the authors built Bi-GSM8K, a Korean–English benchmark derived from GSM8K. Mathematics education experts adapted the problems to the Korean curriculum and wrote step-by-step teacher solutions. They also produced deliberately flawed "student" solutions, each labelled with its first error. The Korean data was then translated into English with GPT-4o.

Two tasks are defined:
- **GTA:** step alignment between student and teacher solutions, scored by language-model similarity functions combined with the Needleman–Wunsch (NW) algorithm.
- **SED:** detecting the first error in a student solution.

Alignment scores agree closely with human ratings in both languages. Open-source models run faster and cheaper than commercial APIs. In error detection they trail GPT-4o by about ten points. This suggests that affordable, near-real-time tutoring feedback is feasible, including for Korean, which has fewer LLM training resources than English.

## Key points
- **Dataset:** Bi-GSM8K has 7,985 JSON entries. Each entry holds curriculum metadata, the problem, the original GSM8K `solution`, a stepwise `correct_solution`, an expert-simulated `error_solution`, and the location of the first error.
- **Alignment quality:** agreement with human judgments reached Pearson 0.89 and Spearman 0.88 for English, and Pearson 0.89 and Spearman 0.87 for Korean. BERTScore with bert-large-uncased plus NW performed on par with commercial systems.
- **Error detection:** GPT-4o reached roughly 95% accuracy, while the best open-source model reached about 86%.
- **Language effects:** results differed between the English and Korean subsets, which the authors attribute to linguistic properties and the makeup of training data.
- **Limitations:**
  - Coverage is limited to elementary-level problems.
  - Student solutions are simulated rather than collected from real learners.
  - The system has not been tested in classrooms.
  - Alignment works at sentence level, even though a single sentence can contain several solution steps.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Jieun Park, KyungTae Lim and Joon-ho Lim. 2026. Beyond Accuracy: Alignment and Error Detection across Languages in the Bi-GSM8K Math-Teaching Benchmark. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{park2026beyond,
  title = {{Beyond Accuracy: Alignment and Error Detection across Languages in the Bi-GSM8K Math-Teaching Benchmark}},
  author = {Jieun Park, KyungTae Lim and Joon-ho Lim},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.85},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.85}
}
```

- [← previous Beyond Accuracy: A Structured Error Analysis of Multilingual LLMs on Marathi Script Variation and Syntax](https://md.research.elamur.ai/free/en/papers/patil-2026-beyond-accuracy-structured-error-analysis/)
- [next → Beyond Benchmark Accuracy: Robustness Evaluation of Hinglish Sentiment Models](https://md.research.elamur.ai/free/en/papers/rahul-2026-beyond-benchmark-accuracy-robustness-evaluation/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/23/)

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*Updated 1 November 2026*