Papers with o3
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated promising results across various domains, including the legal domain. |
| Approach: | They propose a benchmark to assess the ability of large language models to predict the legitimacy of additional tax penalties. |
| Outcome: | The proposed model is based on 100 Korean court precedents and 100 binary-choice questions. |
Implicit Reasoning in Transformers is Reasoning through Shortcuts (2025.findings-acl)
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| Challenge: | Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. |
| Approach: | They train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi- step tasks. |
| Outcome: | The proposed model performs better on multi-step tasks than the explicit reasoning model. |
OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation (2026.findings-acl)
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| Challenge: | Existing reward models perform suboptimal on held-out benchmarks, resulting in poor quality outputs. |
| Approach: | They propose a framework and a high-quality dataset to evaluate reward models . they define four key metrics to assess generation quality and develop a pipeline to evaluate outputs . |
| Outcome: | The proposed framework and dataset improves hallucination-free, comprehensive, reliable, and efficient open-ended long-context generation. |