Papers with accuracy rate
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) and multi-modal models (MMs) have demonstrated remarkable capabilities in problem-solving, but their proficiency in tackling geometry math problems has not been thoroughly evaluated. |
| Approach: | They propose a benchmark to evaluate the performance of large language models and multi-modal models in solving geometry math problems. |
| Outcome: | The proposed model achieves 55.67% accuracy on main subset but only 6.00% accuracy on hard subset. |
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text (2020.emnlp-main)
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| Challenge: | MRC has achieved significant progress on the open domain in recent years due to large-scale pre-trained language models. |
| Approach: | They propose a machine reading comprehension model which exploits structural medical knowledge and reference medical plain text to improve the exam's accuracy. |
| Outcome: | The proposed model outperforms existing models with a large margin and passes the exam with 61.8% accuracy rate on the test set. |
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
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| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
| Approach: | They propose to use LLMs as automatic post-editors rather than direct translators to improve BLEU and COMET performance. |
| Outcome: | The proposed approach improves BLEU but COMET performance compared to in-context learning. |