Papers with model agnostic approach
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)
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Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. |
| Approach: | They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks. |
| Outcome: | The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT. |
Universal Sentence Representation Learning with Conditional Masked Language Model (2021.emnlp-main)
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| Challenge: | Existing methods to learn sentence representations on unlabeled corpora are difficult and expensive to obtain, making it hard to cover many domains and languages. |
| Approach: | They propose a method to train sentence representations on large unlabeled corpora by conditioning on the encoded vectors of adjacent sentences. |
| Outcome: | The proposed method outperforms existing models on SentEval and can be extended to a broad range of languages and domains. |