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    <abstract>Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model‘s familiarity with the given prompt or instruction, which is typically estimated by its perplexity. However, finding the prompt with the lowest perplexity is challenging, given the enormous space of possible prompting phrases. In this paper, we propose monotonic paraphrasing (MonoPara), an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt (or instruction) rewriting, and a target LM (i.e. the prompt or instruction executor) that constrains the generation for lower perplexity. The ensemble decoding process can efficiently paraphrase the original prompt without altering its semantic meaning, while monotonically decrease the perplexity of each generation as calculated by the target LM. We explore in detail both greedy and search-based decoding as two alternative decoding schemes of MonoPara. Notably, MonoPara does not require any training and can monotonically lower the perplexity of the paraphrased prompt or instruction, leading to improved performance of zero-shot LM prompting as evaluated on a wide selection of tasks. In addition, MonoPara is also shown to effectively improve LMs’ generalization on perturbed and unseen task instructions.</abstract>
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%0 Conference Proceedings
%T Monotonic Paraphrasing Improves Generalization of Language Model Prompting
%A Liu, Qin
%A Wang, Fei
%A Xu, Nan
%A Yan, Tianyi Lorena
%A Meng, Tao
%A Chen, Muhao
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F liu-etal-2024-monotonic
%X Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model‘s familiarity with the given prompt or instruction, which is typically estimated by its perplexity. However, finding the prompt with the lowest perplexity is challenging, given the enormous space of possible prompting phrases. In this paper, we propose monotonic paraphrasing (MonoPara), an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt (or instruction) rewriting, and a target LM (i.e. the prompt or instruction executor) that constrains the generation for lower perplexity. The ensemble decoding process can efficiently paraphrase the original prompt without altering its semantic meaning, while monotonically decrease the perplexity of each generation as calculated by the target LM. We explore in detail both greedy and search-based decoding as two alternative decoding schemes of MonoPara. Notably, MonoPara does not require any training and can monotonically lower the perplexity of the paraphrased prompt or instruction, leading to improved performance of zero-shot LM prompting as evaluated on a wide selection of tasks. In addition, MonoPara is also shown to effectively improve LMs’ generalization on perturbed and unseen task instructions.
%R 10.18653/v1/2024.findings-emnlp.576
%U https://aclanthology.org/2024.findings-emnlp.576/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.576
%P 9861-9877

Markdown (Informal)

[Monotonic Paraphrasing Improves Generalization of Language Model Prompting](https://aclanthology.org/2024.findings-emnlp.576/) (Liu et al., Findings 2024)

• Monotonic Paraphrasing Improves Generalization of Language Model Prompting (Liu et al., Findings 2024)

ACL

• Qin Liu, Fei Wang, Nan Xu, Tianyi Lorena Yan, Tao Meng, and Muhao Chen. 2024. Monotonic Paraphrasing Improves Generalization of Language Model Prompting. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9861–9877, Miami, Florida, USA. Association for Computational Linguistics.