Prompt-based Generation of Natural Language Explanations of Synthetic Lethality for Cancer Drug Discovery (2024.lrec-main)
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| Challenge: | Synthetic lethality (SL) is a genetic interaction where a single gene mutation allows cell survival, but simultaneous mutations in two genes lead to cell death. |
| Approach: | They propose a prompt-based pipeline for generating natural language explanations using a dataset derived from New Bing . |
| Outcome: | The proposed pipeline improves on existing biomedical language models in terms of text quality and explainability. |
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| Challenge: | Existing explanation methods rely on linear approximations, accentuating irrelevant input tokens. |
| Approach: | They propose a method that aligns the explanation process with the masked language modeling task of pretrained language models and leverages prompt-based learning to generate class-dependent explanations. |
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PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)
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| Challenge: | Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. |
| Approach: | They propose a model PromptGen which can automatically generate prompts conditional on the input sentence. |
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Unnatural language processing: How do language models handle machine-generated prompts? (2023.findings-emnlp)
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| Challenge: | Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are outperformed by automatically generated token sequences with no apparent meaning or syntactic structure. |
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Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)
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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
| Challenge: | Pre-trained models perform poorly with limited data and rare biomedical words. |
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JoPA: Explaining Large Language Model’s Generation via Joint Prompt Attribution (2025.acl-long)
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| Challenge: | Existing attempts to explain the entire language generation often treat input prompt texts independently, ignoring their combinatorial effects on the follow-up generation. |
| Approach: | They propose a framework for explaining how a few prompt texts collaboratively influences the LLM's complete generation. |
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DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)
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| Challenge: | Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization. |
| Approach: | They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization. |
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Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints (2023.findings-eacl)
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| Challenge: | Existing and potential applications of open-ended text generation are farreaching, spanning domains such as QA, story generation, open-end dialogue, and ChatGPT 1 . |
| Approach: | They propose a prompt-centric approach to analyzing and bounding the abilities of open-ended generative models by a set of structural and stylistic prompts. |
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Beyond Abstracts: A New Dataset, Prompt Design Strategy and Method for Biomedical Synthesis Generation (2024.acl-srw)
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| Challenge: | Existing methods to automate systematic reviews of papers are slow and incomplete . authors propose a new method to automating the systematic review process . |
| Approach: | They propose a method for automatic synthesis generation using a dataset and prompting-based method. |
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Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. |
| Approach: | They propose a method called controlled generation with Prompt Insertion that uses Large Language Models to explain the reasons for corrections in natural language. |
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MutantPrompt: Prompt Optimization via Mutation Under a Budget on Modest-sized LMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have revolutionized the way we learn and process information, but identifying optimal prompts remains a challenge for low-resource languages. |
| Approach: | They propose a framework that leverages multi-armed bandit algorithms to efficiently identify optimal prompts tailored to low-resource languages. |
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