Papers with prompt methods
FPT: Feature Prompt Tuning for Few-shot Readability Assessment (2024.naacl-long)
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| Challenge: | Prompt-based methods lack crucial linguistic knowledge for readability assessment tasks such as word length, sentence length, and usage of different difficulty-level words. |
| Approach: | They propose a new prompt-based tuning framework that incorporates linguistic knowledge and a loss function to calibrate the similarity ranking order between categories. |
| Outcome: | The proposed framework outperforms the large language model gpt-3.5-turbo-16k in most cases. |
Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021.naacl-main)
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| Challenge: | Existing methods for factual probing can interpret the model’s prediction accuracy as a lower bound on the amount of factual information it encodes. |
| Approach: | They propose a method which directly optimizes in continuous embedding space and can predict an additional 6.4% of facts in the LAMA benchmark. |
| Outcome: | The proposed method outperforms the best previous prompt method by 6.4% on the LAMA benchmark. |
EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (2024.lrec-main)
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| Challenge: | Existing methods for Emotion-cause pair extraction (ECPE) do not distinguish between the emotion-caused pairs that belong to different types of emotions, limiting their applicability. |
| Approach: | They propose an Emotion-cause pair extraction method which integrates the implicit knowledge of cause clauses into a prompt template and extends the emotion labels to categories with an external emotion word base. |
| Outcome: | The proposed method extracts all potential emotion clauses and corresponding cause clauses from unannotated documents. |
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024.lrec-main)
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| Challenge: | Multimodal semantic understanding is crucial for developing machines capable of interpreting complex interplay of text and visual information. |
| Approach: | They propose a multi-modal soft prompt framework that integrates three experts of soft prompts . they propose sarcasm detection and sentiment analysis tasks that are critical for few-shot learning . |
| Outcome: | The proposed model outperforms the 8.2B model InstructBLIP with 2% parameters . it significantly outperformed other prompt methods on VLMs or task-specific methods . |
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities (2025.emnlp-main)
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| Challenge: | Existing research has explored model-driven strategies for prompt optimization, but these methods suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability. |
| Approach: | They propose a framework that optimizes and generates role-playing prompts by limiting the prompt search space to role-player scenarios. |
| Outcome: | The proposed framework matches and surpasses existing prompt optimization methods in terms of performance. |