| Challenge: | Unlike previous methods, output2prompt only needs outputs of normal user queries. |
| Approach: | They propose a black-box method that extracts the model's prompt without accessing its logits and without adversarial or jailbreaking queries. |
| Outcome: | The proposed method extracts the prompt that generated the outputs without accessing the model's logits and without adversarial or jailbreaking queries. |
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| Challenge: | Existing methods rely on a large number of outputs for training and inference, and they can produce garbled text. |
| Approach: | They propose a training-free framework that reconstructs prompts using only a limited number of text outputs from a language model. |
| Outcome: | The proposed framework generates high-quality prompt recovery and more semantically and functionally aligned with the originals than current state-of-the-art methods. |
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts (2026.tacl-1)
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| Challenge: | Evaluating natural language generation systems is challenging due to the diversity of valid outputs. |
| Approach: | They propose an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions. |
| Outcome: | The proposed method requires only a single evaluation sample and eliminates manual prompt engineering. |
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)
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| Challenge: | Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations. |
| Approach: | They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch. |
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PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
| Approach: | They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs. |
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Prompt2Model: Generating Deployable Models from Natural Language Instructions (2023.emnlp-demo)
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| Challenge: | Large language models (LLMs) are a step backward from traditional special-purpose NLP models . they require extensive computational resources for deployment and can be gated behind APIs . |
| Approach: | They propose a general-purpose method that takes a natural language task description and uses it to train a special-purpose model. |
| Outcome: | The proposed method outperforms a strong LLM by 20% while being 700 times smaller. |
DORY: Deliberative Prompt Recovery for LLM (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are used for their groundbreaking performance across various tasks. |
| Approach: | They propose a method that leverages uncertainty to recover prompts accurately using a single LLM without external resources or models. |
| Outcome: | The proposed approach outperforms baselines across diverse LLMs and prompt benchmarks and establishes a new state-of-the-art record in prompt recovery tasks. |
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)
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| Challenge: | Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts . |
| Approach: | They propose a formalism and a taxonomy of known (and possible) jailbreaks . they propose generating a dataset of model outputs across 3700 jailbreak prompts a 'prompt' attack is a new attack popularly categorized as "prompting injection attacks" |
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Prompterator: Iterate Efficiently towards More Effective Prompts (2023.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) use a process known as prompting to solve arbitrary language tasks. prompting is a non-trivial task that requires experimentation in order to arrive at a prompt that solves a specific task. |
| Approach: | They propose a tool that helps users iterate over different potential prompts and choose the best performing one based on human feedback. |
| Outcome: | The proposed tool is open source and easily extensible. |
Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
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| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |
One-Shot is Enough: Consolidating Multi-Turn Attacks into Efficient Single-Turn Prompts for LLMs (2025.acl-long)
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| Challenge: | a novel framework for consolidating multi-turn adversarial “jailbreak” prompts into single-turn queries is presented in a journal of computational linguistics. |
| Approach: | They propose a framework for consolidating adversarial “jailbreak” prompts into single-turn queries. |
| Outcome: | The proposed framework outperforms the original multi-turn attacks by up to 17.5 % in absolute ASR . it reduces token usage by more than half on average, and provides a powerful tool for large-scale red-teaming . |