Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong
| Challenge: | Recent years have witnessed a paradigm shift in natural language processing, driven by large language models such as GPT-3, PaLM, and Llama. |
| Approach: | They propose a strategy for role-play prompting and assess its performance under the zero-shot setting. |
| Outcome: | The proposed method outperforms the standard zero-shot prompting approach across 12 reasoning benchmarks. |
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Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
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| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
| Outcome: | The proposed prompting strategies outperform state-of-the-art prompting methods on KE benchmark datasets. |
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (2023.acl-long)
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| Challenge: | Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. |
| Approach: | They propose a plan-and-solve (PS) prompting that includes a few manual steps to generate reasoning steps and improves the quality of generated reasoning steps. |
| Outcome: | The proposed strategy outperforms Zero-shot-CoT on ten reasoning problems and has comparable performance to 8-shot CoT prompting on the math reasoning problem. |
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. |
| Approach: | They propose an approach that optimizes the zero-shot prompts for individual task instances following an innovative manner of "LLMs in the loop" their results show that PRomPTed outperforms naive zero- shot approaches and a strong baseline which refines the task output instead of the input prompt. |
| Outcome: | The proposed approach outperforms naive approaches and a strong baseline which refines the task output instead of the input prompt. |
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. |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance. |
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science (2024.lrec-main)
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Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song
| Challenge: | Existing instruction-tuned Large Language Models (LLMs) have impressive language understanding and the capacity to generate responses that follow specific prompts. |
| Approach: | They evaluate the zero-shot performance of two publicly accessible LLMs, ChatGPT and OpenAssistant, in the context of six Computational Social Science classification tasks. |
| Outcome: | The proposed LLMs perform better than state-of-the-art models on social science tasks. |
Better Zero-Shot Reasoning with Self-Adaptive Prompting (2023.findings-acl)
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| Challenge: | Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. |
| Approach: | They propose a new method that uses a set of examples from the LLM zero-shot outputs to improve performance. |
| Outcome: | The proposed method improves performance up to 15% compared to baselines and matches or exceeds few-shot baselines at a range of reasoning tasks. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination (2024.emnlp-main)
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| Challenge: | a series of investigations into an interesting phenomenon where performance increases in large language models when providing a prompt that causes and exploits hallucination. |
| Approach: | They propose a null-shot prompting approach that intentionally instructs LLMs to look at and utilize information from a nil section. |
| Outcome: | The proposed approach causes and exploits hallucination in large language models on a range of tasks including arithmetic reasoning, commonsense reasoning, and reading comprehension. |
Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
Universal Self-Adaptive Prompting (2023.emnlp-main)
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| Challenge: | a hallmark of modern large language models is their impressive general zero-shot and few-shot abilities . however, zero- shot performances are weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks. |
| Approach: | They propose an automatic prompt design approach specifically tailored for zero-shot learning that categorizes a possible NLP task into one of three possible task types and then uses a selector to select the most suitable queries and zero- shot model-generated responses as pseudo-demonstrations. |
| Outcome: | The proposed approach is able to generalize ICL to zero-shot learning tasks while also allowing for a more efficient and efficient prompt design. |