| Challenge: | Recent work demonstrates that Large Language Models are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks. |
| Approach: | They revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. |
| Outcome: | The proposed approach outperforms manual prompting on six different downstream tasks and a larger range of K-shot learning settings. |
Similar Papers
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2022.findings-acl)
Copied to clipboard
| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
| Approach: | They propose to fine tune masked language models with training examples and task descriptions to reduce prompt engineering by using null prompts. |
| Outcome: | The proposed prompts can be used to improve few-shot learning by finetuning only the bias terms while updating only 0.1% of the parameters. |
Do Prompt-Based Models Really Understand the Meaning of Their Prompts? (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. |
| Approach: | They experiment with 30 prompts manually written for natural language inference (NLI) they find that models can learn just as fast with many irrelevant or pathologically misleading prompts . |
| Outcome: | The proposed model can learn as fast with irrelevant or pathologically misleading prompts as with instructively “good” prompts. |
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
| Outcome: | The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks. |
Reframing Instructional Prompts to GPTk’s Language (2022.findings-acl)
Copied to clipboard
| Challenge: | Using reframing techniques, we find that instructional prompts are easier to follow for Language Models (LMs) |
| Approach: | They propose reframing techniques for manual reformulation of prompts into more effective ones . they compare performance of LMs prompted with reframed instructions on 12 NLP tasks . |
| Outcome: | The reframing techniques used for prompt reformulation improve performance on 12 tasks . the techniques boost performance on LMs with different sizes compared with original prompts . |
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance (2024.findings-acl)
Copied to clipboard
| 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. |
The language of prompting: What linguistic properties make a prompt successful? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that pretraining and instruction-tuned LLMs can achieve impressive performance on a multitude of tasks. |
| Approach: | They propose to use a standard for prompting research to better understand linguistic properties of LLMs. |
| Outcome: | The proposed standard would improve the performance of pre-trained and instruction-tuned LLMs on a multitude of tasks. |
Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. |
| Approach: | They propose to revisit the optimization by prompting approach for small-scale LLMs . they suggest future prompting engineering to consider both model capabilities and computational costs . |
| Outcome: | The proposed approach shows limited effectiveness in small-scale LLMs, with limited inference capabilities constraining optimization ability. |
Prompting open-source and commercial language models for grammatical error correction of English learner text (2024.findings-acl)
Copied to clipboard
Christopher Davis, Andrew Caines, O Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
| Challenge: | Recent advances in generative AI have enabled us to prompt large language models (LLMs) to produce texts which are fluent and grammatical. |
| Approach: | They evaluate model performance by measuring their performance on established benchmarks. |
| Outcome: | The proposed models outperform supervised English GEC models on fluency correction benchmarks and commercial LLMs on edit benchmarks. |
Universal Self-Adaptive Prompting (2023.emnlp-main)
Copied to clipboard
| 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. |
Continued Pretraining for Better Zero- and Few-Shot Promptability (2022.emnlp-main)
Copied to clipboard
Zhaofeng Wu, Robert L Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz Beltagy
| Challenge: | Recent language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. |
| Approach: | They propose to use a dedicated pretraining stage to improve promptability in zero-shot settings and few-shot tuning. |
| Outcome: | The proposed method improves promptability in zero- and few-shot settings, while the existing method yields subpar performance. |