EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning (2024.naacl-short)
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| Challenge: | Language models are adopting inference-time prompting techniques such as zero-shot and few-shot prompting. |
| Approach: | They propose a prompting technique that prompts the model to rephrase its queries before answering them. |
| Outcome: | The proposed prompt improves zero-shot-CoT performance of code-davinci-002 by 5% . the proposed prompt is tailored for four scenarios in both zero- and few-shot settings . |
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SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data (2023.findings-emnlp)
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| Challenge: | Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. |
| Approach: | They propose a method to improve few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs) they propose 'SQlPrompt' which aims to diversify the SQL proposals during consistency selection with different prompt designs and foundation models. |
| Outcome: | The proposed method outperforms previous approaches for in-context learning with zero labeled data by a large margin, closing the gap with finetuning state-of-the-art with thousands of labeles. |
MetaPrompting: Learning to Learn Better Prompts (2022.coling-1)
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| Challenge: | Recent research on prompting moves from discrete tokens based "hard prompts" to continuous "soft prompts", which employ learnable vectors as pseudo prompt tokens and achieve better performance. |
| Approach: | They propose a generalized soft prompting method that uses model-agnostic meta-learning to find better initialization for soft prompts. |
| Outcome: | The proposed method improves on three datasets and brings new state-of-the-art performance. |
AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)
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| Challenge: | Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models . |
| Approach: | They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models. |
| Outcome: | The proposed method outperforms standard prompt-based methods in few-shot settings. |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
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| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
| Outcome: | The proposed method outperforms previous methods on diverse tasks. |
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification (2021.emnlp-main)
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| Challenge: | Recent studies show that prompts improve performance of large pre-trained language models for few-shot text classification. |
| Approach: | They propose a prompt-based framework for few-shot learning that captures cross-task transferable knowledge and uses two de-biasing techniques to make it more task-agnostic and unbiased . |
| Outcome: | The proposed framework outperforms strong baselines over multiple NLP tasks and datasets. |
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
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| Challenge: | Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation. |
| Approach: | They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework. |
| Outcome: | The proposed framework is easy to use and flexible enough to integrate with other frameworks. |
Batch Prompting: Efficient Inference with Large Language Model APIs (2023.emnlp-industry)
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| Challenge: | Performing inference on large volumes of samples can be computationally and financially costly. |
| Approach: | They propose a prompting approach that enables large language models to run inference in batches instead of one sample at a time. |
| Outcome: | The proposed prompting reduces both token and time costs while retaining downstream performance. |
Prompting-based Synthetic Data Generation for Few-Shot Question Answering (2024.lrec-main)
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| Challenge: | Language models have boosted the performance of Question Answering, but data annotation is costly. |
| Approach: | They propose to use large language models to improve Question Answering performance . they argue that domain-agnostic knowledge from LMs is sufficient to create a well-curated dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on few-shot Question Answering. |
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. |
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting (2024.findings-acl)
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| Challenge: | Existing prompting methods oversimplify time-series forecasting (TSF) time-Series data are ubiquitous across various domains, including public health, finance and energy. |
| Approach: | They propose a method for prompting off-the-shelf Large Language Models (LLMs) they decompose TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each . |
| Outcome: | The proposed approach decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. |