AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process. |
| Approach: | They propose a method to create prompts for a diverse set of tasks using a gradient-guided search. |
| Outcome: | The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning. |
Similar Papers
How Can We Know What Language Models Know? (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent work examines knowledge contained in language models by having the LM fill in the blanks of prompts such as “Obama is a __ by profession”. |
| Approach: | They propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. |
| Outcome: | The proposed methods improve accuracy from 31.1% to 39.6% on the LAMA benchmark for extracting relational knowledge from LMs. |
AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)
Copied to clipboard
| 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. |
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
Copied to clipboard
| 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. |
PromptExplainer: Explaining Language Models through Prompt-based Learning (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing explanation methods rely on linear approximations, accentuating irrelevant input tokens. |
| Approach: | They propose a method that aligns the explanation process with the masked language modeling task of pretrained language models and leverages prompt-based learning to generate class-dependent explanations. |
| Outcome: | Extensive experiments show that PromptExplainer outperforms state-of-the-art explanation methods. |
Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts (2025.coling-main)
Copied to clipboard
| Challenge: | Experimental results show that Large Language Models can generate rule-based data in long contexts without following all specified rules. |
| Approach: | They propose a novel prompting strategy Multi-Lingual Prompt which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods on public datasets across various tasks, with a specific case study in text-to-MIP instances. |
PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. |
| Approach: | They propose a model PromptGen which can automatically generate prompts conditional on the input sentence. |
| Outcome: | The proposed model outperforms baseline models on the knowledge probing LAMA benchmark. |
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021.naacl-main)
Copied to clipboard
| Challenge: | Pretrained language models retain factual knowledge that can be extracted with a sentential prompt. |
| Approach: | They propose to learn prompts by gradient descent, either fine-tuning prompts or starting from random initialization. |
| Outcome: | The proposed approach outperforms existing methods on English LMs and tasks. |
Demystifying optimized prompts in language models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Modern language models (LMs) are not robust to out-of-distribution inputs. |
| Approach: | They investigate the composition of machine generated (“optimized”) prompts and the mechanisms by which LMs parse and build predictions from them. |
| Outcome: | The proposed prompts are primarily composed of punctuation and noun tokens, which are more rare in the training data. |
SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple. |
| Approach: | They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree. |
| Outcome: | The proposed method exploits the syntactic knowledge embedded in PLMs and achieves favorable results on three benchmark datasets. |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
Copied to clipboard
| 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. |