| Challenge: | Pre-trained language models have increased the performance of data-driven natural language processing (NLP) models on a wide variety of tasks. |
| Approach: | They propose a model-free approach to probing via prompting which formulates probing as a prompting task and combine pruning to analyze where the model stores the linguistic information in its architecture. |
| Outcome: | The proposed approach extracts information from pre-trained models while learning much less on its own. |
Similar Papers
Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found prompt-based probing evaluations inaccurate, inconsistent and unreliable. |
| Approach: | They propose to conduct debiasing via causal intervention to uncover biases in probing evaluations . authors argue that prompt-based probing is inaccurate, inconsistent and unreliable . |
| Outcome: | This paper examines the effectiveness of prompt-based probing in pretrained language models . it highlights critical biases which could induce biased results and conclusions . authors suggest rethinking criteria for evaluating better pretrained models based on such evaluations . |
A Study on Accessing Linguistic Information in Pre-Trained Language Models by Using Prompts (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to access linguistic information in pre-trained multilingual language models are difficult to use. |
| Approach: | They propose prompting and formulate linguistic tasks to test the LM's access to explicit grammar principles and find out what type of information can be obtained . |
| Outcome: | The proposed method can provide access to linguistic features in pre-trained models, but some are harder to capture . |
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
| Approach: | They examine the probing paradigm through a set of controlled synthetic tasks and show that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself. |
| Outcome: | The proposed model can encode linguistic properties above chance-level even when distributed in the data as random noise, reversing the interpretation of absolute claims on probing tasks. |
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020.acl-main)
Copied to clipboard
| Challenge: | Recent pre-trained language models achieve state-of-the-art performance for downstream NLP tasks. |
| Approach: | They propose a parameter-free probing technique for analyzing pre-trained language models . their method does not require direct supervision from probing tasks . |
| Outcome: | The proposed method improves on linguistically-uninformed baselines on pre-trained language models. |
Prompting Language Models for Linguistic Structure (2023.acl-long)
Copied to clipboard
| Challenge: | Existing prompting methods can test this hypothesis on autoregressive PLMs. |
| Approach: | They propose a structured prompting approach for linguistic structured prediction tasks that performs zero- and few-shot sequence tagging with autoregressive PLMs. |
| Outcome: | The proposed approach shows that the model can perform few-shot sequence tagging on part-of-speech taging, named entity recognition, and sentence chunking tasks. |
Probing Task-Oriented Dialogue Representation from Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Using pre-trained language models, we find out which model has the most informative representation for task-oriented dialogue tasks. |
| Approach: | They propose a supervised classifier probe and unsupervised mutual information probe to investigate the mutual dependence between a real clustering and a representation clustering. |
| Outcome: | The proposed model is a supervised classifier probe and unsupervised mutual information probe. |
ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to prob pre-trained language models (PLMs) lack readability and credibility. |
| Approach: | They propose a method to identify meaningful sentences to serve as prompts to assess the knowledge encoded within pre-trained language models (PLMs). |
| Outcome: | The proposed method achieves state-of-the-art on the current knowledge probing benchmark. |
Baked-in State Probing (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work shows language models trained on form can capture aspects of meaning without explicit state supervision. |
| Approach: | They propose to use probing to "bake" state knowledge into language models . they propose to probe for underlying world state knowledge via text prompts . |
| Outcome: | The proposed methods show that language models trained on form can capture the world state without state supervision. |
Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for factual probing can interpret the model’s prediction accuracy as a lower bound on the amount of factual information it encodes. |
| Approach: | They propose a method which directly optimizes in continuous embedding space and can predict an additional 6.4% of facts in the LAMA benchmark. |
| Outcome: | The proposed method outperforms the best previous prompt method by 6.4% on the LAMA benchmark. |
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge-enhanced pre-trained language models (PLMs) introduce redundant factual knowledge from knowledge bases and require complex modules. |
| Approach: | They propose a knowledge prompting-based PLM framework that incorporates factual knowledge into PLMs. |
| Outcome: | The proposed framework can be flexibly combined with existing mainstream PLMs. |