| Challenge: | Current approaches to natural language processing rely on fixed artifacts such as language models . current studies have focused on how these models acquire and demonstrate knowledge . |
| Approach: | They apply probing techniques to examine how language models acquire knowledge . they aim to inform future work on more efficient pretraining and understanding dependencies . |
| Outcome: | The proposed model learns linguistic abstractions, factual and commonsense knowledge, and reasoning abilities fast, stably, and robustly across domains. |
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Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020.acl-main)
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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman
| Challenge: | Unsupervised pretraining has recently pushed the state of the art on many natural language understanding tasks. |
| Approach: | They perform a large-scale survey on a pretrained RoBERTa model with 110 intermediate-target task combinations and 25 probing tasks to reveal the specific skills that drive transfer. |
| Outcome: | The proposed model is trained on 110 intermediate-target task combinations and compared with 25 probing tasks to reveal the specific skills that drive transfer. |
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
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| 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. |
Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021.naacl-main)
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| 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. |
Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing (2021.naacl-main)
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| Challenge: | a study of neural language models shows that syntactic probes do not properly isolate syntax. |
| Approach: | They show that syntactic probes do not properly isolate syntax . they train two probes trained on normal data and find they perform worse . |
| Outcome: | The proposed method outperforms the baseline models on the most popular models, but their lead is reduced by 53%. |
Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models (2023.findings-emnlp)
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| Challenge: | Pre-trained language models are trained on vast unlabeled data, rich in world knowledge. |
| Approach: | They propose a categorization scheme for factual probing methods based on how inputs, outputs and probed PLMs are adapted . they synthesize insights about knowledge retention and prompt optimization in PLM models and analyze obstacles to adopting them as knowledge bases . |
| Outcome: | The proposed method synthesizes insights about knowledge retention and prompt optimization in PLMs, analyzes obstacles to adopting them as knowledge bases and outline directions for future work. |
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study (2024.lrec-main)
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| Challenge: | Existing studies have focused on enhancing the factualness of large language models using context knowledge. |
| Approach: | They propose to use ChatGPT to construct probing datasets that provide diverse and coherent evidence corresponding to various facts. |
| Outcome: | The proposed model can encode knowledge across different layers, and it is compared with existing models. |
What Does Parameter-free Probing Really Uncover? (2024.acl-short)
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| Challenge: | Probing large language models (LLMs) has been criticized for using pre-defined label-laden target labels. |
| Approach: | They extend a parameter-free probing technique called perturbed masking applied to BERT to examine the relationship between UD and BERT. |
| Outcome: | The proposed method is compared to the UD formalism for English and shows that it lacks correlations with linguistic theory. |
Baked-in State Probing (2022.findings-emnlp)
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| 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. |
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers (2020.findings-emnlp)
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| Challenge: | linguistic knowledge encoded in pre-trained contextual embeddings is poorly understood . fine-tuning can be used to investigate the representations of pre-train models . |
| Approach: | They propose to investigate fine-tuning of contextualized embedding models through sentence-level probing. |
| Outcome: | The proposed method improves probing accuracy for three pre-trained models. |
Predicting Fine-Tuning Performance with Probing (2022.emnlp-main)
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| Challenge: | Large-scale neural models have recently demonstrated impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. |
| Approach: | They propose to use probing to extract a proxy signal widely used in model development to predict fine-tuning performance. |
| Outcome: | The proposed method predicts fine-tuning performance with errors 40% - 80% smaller than baselines. |