| 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. |
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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. |
On the Importance of Data Size in Probing Fine-tuned Models (2022.findings-acl)
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| Challenge: | Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. |
| Approach: | They propose to investigate the reasons behind the effectiveness of fine-tuning by examining the impact of data size on the extent of encoded linguistic knowledge. |
| Outcome: | The proposed probes show that the size of the training data affects the recoverability of the changes made to the model’s linguistic knowledge. |
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models? (2022.coling-1)
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| Challenge: | grammatical knowledge is encoded in large pre-trained language models (LMs) this is done through supervised classification tasks to predict the grammamatical properties of a span using only the token representations coming from the LM encoder. |
| Approach: | They propose to use a supervised 'edge probing' task to detect grammatical knowledge in large pre-trained language models (LMs) this is done by encoding grammamatical properties using only token representations coming from the LM encoder. |
| Outcome: | The proposed model performs well when fine-tuned or in adversarial situations where the model is forced to learn wrong correlations. |
Modular and Parameter-Efficient Fine-Tuning for NLP Models (2022.emnlp-tutorials)
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| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
| Approach: | They provide an overview of parameter-efficient fine-tuning methods and highlight similarities and differences . they highlight benefits and usage scenarios of a neglected property of parameter efficient models . |
| Outcome: | This paper provides an overview of parameter-efficient fine-tuning methods . it highlights similarities and differences by presenting them in a unified view . |
On the data requirements of probing (2022.findings-acl)
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| Challenge: | Existing methods to probe neural networks are expensive and require large datasets. |
| Approach: | They propose a method to estimate the required number of data samples in probing datasets . they use a classification task to encode a text with a deep neural network . |
| Outcome: | The proposed method estimates the required number of data samples in two probing configurations and proves it is statistically powerful. |
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. |
Predicting Performance for Natural Language Processing Tasks (2020.acl-main)
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| Challenge: | Natural language processing (NLP) is a vast field, with a wide variety of tasks, languages, and domains. |
| Approach: | They build regression models to predict evaluation score of an NLP experiment . they find that their models can produce meaningful predictions over unseen languages . |
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Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood. |
| Approach: | They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs. |
| Outcome: | The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks. |
On the Transformation of Latent Space in Fine-Tuned NLP Models (2022.emnlp-main)
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| Challenge: | a large body of work analyzed the knowledge learned within representations of pre-trained models. |
| Approach: | They use hierarchical clustering to discover latent concepts in representational space . they compare pre-trained and fine-tuned models and perform a thorough analysis . |
| Outcome: | The results show that the model space evolves towards task-specific concepts whereas the lower layers retain generic concepts acquired in the pre-trained model. |
A Closer Look at How Fine-tuning Changes BERT (2022.acl-long)
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| Challenge: | Pre-trained contextualized representations are used to analyze information in NLP . however, how fine-tuning changes the underlying embedding space is less studied . |
| Approach: | They propose to use probing techniques to analyze how fine-tuning changes the embedding space of pre-trained contextualized representations. |
| Outcome: | The proposed model improves classification performance by increasing the distances between examples associated with different labels. |