# Deepening Hidden Representations from Pre-trained Language Models

arXiv: https://arxiv.org/abs/1911.01940 · HTML: https://mlsanity.com/paper/1911.01940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal Documents](https://mlsanity.com/paper/2403.06872/) (2024)

## Abstract (arXiv)

Transformer-based pre-trained language models have proven to be effective for learning contextualized language representation. However, current approaches only take advantage of the output of the encoder's final layer when fine-tuning the downstream tasks. We argue that only taking single layer's output restricts the power of pre-trained representation. Thus we deepen the representation learned by the model by fusing the hidden representation in terms of an explicit HIdden Representation Extractor (HIRE), which automatically absorbs the complementary representation with respect to the output from the final layer. Utilizing RoBERTa as the backbone encoder, our proposed improvement over the pre-trained models is shown effective on multiple natural language understanding tasks and help our model rival with the state-of-the-art models on the GLUE benchmark.