# On the Branching Bias of Syntax Extracted from Pre-trained Language Models

arXiv: https://arxiv.org/abs/2010.02448 · HTML: https://mlsanity.com/paper/2010.02448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Probing for Constituency Structure in Neural Language Models](https://mlsanity.com/paper/2204.06201/) (2022)
- [Emergent Word Order Universals from Cognitively-Motivated Language Models](https://mlsanity.com/paper/2402.12363/) (2024)
- [How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions](https://mlsanity.com/paper/2311.08287/) (2023)

## Abstract (arXiv)

Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing. However, this kind of methods may suffer from the branching bias issue, which will inflate the performances on languages with the same branch it biases to. In this work, we propose quantitatively measuring the branching bias by comparing the performance gap on a language and its reversed language, which is agnostic to both language models and extracting methods. Furthermore, we analyze the impacts of three factors on the branching bias, namely parsing algorithms, feature definitions, and language models. Experiments show that several existing works exhibit branching biases, and some implementations of these three factors can introduce the branching bias.