BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes
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| Format: | Preprint |
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2025
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| _version_ | 1866913043839975424 |
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| author | Huang, Baichuan Balashankar, Ananth Aminifar, Amir |
| author_facet | Huang, Baichuan Balashankar, Ananth Aminifar, Amir |
| contents | Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competitive performance, particularly in low-data regimes. However, the link between fine-tuning different bias terms (i.e., $\boldsymbol{b}_q$, $\boldsymbol{b}_k$, and $\boldsymbol{b}_v$ in the query, key, or value projections) and downstream performance remains largely unclear to date. In this paper, we investigate the link between fine-tuning $\boldsymbol{b}_q$, $\boldsymbol{b}_k$, and $\boldsymbol{b}_v$ with the performance of the downstream task. Our key finding is that directly fine-tuning $\boldsymbol{b}_v$ generally leads to higher downstream performance in low-data regimes, in comparison to $\boldsymbol{b}_q$ and $\boldsymbol{b}_k$. We extensively evaluate this unique property across a wide range of LLMs spanning encoder-only and decoder-only architectures up to 6.7B parameters (including bias-free LLMs). Our results provide strong evidence for the effectiveness of directly fine-tuning $\boldsymbol{b}_v$ across various downstream tasks. The implementation code is available at https://github.com/whubaichuan/BEFT. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes Huang, Baichuan Balashankar, Ananth Aminifar, Amir Computation and Language Artificial Intelligence Machine Learning Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competitive performance, particularly in low-data regimes. However, the link between fine-tuning different bias terms (i.e., $\boldsymbol{b}_q$, $\boldsymbol{b}_k$, and $\boldsymbol{b}_v$ in the query, key, or value projections) and downstream performance remains largely unclear to date. In this paper, we investigate the link between fine-tuning $\boldsymbol{b}_q$, $\boldsymbol{b}_k$, and $\boldsymbol{b}_v$ with the performance of the downstream task. Our key finding is that directly fine-tuning $\boldsymbol{b}_v$ generally leads to higher downstream performance in low-data regimes, in comparison to $\boldsymbol{b}_q$ and $\boldsymbol{b}_k$. We extensively evaluate this unique property across a wide range of LLMs spanning encoder-only and decoder-only architectures up to 6.7B parameters (including bias-free LLMs). Our results provide strong evidence for the effectiveness of directly fine-tuning $\boldsymbol{b}_v$ across various downstream tasks. The implementation code is available at https://github.com/whubaichuan/BEFT. |
| title | BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.15974 |