Abstract
Language differs dramatically from context to context. To some degree, large language models like GPT-3 account for such variation by conditioning on strings of initial input text, or prompts. However, prompting can be ineffective when contexts are sparse, out-of-sample, or extra-textual. In this paper, we introduce the mixed-effects transformer (MET), a novel approach for learning hierarchically-structured prefixes— lightweight modules prepended to an input sequence— to account for structured variation in language use. Specifically, we show how the popular class of mixed-effects regression models may be extended to transformer-based architectures using a regularized prefix-tuning procedure with dropout. We evaluate this approach on several domain-adaptation benchmarks, finding that it learns contextual variation from minimal data while generalizing well to unseen contexts.- Anthology ID:
- 2022.emnlp-main.261
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3944–3954
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.261
- DOI:
- Cite (ACL):
- Julia White, Noah Goodman, and Robert Hawkins. 2022. Mixed-effects transformers for hierarchical adaptation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3944–3954, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Mixed-effects transformers for hierarchical adaptation (White et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.261.pdf