Abstract
Prior research endeavors of the ensemble Large Language Models (LLMs) achieved great success by employing an individual language model (LM) rank before the text generation. However, the use of an individual LM ranker faces two primary challenges: (1) The time-intensive nature of the ranking process, stemming from the comparisons between models; (2) The issue of error propagation arising from the separate ranking and generation models within the framework. In order to overcome these challenges, we propose a novel ensemble framework, namely Unified Ranking and Generation (URG). URG represents an end-to-end framework that jointly ranks the outputs of LLMs and generates fine-grained fusion results, via utilizing a dedicated cross-attention-based module and noise mitigation training against irrelevant information stemming from bad ranking results. Through extensive experimentation and evaluation, we demonstrate the efficiency and effectiveness of our framework in both the ranking and generation tasks. With the close coordination of the ranking and generation modules, our end-to-end framework achieves the state-of-the-art (SOTA) performance on these tasks, and exhibits substantial enhancements to any of the ensembled models.- Anthology ID:
- 2024.findings-acl.261
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4421–4434
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.261
- DOI:
- Cite (ACL):
- Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Ping Luo, and Yue Yu. 2024. URG: A Unified Ranking and Generation Method for Ensembling Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 4421–4434, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- URG: A Unified Ranking and Generation Method for Ensembling Language Models (Lv et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.261.pdf