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Cited by 22Topic ModelingInformation Retrieval and Search BehaviorNatural Language Processing Techniques

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RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models

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Chen, Shuhao, Jiang, Weisen, Lin, Baijiong, Kwok, James, Zhang, Yu·2024·Advances in Neural Information Processing Systems 37
DOI 10.52202/079017-2120
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Abstract

Recent works show that assembling multiple off-the-shelf large language models (LLMs) can harness their complementary abilities. To achieve this, routing is a promising method, which learns a router to select the most suitable LLM for each query. However, existing routing models are ineffective when multiple LLMs perform well for a query. To address this problem, in this paper, we propose a method called query-based Router by Dual Contrastive learning (RouterDC). The RouterDC model consists of an encoder and LLM embeddings, and we propose two contrastive learning losses to train the RouterDC model. Experimental results show that RouterDC is effective in assembling LLMs and largely outperforms individual top-performing LLMs as well as existing routing methods on both in-distribution (+2.76\%) and out-of-distribution (+1.90\%) tasks. Source code is available at https://github.com/shuhao02/RouterDC.

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  1. [4][4] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, 'A simple framework for contrastive learning of visual representations,' in International Conference on Machine Learning , 2020. -
  2. [5][5] Z. Chen, Y. Deng, H. Yuan, K. Ji, and Q. Gu, 'Self-play fine-tuning converts weak language models to strong language models,' in International Conference on Machine Learning , 2024. -
  3. [12][12] N. Gupta, H. Narasimhan, W. Jitkrittum, A. S. Rawat, A. K. Menon, and S. Kumar, 'Language model cascades: Token-level uncertainty and beyond,' in International Conference on Learning Representations , 2024. -
  4. [15][15] P. He, X. Liu, J. Gao, and W. Chen, 'DeBERTa: Decoding-enhanced BERT with disentangled attention,' in International Conference on Learning Representations , 2021. -
  5. [16][16] D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, 'Measuring massive multitask language understanding,' in International Conference on Learning Representations , 2021. -
  6. [17][17] D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, 'Measuring mathematical problem solving with the MATH dataset,' in Neural Information Processing Systems: Datasets and Benchmarks , 2021. -
  7. [21][21] G. Ilharco, M. T. Ribeiro, M. Wortsman, L. Schmidt, H. Hajishirzi, and A. Farhadi, 'Editing models with task arithmetic,' in International Conference on Learning Representations , 2023. -
  8. [22][22] G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave, 'Unsupervised dense information retrieval with contrastive learning,' Transactions on Machine Learning Research , 2022. -
  9. [25][25] X. Jin, X. Ren, D. Preotiuc-Pietro, and P. Cheng, 'Dataless knowledge fusion by merging weights of language models,' in International Conference on Learning Representations , 2023. -
  10. [26][26] S. Kullback and R. A. Leibler, 'On information and sufficiency,' The Annals of Mathematical Statistics , 1951. -
  11. [29][29] I. Loshchilov and F. Hutter, 'Decoupled weight decay regularization,' in International Conference on Learning Representations , 2019. -
  12. [32][32] J. MacQueen et al. , 'Some methods for classification and analysis of multivariate observations,' in Berkeley Symposium on Mathematical Statistics and Probability , 1967. -
  13. [35][35] L. Nie, Z. Ding, E. Hu, C. Jermaine, and S. Chaudhuri, 'Online cascade learning for efficient inference over streams,' in International Conference on Machine Learning , 2024. -
  14. [36][36] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, 'Learning transferable visual models from natural language supervision,' in International Conference on Machine Learning , 2021. -
  15. [45][45] L. Van der Maaten and G. Hinton, 'Visualizing data using t-SNE,' Journal of Machine Learning Research , 2008. -
  16. [47][47] H. Wang, F. M. Polo, Y. Sun, S. Kundu, E. Xing, and M. Yurochkin, 'Fusing models with complementary expertise,' in International Conference on Learning Representations , 2024. -
  17. [48][48] X. Wang, J. Wei, D. Schuurmans, Q. V. Le, E. H. Chi, S. Narang, A. Chowdhery, and D. Zhou, 'Selfconsistency improves chain of thought reasoning in language models,' in International Conference on Learning Representations , 2023. -
  18. [50][50] L. Yu, W. Jiang, H. Shi, J. Yu, Z. Liu, Y. Zhang, J. T. Kwok, Z. Li, A. Weller, and W. Liu, 'MetaMath: Bootstrap your own mathematical questions for large language models,' in International Conference on Learning Representations , 2024. -
  19. [51][51] M. Yue, J. Zhao, M. Zhang, L. Du, and Z. Yao, 'Large language model cascades with mixture of thought representations for cost-efficient reasoning,' in International Conference on Learning Representations , 2024. -

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Metadata

Authors
Chen, S., Jiang, W., Lin, B., Kwok, J., & Zhang, Y.
ORCID
Venue
Advances in Neural Information Processing Systems 37
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2024
Pages
66305-66328
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/079017-2120
OpenAlex
W4415797314
Cited by
22
References
0
Citations by year
2026: 19 · 2025: 3
FWCI
5.32
Type
conference paper
Language
en
Open access
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62076118, 62136005)
  • Shenzhen Fundamental Research Program (JCYJ20210324105000003)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
dual contrastive learning, contrastive learning

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