{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mixture-of-experts-using-tensor-products","title":"Mixture of Latent Experts Using Tensor Products","arxiv_id":"2405.16671","date":"2024-05-26","proceeding":null,"authors":["Zhan Su","Fengran Mo","Prayag Tiwari","Benyou Wang","Jian-Yun Nie","Jakob Grue Simonsen"],"abstract":"In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with one another, potentially leading to \\textit{negative transfer}. To mitigate this, we investigate if modular language models can facilitate positive transfer and systematic generalization. Specifically, we propose a novel modular language model (\\texttt{TensorPoly}), that balances parameter efficiency with nuanced routing methods. For \\textit{modules}, we reparameterize Low-Rank Adaptation (\\texttt{LoRA}) by employing an entangled tensor through the use of tensor product operations and name the resulting approach \\texttt{TLoRA}. For \\textit{routing function}, we tailor two innovative routing functions according to the granularity: \\texttt{TensorPoly-I} which directs to each rank within the entangled tensor while \\texttt{TensorPoly-II} offers a finer-grained routing approach targeting each order of the entangled tensor. The experimental results from the multi-task T0-benchmark demonstrate that: 1) all modular LMs surpass the corresponding dense approaches, highlighting the potential of modular language models to mitigate negative inference in multi-task learning and deliver superior outcomes. 2) \\texttt{TensorPoly-I} achieves higher parameter efficiency in adaptation and outperforms other modular LMs, which shows the potential of our approach in multi-task transfer learning.","url_abs":"https://arxiv.org/abs/2405.16671v2","url_pdf":"https://arxiv.org/pdf/2405.16671v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mixture-of-experts-using-tensor-products","repo_url":"https://github.com/microsoft/mttl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"systematic-generalization","task_name":"Systematic Generalization"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.16671","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}