Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Authors: Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljacic
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that Quan TA significantly enhances commonsense reasoning, arithmetic reasoning, and scalability compared to traditional methods. |
| Researcher Affiliation | Academia | 1NSF AI Institute for Artificial Intelligence and Fundamental Interactions 2Department of Physics, Massachusetts Institute of Technology 3Department of EECS, Massachusetts Institute of Technology 4Department of Physics, Harvard University EMAIL |
| Pseudocode | Yes | torch.einsum("...abc,efbc,diaf,ghde->...ghi", x, T_3, T_2, T_1) torch.einsum("efbc,diaf,ghde->ghiabc", T_3, T_2, T_1) |
| Open Source Code | Yes | *https://github.com/quanta-fine-tuning/quanta |
| Open Datasets | Yes | We assess the general applicability of the low-rank hypothesis, we examine two datasets of varying difficulties: the RTE dataset [49], a classification task... and the DROP dataset [50], a generation task... |
| Dataset Splits | Yes | Instead, we create a validation set from the train set and optimize the hyperparameters on the validation set. |
| Hardware Specification | Yes | All the experiments are conducted on NVIDIA A100 GPUs with 80 GB memory. |
| Software Dependencies | No | The paper mentions 'torch.einsum' and 'opt_einsum' as libraries used, and notes the code is implemented using [54] and [68] as references. However, no specific version numbers for these software components or programming languages are provided. |
| Experiment Setup | Yes | In Table E.2, we show the hyperparameters used for the DROP experiments. Only Lo RA and Quan TA are applied to the 13and 70-billion-parameter LLa MA2 models. For the 13-billion-parameter model or smaller, only a single A100 GPU is used for fine-tuning. And for the 70-billion-parameter model, four A100 GPUs are used. |