Papers with FFT
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have ignited a surge in demand for effective fine-tuning techniques . data labeling is notoriously time-consuming and expensive, hindering the development of sizable labeled datasets . |
| Approach: | They propose to use active learning to reduce labeling costs by minimizing label complexity . they find PEFT adapter modules have significant potential in low-resource settings . |
| Outcome: | The proposed model outperforms FFT in low-resource settings and shows that it yields more stable representations of early and middle layers than FFT. |
Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have shown that the choice of space for knowledge graph (KG) embeddings has significant effects on the performance of KG completion tasks. |
| Approach: | They propose to use the Fourier transform to convert between real and complex hyperbolic space to capture hierarchical patterns. |
| Outcome: | The proposed models outperform the baseline models for knowledge graph (KG) embeddings. |
Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a large computational cost for attention computation in large language models is a major obstacle . |
| Approach: | They propose a convolution-like structure for attention computation using convolution matrices . they then propose an efficient approximation method to approximate the attention matrix . |
| Outcome: | The proposed method achieves nearly linear time complexity in n1+o(1) time. |
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing transformer models are computationally demanding and prohibitively costly for long sequences due to the quadratic complexity of its selfattention module. |
| Approach: | They propose a transformer-based model that inherits weights from large pretrained models by removing redundancies in hidden sequences using the ready-made Fast Fourier Transform operator. |
| Outcome: | The proposed model outperforms the standard BART model on the long-range modeling benchmark LRA with significant improvements in speed and space. |
HFT: Half Fine-Tuning for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) with one or more fine-tuning phases can unlock various capabilities, but can be catastrophic forgetting during sequential training. |
| Approach: | They propose a method to regularly reset partial parameters to mitigate forgetting issues by using half fine-tuning instead of full fine-uning. |
| Outcome: | The proposed approach reduces the risk of catastrophic forgetting during training and the parametric knowledge lost during training may be overwhelmed by incoming training data. |
CoLA: Collaborative Low-Rank Adaptation (2025.findings-acl)
Copied to clipboard
| Challenge: | The scaling law of Large Language Models (LLMs) reveals diminishing return on performance as model scale increases. |
| Approach: | They propose a more flexible LoRA architecture with an efficient initialization scheme . they propose combining three collaborative strategies to enhance performance . |
| Outcome: | The proposed model outperforms existing methods in low-sample scenarios. |
zFLoRA: Zero-Latency Fused Low-Rank Adapters (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly deployed with task-specific adapters catering to multiple downstream applications. |
| Approach: | They propose a low-latency fused low-rank adapter that introduces zero latency overhead on top of the base model. |
| Outcome: | The proposed adapter reduces the inference time of the model by 2.5x . the proposed adapters are tested on 18 different tasks on different platforms . |
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces. |
| Approach: | They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters. |
| Outcome: | The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios. |
Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026.acl-long)
Copied to clipboard
| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) is an alternative to Full-Parameter Fine-tuning, but its effectiveness on complex tasks such as reasoning and instruction-following remains unclear. |
| Approach: | They propose to use PEFT to reduce the number of trainable parameters while freezing the weights of LLMs. |
| Outcome: | The proposed methods perform well on standard tasks, but weaknesses on complex and adversarial settings call for new directions beyond current paradigms. |