Tootfinder

BEFT: Bias-Efficient Fine-Tuning of Language Models
Baichuan Huang, Ananth Balashankar, Amir Aminifar
https://arxiv.org/abs/2509.15974 https://arxiv.org/pd…

BEFT: Bias-Efficient Fine-Tuning of Language Models
Fine-tuning all-bias-terms stands out among various parameter-efficient fine-tuning (PEFT) techniques, owing to its out-of-the-box usability and competitive performance, especially in low-data regimes. Bias-only fine-tuning has the potential for unprecedented parameter efficiency. However, the link between fine-tuning different bias terms (i.e., bias terms in the query, key, or value projections) and downstream performance remains unclear. The existing approaches, e.g., based on the magnitude o…

Model fine-tuning without compromising information access controls.
"Copilot Tuning" attaches fine-tuning datasets to LLMs while respecting your permissions. See how:
youtu.be/6udJzJAyT5I
#CopilotTuning #copilot

The Indian Pulsar Timing Array Data Release 2: I. Dataset and Timing Analysis
Prerna Rana, Pratik Tarafdar, Nobleson K, Churchil Dwivedi, Bhal Chandra Joshi, Debabrata Deb, Sushovan Mondal, M. A. Krishnakumar, Adya Shukla, Jaikhomba Singha, Himanshu Grover, Hemanga Tahbildar, Abhimanyu Susobhanan, Mayuresh Surnis, Shantanu Desai, Neelam Dhanda Batra, Aman Srivastava, Vinay Bharambe, Jibin Jose, Vaishnavi Vyasraj, Shebin Jose Jacob, Amarnath, Manpreet Singh, Zenia Zuraiq, Sarbartha Sen…

The Indian Pulsar Timing Array Data Release 2: I. Dataset and Timing Analysis
The Indian Pulsar Timing Array (InPTA) employs unique features of the upgraded Giant Metrewave Radio Telescope (uGMRT) to monitor dozens of the International Pulsar Timing Array (IPTA) millisecond pulsars (MSPs), simultaneously in the 300-500 MHz and the 1260-1460 MHz bands. This dual-band approach ensures that any frequency-dependent delays are accurately characterized, significantly improving the timing precision for pulsar observations, which is crucial for pulsar timing arrays. We present d…

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions
Yongju Jia, Jiarui Ma, Xiangxian Li, Baiqiao Zhang, Xianhui Cao, Juan Liu, Yulong Bian
https://arxiv.org/abs/2508.15688

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions
Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive capability in visual tasks, but their fine-tuning often suffers from bias in class-imbalanced scene. Recent works have introduced large language models (LLMs) to enhance VLM fine-tuning with supplementing semantic information. However, they often overlook inherent class imbalance in VLMs' pre-training, which may lead to bias accumulation in downstream tasks. To address this problem, this paper proposes a Multi…

Language-Guided Tuning: Enhancing Numeric Optimization with Textual Feedback
Yuxing Lu, Yucheng Hu, Nan Sun, Xukai Zhao
https://arxiv.org/abs/2508.15757 https://

Language-Guided Tuning: Enhancing Numeric Optimization with Textual Feedback
Configuration optimization remains a critical bottleneck in machine learning, requiring coordinated tuning across model architecture, training strategy, feature engineering, and hyperparameters. Traditional approaches treat these dimensions independently and lack interpretability, while recent automated methods struggle with dynamic adaptability and semantic reasoning about optimization decisions. We introduce Language-Guided Tuning (LGT), a novel framework that employs multi-agent Large Langua…

Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
Marko Tuononen, Heikki Penttinen, Ville Hautam\"aki
https://arxiv.org/abs/2509.15950 https://

Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
We present the first use of influence functions for deep learning-based wireless receivers. Applied to DeepRx, a fully convolutional receiver, influence analysis reveals which training samples drive bit predictions, enabling targeted fine-tuning of poorly performing cases. We show that loss-relative influence with capacity-like binary cross-entropy loss and first-order updates on beneficial samples most consistently improves bit error rate toward genie-aided performance, outperforming random fi…

Transfer learning optimization based on evolutionary selective fine tuning
Jacinto Colan, Ana Davila, Yasuhisa Hasegawa
https://arxiv.org/abs/2508.15367 https://

Transfer learning optimization based on evolutionary selective fine tuning
Deep learning has shown substantial progress in image analysis. However, the computational demands of large, fully trained models remain a consideration. Transfer learning offers a strategy for adapting pre-trained models to new tasks. Traditional fine-tuning often involves updating all model parameters, which can potentially lead to overfitting and higher computational costs. This paper introduces BioTune, an evolutionary adaptive fine-tuning technique that selectively fine-tunes layers to enh…

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Ke Wang, Wenning Wei, Yan Deng, Lei He, Sheng Zhao
https://arxiv.org/abs/2509.15701 https://

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Automatic Pronunciation Assessment (APA) is critical for Computer-Assisted Language Learning (CALL), requiring evaluation across multiple granularities and aspects. Large Multimodal Models (LMMs) present new opportunities for APA, but their effectiveness in fine-grained assessment remains uncertain. This work investigates fine-tuning LMMs for APA using the Speechocean762 dataset and a private corpus. Fine-tuning significantly outperforms zero-shot settings and achieves competitive results on si…

Global Regulation and Excitation via Attention Tuning for Stereo Matching
Jiahao Li, Xinhong Chen, Zhengmin Jiang, Qian Zhou, Yung-Hui Li, Jianping Wang
https://arxiv.org/abs/2509.15891

Global Regulation and Excitation via Attention Tuning for Stereo Matching
Stereo matching achieves significant progress with iterative algorithms like RAFT-Stereo and IGEV-Stereo. However, these methods struggle in ill-posed regions with occlusions, textureless, or repetitive patterns, due to a lack of global context and geometric information for effective iterative refinement. To enable the existing iterative approaches to incorporate global context, we propose the Global Regulation and Excitation via Attention Tuning (GREAT) framework which encompasses three attent…

A method for improving multilingual quality and diversity of instruction fine-tuning datasets
Chunguang Zhao, Yilun Liu, Pufan Zeng, Yuanchang Luo, Shimin Tao, Minggui He, Weibin Meng, Song Xu, Ziang Chen, Chen Liu, Hongxia Ma, Li Zhang, Boxing Chen, Daimeng Wei
https://arxiv.org/abs/2509.15549

A method for improving multilingual quality and diversity of instruction fine-tuning datasets
Multilingual Instruction Fine-Tuning (IFT) is essential for enabling large language models (LLMs) to generalize effectively across diverse linguistic and cultural contexts. However, the scarcity of high-quality multilingual training data and corresponding building method remains a critical bottleneck. While data selection has shown promise in English settings, existing methods often fail to generalize across languages due to reliance on simplistic heuristics or language-specific assumptions. In…