Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv Computation and Language

Oct 27

Towards a Learnable Retriever for Textual Graph Question Answering

arXiv:2610.33511v1 Announce Type: new Abstract: Textual graph question answering (GraphQA) and graph-based retrieval-augmented generation (GraphRAG) have gained increasing attention as a way to groun...

By Kaifeng Jin, Yuchen Yan, Zhichen Zeng, Xiao Lin, Lihui Liu, Yinglong Xia, Hanghang Tong

llms rag nlp fine-tuning

arXiv Computation and Language

Oct 27

RoleHear: Pseudo-Labeling for Semi-Supervised Role-Attributed Streaming Speech Recognition

arXiv:2610.33765v1 Announce Type: new Abstract: Streaming automatic speech recognition transcribes at low latency but produces an unattributed word stream, while real-time applications such as custom...

By Zefang Liu, Chenyang Zhu, Sangwoo Cho, Xujun Peng, Rongqing Huang, Shi-Xiong Zhang, Sambit Sahu

llms fine-tuning multimodal

arXiv Computation and Language

Oct 27

Task Format as a Factor in Adaptation: A Controlled Comparison of LoRA and Full Fine-Tuning for Prompt-Safety Classification

arXiv:2610.33813v1 Announce Type: new Abstract: Fine-tuning a language model for classification involves many design decisions. Comparisons of low-rank adaptation (LoRA) with full fine-tuning are com...

By John Alderete, Srirakshith Betageri, Jennifer Flynn, Alex Greenhalgh, Josh Rubin, Yoshita Varma Annam

llms fine-tuning safety

arXiv Computation and Language

Oct 27

"High-Quality" Data for Early-Stage Instruction Tuning: An Empirical Study of Data Quality Metrics

arXiv:2610.33969v1 Announce Type: new Abstract: "Data quality" is widely invoked in instruction tuning, but the term is often underspecified. We study it as an operational property by asking which si...

By Vladimir Necula, David Gvadzabia, Sofia Serrano

llms fine-tuning data-engineering

arXiv Computation and Language

Oct 27

The Geometry of Emotion Representations Across Scale, Depth, and Instruction Tuning

arXiv:2610.35411v1 Announce Type: new Abstract: We measure the emotion geometry of six LLMs spanning Google's Gemma 2 family. Using an extraction pipeline established by Sofroniew et al. (2026) on Cl...

By Adam Hollowell

llms rag fine-tuning

arXiv Computation and Language

Oct 27

Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR

arXiv:2610.35846v1 Announce Type: new Abstract: Expanding multilingual ASR systems to new languages is challenging when adaptation must preserve previously supported languages without replaying sourc...

By Yuchen Zhang, Haralambos Mouratidis, Ravi Shekhar

llms fine-tuning multimodal

arXiv Computation and Language

Oct 27

Mechanistic Insights into LoRA: Layer Sparsity for Adaptive Fine-Tuning via Path Patching

arXiv:2610.34756v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has emerged as the dominant paradigm for parameter-efficient fine-tuning of large language models. Standard all-layer LoRA...

By Fei Zuo, Yizhou Huang, Kezhi Wang

llms fine-tuning efficiency benchmarks safety

arXiv Computation and Language

Oct 27

KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues

arXiv:2509.21856v2 Announce Type: replace Abstract: Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. How...

By Junhao Chen, Yu Huang, Siyuan Li, Rui Yao, Hanqian Li, Hanyu Zhang, Jungang Li, Jian Chen, Bowen Wang, Xuming Hu

llms rag nlp fine-tuning benchmarks

arXiv Computation and Language

Oct 27

PriFT: Guiding Supervised Fine-Tuning with Pretrained Priors

arXiv:2606.09396v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement...

By Ke Wang, Shuangqi Li, Mathieu Salzmann, Pascal Frossard

reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Oct 27

Can Multi-modal (reasoning) LLMs detect document manipulation?

arXiv:2508.11021v2 Announce Type: replace-cross Abstract: Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechan...

By Zisheng Liang, Kidus Zewde, Rudra Pratap Singh, Disha Patil, Zexi Chen, Jiayu Xue, Yao Yao, Yifei Chen, Qinzhe Liu, Simiao Ren

llms robotics fine-tuning multimodal benchmarks

arXiv Computation and Language

Oct 27

Dummy Backdoor as a Defense: Removing Unknown Backdoors via Shared Internal Mechanisms for Generative LLMs

arXiv:2606.11648v2 Announce Type: replace-cross Abstract: Backdoor attacks pose a serious threat to the safety and reliability of Large Language Models (LLMs), as they cause models to behave normally...

By Kazuki Iwahana, Masaru Matsubayashi, Takuma Koyama, Toshiki Shibahara, Kenichiro Ominato, Akira Ito

llms fine-tuning safety

arXiv Computer Vision

Oct 27

KINESYS: Test-Time Adaptation for Reference-Based Effect Editing in Images

arXiv:2610.33292v1 Announce Type: new Abstract: Visual effects are often easier to demonstrate via reference than to describe in words. We study effect-reference editing, where a reference image show...

By Noah Frahm, Luchao Qi, Haoze Li, Dan B Goldman, Sambit Bhattacharya, Roni Sengupta

fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 27

CADonatello: Towards Real-World Multi-Part 3D CAD Reconstruction

arXiv:2610.33575v1 Announce Type: new Abstract: Existing methods for CAD reconstruction from 3D scans achieve strong performance on standard benchmarks, yet their effectiveness often degrades in real...

By Ahmet Serdar Karadeniz, Dimitrios Mallis, Parsa Akbari Chianeh, Kseniya Cherenkova, Anis Kacem, Djamila Aouada

computer-vision fine-tuning benchmarks

arXiv Computer Vision

Oct 27

LENS: Likelihood Evaluation and Neighbor Smoothing for Attribute-Based Person Retrieval on Unseen Cameras

arXiv:2610.33641v1 Announce Type: new Abstract: Attribute-based person retrieval ranks surveillance images from a binary query of semantic attributes, but mean Average Attribute Degree of Match (mADM...

By Nguyen Dang Duy Tan, Vo Thanh Nguyen, Linh T. T. Tran, Huynh Nguyen Phat

fine-tuning benchmarks

arXiv Computer Vision

Oct 27

Learning Discriminative Prototypes via Boundary Repulsion MixUp for Object Re-Identification

arXiv:2610.34305v1 Announce Type: new Abstract: Object re-identification requires embeddings that are robust to intra-class variation in viewpoint, illumination, and occlusion, yet preserve sharp sep...

By Athena Psalta, Vasileios Tsironis, Konstantinos Karantzalos

rag fine-tuning benchmarks

arXiv Computer Vision

Oct 27

Toward Clinical Translation of Deep Learning-Based Intracranial Super-Resolution 4D Flow MRI Using Multi-Cohort Transfer Learning

arXiv:2610.35396v1 Announce Type: new Abstract: We evaluated synthetic pretraining and multi-cohort transfer learning for deep learning-based super-resolution of intracranial 4D Flow MRI across heter...

By Oliver Welin Odeback, Susanne Schnell, Patrick Winter, Johan Lundberg, Fredrik Piehl, Tobias Granberg, Alexander Fyrdahl, David Marlevi

fine-tuning

arXiv Computer Vision

Oct 27

Model-Aware Generation of Safety-Critical Scenes for Robust End-to-End Driving via Distilled Planner Surrogates

arXiv:2610.35448v1 Announce Type: cross Abstract: End-to-end driving models perform well on standard benchmarks but collide frequently in safety-critical scenarios. We present a failure-mode analysis...

By Khaled M. Seyam (University of Stuttgart, Germany), Julian Wiederer (Mercedes-Benz AG, Germany), Samar Fathalah (University of Stuttgart, Germany), Markus Braun (Mercedes-Benz AG, Germany), Bin Yang (University of Stuttgart, Germany)

fine-tuning benchmarks safety

arXiv Computer Vision

Oct 27

GRASP: Guided Residual Adapters with Sample-wise Partitioning

arXiv:2512.01675v3 Announce Type: replace Abstract: Text-to-image flow matching transformers degrade sharply in long-tail settings: tail-class outputs collapse in fidelity and diversity, limiting the...

By Felix N\"utzel, Mischa Dombrowski, Bernhard Kainz

llms diffusion fine-tuning safety

arXiv Computer Vision

Oct 27

ViT-AdaLA: Adapting Vision Transformers with Linear Attention

arXiv:2603.16063v2 Announce Type: replace Abstract: Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from...

By Yifan Li, Seunghyun Yoon, Viet Dac Lai, Franck Dernoncourt, Jason Kuen, Yu Kong, Trung Bui

llms computer-vision fine-tuning benchmarks safety

arXiv Computer Vision

Oct 27

Masked and Predictive Self-Supervised Pretraining for Brain MRI: Effects of Objective Design on Disease Detection

arXiv:2606.13315v2 Announce Type: replace Abstract: MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretrainin...

By Esra Erg\"un, Hersh Chandarana, Dan Sodickson, Gozde Unal

rag computer-vision fine-tuning safety

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