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 Machine Learning

Jul 28

Transfer learning for conflict and duplicate detection in software requirement pairs

arXiv:2301. 03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects.

By Garima Malik, Savas Yildirim, Mucahit Cevik

llms nlp fine-tuning

arXiv Machine Learning

Jul 28

MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

arXiv:2604. 13897v2 Announce Type: replace Abstract: We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs.

By Adam Lahouari, Shen Ai, Jihye Han, Jillian Hoffstadt, Philipp Hoellmer, Charlotte Infante, Pulkita Jain, Sangram Kadam, Maya M. Martirossyan, Amara McCune, Hypatia Newton, Shlok J. Paul, Willmor Pena, Jonathan Raghoonanan, Sumon Sahu, Oliver Tan, Andrea Vergara, Jutta Rogal, Mark E. Tuckerman

fine-tuning benchmarks

arXiv AI

Jul 28

Traceable LLM Reasoning for Fake-Order Fraud Detection

arXiv:2607. 23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability.

By Siqi You, Bingsong Xu, Zhixian Zheng, Xinjian Peng, Yang Xie, Ying Wang, Jiarong Xu

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 28

TriSP: Tri-Signal Structured Pruning for Large Language Models

arXiv:2607. 22587v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but their deployment is constrained by the memory and compute cost of their parameters.

By Manel Kara laoua, Soumia Bouyahiaoui, Aicha Boutorh

llms fine-tuning efficiency

arXiv AI

Jul 28

Speed Reading Tool Powered by Artificial Intelligence for Students with ADHD, Dyslexia, and Short Attention Span

arXiv:2307. 14544v2 Announce Type: replace-cross Abstract: This paper presents an artificial intelligence tool designed to assist students with dyslexia, ADHD, and short attention spans in processing text-based information more efficiently.

By Megat Irfan Zackry Bin Ismail, Ahmad Nazran bin Yusri, Muhammad Hafizzul Bin Abdul Manap, Muhammad Muizzuddin Bin Kamarozaman

llms nlp fine-tuning

arXiv AI

Jul 28

Reference Feature Atlases for Mechanistic Auditing of Language Models

arXiv:2607. 22570v1 Announce Type: new Abstract: Auditing a new language model usually means relearning and reinterpreting its internal features from scratch.

By Rui Wu, Tong Che

llms fine-tuning benchmarks

arXiv AI

Jul 28

LU-500: A Logo Benchmark for Concept Unlearning

arXiv:2607. 24101v1 Announce Type: cross Abstract: Concept unlearning is increasingly used to limit the reproduction of protected or unsafe visual concepts in text-to-image models.

By Keyu Li, Jin Gao, Jialing Zhang, Dequan Wang

agents diffusion fine-tuning benchmarks

arXiv AI

Jul 28

What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features

arXiv:2607. 23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.

By Chen-Yi Lu, Yueh-Shao Chen, Somali Chaterji

llms rag diffusion fine-tuning multimodal safety

arXiv Machine Learning

Jul 28

Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning

arXiv:2602. 04266v2 Announce Type: replace-cross Abstract: Aortic valve disease (AVD) represents a major public health burden, while its diagnosis relies on echocardiography, which is limited by cost and specialist expertise, restricting scalable screening and risk stratification.

By Jiaze Wang, Qinghao Zhao, Zizheng Chen, Zhejun Sun, Deyun Zhang, Yuxi Zhou, Shenda Hong

fine-tuning

arXiv Machine Learning

Jul 28

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.

By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jul 28

Shift-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment

arXiv:2501. 19060v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs), such as CLIP, adapt effectively to downstream tasks through prompt tuning, but fine-tuning can misalign predictive confidence and accuracy, particularly on unseen classes.

By Song-Lin Lv, Yu-Yang Chen, Zhi Zhou, Lan-Zhe Guo

llms fine-tuning multimodal safety

arXiv AI

Jul 28

An Interactive Vision Language Platform for Cognitive Remediation in Schizophrenia

arXiv:2607. 22721v1 Announce Type: cross Abstract: Cognitive remediation tasks often require patients to perform structured actions involving object manipulation and sequential reasoning.

By Nassira Ait Mehdi, Milissa Temmam, Slimane Larabi

llms robotics fine-tuning multimodal

arXiv AI

Jul 28

Cheap Probes Predict Expensive Training in 3D-CT Vision--Language Models

arXiv:2607. 22771v1 Announce Type: cross Abstract: Picking the frozen image encoder for a 3D~CT vision--language model (VLM), together with the token-compression scheme on top of it, is a search over many candidates.

By Renjie Liang

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jul 28

Sign-Symmetry Learning Rules are Robust Fine-Tuners

arXiv:2502. 05925v2 Announce Type: replace-cross Abstract: Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness.

By Aymene Berriche, Mehdi Zakaria Adjal, Riyadh Baghdadi

fine-tuning benchmarks safety

arXiv AI

Jul 28

Moral Hazard in Multi-Agent Language Models

arXiv:2607. 23982v1 Announce Type: cross Abstract: Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others.

By Dane Malenfant

llms agents fine-tuning safety

arXiv Machine Learning

Jul 28

IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems

arXiv:2607. 23322v1 Announce Type: cross Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains.

By Shwetha Singaravelu, Gayathri Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani

llms fine-tuning

arXiv Machine Learning

Jul 28

MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model

arXiv:2607. 23607v1 Announce Type: new Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry.

By Xin Zhao, Yumin Liu, Zhuo Li, Weichu Zheng, Feng Zhu, Xiaokang Yang, Yaohui Jin, Yanyan Xu

llms fine-tuning safety

arXiv Machine Learning

Jul 28

Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models

arXiv:2607. 23052v1 Announce Type: cross Abstract: Dual-encoder vision-language models (VLMs) expose a similarity interface that enables zero-shot retrieval but fails compositional constraints: queries like "umbrella and no person" retrieve images containing both, even when concept detection is reliable.

By Sultan Alshehri, Zhantao Yang, Han Zhang, Marios Savvides

llms rag fine-tuning multimodal

arXiv AI

Jul 28

DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning

arXiv:2607. 22769v1 Announce Type: cross Abstract: The training efficacy of large language models (LLMs) is fundamentally constrained by the quality and composition of training data.

By He Zhang

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jul 28

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning

arXiv:2607. 23837v1 Announce Type: new Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting.

By Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh

llms rag fine-tuning benchmarks

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