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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

Sep 22

onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction

arXiv:2609.24983v1 Announce Type: cross Abstract: We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction a...

By Lei Yang, Mengyin Liu, Jia Wang, Hangyu Guo, Liang Zhao, Zheng Ge, Kang An, Binxing Jiao, Qi Han, Daxin Jiang, Siqi Shen, Xiangyu Zhang

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Sep 22

Glass-Box Deep Learning for FDIA Detection in Nonlinear Automatic Generation Control: A Kolmogorov-Arnold Network Approach

arXiv:2509.05259v2 Announce Type: replace Abstract: Automatic Generation Control (AGC) plays a critical role in maintaining power balance across multi-area power systems. However, its complete relian...

By Ahmad Mohammad Saber, Alok Paranjape, Jehad Jilan, Niranjana Naveen Nambiar, Amr Youssef, Deepa Kundur

fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Sep 22

Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization

arXiv:2512.07419v3 Announce Type: replace Abstract: Mixed-Precision Quantization (MPQ) liberates Deep Neural Networks (DNNs) from the Out-Of-Memory (OOM) bottleneck and has garnered increasing resear...

By Haidong Kang, Jun Du, Guo Yu

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 22

When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity

arXiv:2605.08992v2 Announce Type: replace Abstract: Federated learning (FL) is increasingly used to fine-tune foundation models (FMs) on distributed private data. The community largely assumes that l...

By Kiran Naseer, Umar Shoaib

nlp fine-tuning safety

arXiv Machine Learning

Sep 22

Transfer Learning for Matrix Completion

arXiv:2507.02248v2 Announce Type: replace-cross Abstract: In this paper, we explore the knowledge transfer under the setting of matrix completion, which aims to enhance the estimation of a low-rank t...

By Dali Liu, Yuying Xie, Haolei Weng

arXiv Computation and Language

Sep 22

Team DArgk at the 2026 ELOQUENT lab for evaluating generative language model quality: Residuals of Humanity: AI Detection Evasion via GRPO Fine-Tuning

arXiv:2609.22221v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent and coherent text that is increasingly difficult to distinguish from human writing, motivating the dev...

By Antonela Tommasel, Juan Manuel Rodriguez

llms reinforcement-learning fine-tuning safety

arXiv Computation and Language

Sep 22

Toward Personalized Sleep Guidance from Wearable Data Using Language Models

arXiv:2609.22463v1 Announce Type: new Abstract: Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering r...

By Yusheng Tan, Running Zhao, Sofia Angel, Ninghui Hao, Ash Arian, Nikita N. Dulin, Jay Lin, Ou Zhu, Faiza Shaik, Xinxing Yang, Bonnie W. Leung, Katie Roster, Arlene Ruiz de Luzuriaga, Kenneth Lee, Alejandra Lastra, Habibul Ahsan, Guihong Wan

llms agents nlp fine-tuning safety

arXiv Computation and Language

Sep 22

MIS-Bench: Benchmarking Multimodal LLMs for Psychotherapeutic Interpersonal Skills Assessment

arXiv:2609.22778v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require exper...

By Yuhan Lu, Yi Yao, Hua Shen, Katie Aafjes-van Doorn, Zhaonan Wang

llms fine-tuning multimodal benchmarks

arXiv Computation and Language

Sep 22

OmniEdu: Open Foundation Models for Learning and Teaching

arXiv:2609.23088v1 Announce Type: new Abstract: Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructiona...

By Hao Liang, Qihan Lin, Meiyi Qiang, Linzhuang Sun, Hengyi Feng, Mingrui Chen, Sizhe Qiu, Wentao Zhang

llms fine-tuning benchmarks

arXiv Computation and Language

Sep 22

Low resource cross-modal alignment using HGNN to enhance speech representation

arXiv:2609.23191v1 Announce Type: new Abstract: Speech-text space alignment is a multimodal representation learning method consisting to map different speech and text into a shared representation spa...

By Yannick Yomie Nzeuhang, Marie Tahon, Paulin Melatagia Yonta

fine-tuning multimodal benchmarks safety

arXiv Computation and Language

Sep 22

Error-Supervised Synthetic Learner Writing for Automated Essay Scoring

arXiv:2609.23573v1 Announce Type: new Abstract: Synthetic essays can help reduce dependence on human-written data in Automated Essay Scoring (AES). However, they often lack realistic errors, limiting...

By Duy Anh Nguyen

llms fine-tuning

arXiv Computation and Language

Sep 22

GRACE: Grounded Adversarial Reasoning over Canadian Law

arXiv:2609.23726v1 Announce Type: new Abstract: Large language models have shown strong performance across a range of legal tasks, but existing benchmarks rarely evaluate the ability to take and defe...

By Jiakang Xu, Wantong Huo, Udom Silparcha, Jonathan H. Chan

llms nlp fine-tuning benchmarks safety

arXiv Computation and Language

Sep 22

HaikuS2S: A Cascaded System For Responding In Verse

arXiv:2609.23951v1 Announce Type: new Abstract: Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior w...

By Devangi Sharma, Sophia Judicke, Glenda Tan, Conrad Schaumburg, Shinji Watanabe

llms fine-tuning multimodal safety

arXiv Computation and Language

Sep 22

ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification

arXiv:2609.24903v1 Announce Type: new Abstract: Tone languages constitute over 50-70% of the world's languages, but the vast majority are low-resource, lacking the large transcribed corpora needed fo...

By Qisheng Liao, Youngah Do

fine-tuning benchmarks

arXiv Computation and Language

Sep 22

Tutoring Large Language Models to be Domain-adaptive, Precise and Safe

arXiv:2609.23071v1 Announce Type: cross Abstract: This thesis proposes a framework for "responsible intelligence" to address AI's critical challenges in safety, ethics, and cultural sensitivity. It a...

By Somnath Banerjee

llms fine-tuning safety

arXiv Computation and Language

Sep 22

AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models

arXiv:2609.23979v1 Announce Type: cross Abstract: Attention encoder-decoder (AED) Speech Foundation Models achieve strong ASR performance but can generate acoustically unsupported text when inputs co...

By Natarajan Balaji Shankar, Zilai Wang, Zihan Wang, Mohan Shi, Kaiyuan Zhang, Abeer Alwan

fine-tuning multimodal safety

arXiv Computation and Language

Sep 22

Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models

arXiv:2609.24657v1 Announce Type: cross Abstract: Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wi...

By Xiaoqiang Wang, Mengyang Xiong, Jun Dai, Bang Liu

llms diffusion fine-tuning benchmarks

arXiv Computation and Language

Sep 22

Harness-Zero: Harness Distillation via Agent-as-Harness

arXiv:2609.24974v1 Announce Type: cross Abstract: Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain...

By Haoran Ye, Yuxing Lu, Haonan Dong, Zhaochen Su, Guojie Song

llms agents fine-tuning efficiency

arXiv Computation and Language

Sep 22

The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models

arXiv:2602.01161v2 Announce Type: replace Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning...

By Reem I. Masoud, Chen Feng, Shunta Asano, Saied Alshahrani, Philip Colin Treleaven, Miguel R. D. Rodrigues

llms fine-tuning benchmarks safety

arXiv Computation and Language

Sep 22

Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Models

arXiv:2602.01654v2 Announce Type: replace Abstract: Steering vectors (SVs) offer a lightweight way to control large language models (LLMs) at inference time by shifting hidden activations, providing...

By Jiaqian Li, Yanshu Li, Kuan-Hao Huang

llms fine-tuning