Fine-tuning and adaptation

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

3,865 stories · RSS feed

arXiv Machine Learning

Jun 9

Zero and Few Shot Load Forecasting with Large Language Models

arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.

By Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel

llms nlp fine-tuning

arXiv AI

Jun 9

How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions

arXiv:2606. 08051v1 Announce Type: new Abstract: Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale.

By Donghao Huang, Tomas Drietomsky, Benjamin Barrett, Zhaoxia Wang

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 9

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

arXiv:2510. 27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms fine-tuning benchmarks

arXiv AI

Jun 9

Subtitle-Aligned Fine-Tuning of Whisper for Swiss German ASR: Benchmark Contamination, Convention Mismatch, and an Honest Baseline at 25.6% WER (13.8% cWER)

arXiv:2606. 07608v1 Announce Type: cross Abstract: We present a systematic study of fine-tuning OpenAI's Whisper large-v3 for Swiss German ASR, using 1,367 hours of broadcast speech paired with Standard German subtitles as weak supervision.

By Felix Akeret

fine-tuning multimodal benchmarks safety data-engineering

arXiv AI

Jun 9

OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs

arXiv:2606. 07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited by the linear growth of video tokens and key-value (KV) caches.

By Guangzhi Sun, Yixuan Li, Yudong Yang, Chao Zhang

llms fine-tuning multimodal

arXiv AI

Jun 9

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models

arXiv:2606. 08446v1 Announce Type: cross Abstract: Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive.

By Yang Zhou, Ranajoy Sadhukhan, Zhaofeng Sun, Zhuoming Chen, Souvik Kundu, Saket Dingliwal, Sai Muralidhar Jayanthi, Aram Galstyan, Haizhong Zheng, Beidi Chen

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 9

Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices

arXiv:2606. 07857v1 Announce Type: cross Abstract: The rise of edge-based machine learning has enabled distributed adaptation of language models across mobile and IoT devices, offering privacy preservation and real-time responsiveness.

By Stefan Behfar, Richard Mortier

llms robotics fine-tuning safety

arXiv AI

Jun 9

Evaluating Hallucinations in Domain-Adapted Large Language Models

arXiv:2606. 07521v1 Announce Type: cross Abstract: This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini dataset.

By Sanchita Porwal, Sai Prasath S, Xingjian Bi, Madelyn Scandlen

llms fine-tuning safety

arXiv AI

Jun 9

CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning

arXiv:2606. 08169v1 Announce Type: cross Abstract: Enabling robots to understand and execute tasks from natural language commands while maintaining data efficiency remains challenging.

By Markus Knauer, Valentin Gieraths, Tai Mai, Samuel Bustamante, Alin Albu-Sch\"affer, Freek Stulp, Jo\~ao Silv\'erio

llms robotics fine-tuning multimodal

arXiv AI

Jun 9

Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching

arXiv:2606. 07684v1 Announce Type: cross Abstract: Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmitting high-dimensional Key-Value (KV) caches often dominates time-to-first-token (TTFT).

By Qianli Ma, Zhiqing Tang, Hanshuai Cui, Zhi Yao, Weijia Jia

llms fine-tuning efficiency

arXiv AI

Jun 9

Reinforcement Learning for Flow-Matching Policies with Density Transport

arXiv:2606. 08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems.

By Boshu Lei, Kostas Daniilidis, Antonio Loquercio

diffusion reinforcement-learning robotics fine-tuning efficiency multimodal safety

arXiv AI

Jun 9

Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models

arXiv:2606. 09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored.

By Danya Li, Xiang Su, Yan Feng, Rico Krueger

llms nlp fine-tuning benchmarks safety

arXiv AI

Jun 9

Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning

arXiv:2606. 09353v1 Announce Type: cross Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms.

By Maria De Marsico, Anil K. Jain, Annalaura Miglino

llms computer-vision fine-tuning benchmarks

arXiv AI

Jun 9

Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan

arXiv:2606. 09767v1 Announce Type: cross Abstract: Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping.

By Alexander Chulzhanov, Soeren Eberhardt, Arjun Mukherjee

llms nlp fine-tuning benchmarks

arXiv AI

Jun 9

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

arXiv:2601. 04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance.

By Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Large Language Models for Imbalanced Classification: Diversity makes the difference

arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.

By Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Taylor Braund, Alexis Whitton, Svetha Venkatesh

llms fine-tuning benchmarks

arXiv AI

Jun 9

AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers

arXiv:2606. 08272v1 Announce Type: cross Abstract: AgriGov is a curated, trilingual (English-Hindi-Marathi) dataset designed to address the scarcity of domain-grounded multilingual resources for agricultural policies and farmer welfare schemes.

By Mohsina Bilal, Gopakumar G

rag nlp fine-tuning safety

arXiv AI

Jun 9

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

arXiv:2606. 09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories.

By Yanyan Luo, Xue Han, Ruiqiao Bai, Xin Huang, Yitong Wang, Qian Hu, Qing Wang, Chunxu Zhao, Jie Liu, Cong Geng, Lehao Xing, Pengwei Hu, Junlan Feng

llms agents reinforcement-learning fine-tuning efficiency multimodal safety

arXiv AI

Jun 9

Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing

arXiv:2606. 07558v1 Announce Type: cross Abstract: Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at scale.

By Kateryna Lutsai, Pavel Stra\v{n}\'ak, David Nov\'ak, Dana K\v{r}iv\'ankov\'a

llms computer-vision fine-tuning multimodal

arXiv AI

Jun 9

Phantom transitions in language model fine-tuning

arXiv:2606. 07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently.

By Vaibhav Prakash, Jayasri Dontabhaktuni

llms rag fine-tuning

Get the daily briefing