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 AI

Jun 30

Early Cue Precision Shapes Visual Shortcut Learning in Controlled Cue-Manipulation Benchmarks

arXiv:2606. 30344v1 Announce Type: cross Abstract: Visual classifiers can achieve high matched-distribution accuracy while relying on low-level cues that fail under conflict or suppression.

By Chanho Park, Woochan Lee, Janyeong Oh, Geongho Gong, Minshu Kim, Yeachan Kwak, Seongim Choi

robotics fine-tuning benchmarks

arXiv AI

Jun 30

Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss

arXiv:2606. 29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data.

By Nian Shao, Xian Li, Xiaofei Li

rag fine-tuning

arXiv AI

Jun 30

Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives

arXiv:2605. 00994v2 Announce Type: replace-cross Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors.

By Mohammed Abu Baker, Luca Baroni, Dan Wilhelm

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

Distributionally Robust Reinforcement Learning with Human Feedback

arXiv:2503. 00539v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs).

By Debmalya Mandal, Paulius Sasnauskas, Goran Radanovic

llms reinforcement-learning fine-tuning

arXiv AI

Jun 30

TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

arXiv:2606. 30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important.

By Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler, Yuzhe Wang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Vel\'azquez

rag agents fine-tuning multimodal

arXiv Machine Learning

Jun 30

KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory

arXiv:2606. 29243v1 Announce Type: new Abstract: We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings.

By Khan Raiyan Ibne Reza, Omar Ibne Shahid

rag fine-tuning benchmarks safety

arXiv AI

Jun 30

How to Train Your Long-Context Visual Document Model

arXiv:2602. 15257v3 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text.

By Austin Veselka

llms nlp fine-tuning benchmarks

arXiv AI

Jun 30

Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking

arXiv:2606. 29088v1 Announce Type: cross Abstract: There are various benchmarks to evaluate bugfixing capabilities of Large Language Models.

By Bal\'azs Szalontai, \'Abel Szauter, Bal\'azs M\'arton, P\'eter Verebics, Bal\'azs Pint\'er, Tibor Gregorics

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 30

Generalization error of min-norm interpolators in transfer learning

arXiv:2406. 13944v2 Announce Type: replace-cross Abstract: This paper establishes the generalization error of pooled min-$\ell_2$-norm interpolation in transfer learning, where data from diverse distributions are available.

By Yanke Song, Kenneth Gu, Sohom Bhattacharya, Pragya Sur

fine-tuning safety

arXiv Machine Learning

Jun 30

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting

arXiv:2505. 16903v2 Announce Type: replace Abstract: Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks.

By Peyman Baghershahi, Sourav Medya

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 30

Generalization Analysis of Transformers in Distribution Regression

arXiv:2606. 29256v1 Announce Type: cross Abstract: In recent years, models based on the Transformer architecture have seen widespread applications and have become one of the core tools in the field of deep learning.

By Peilin Liu, Ding-Xuan Zhou

llms nlp fine-tuning

arXiv AI

Jun 30

Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation

arXiv:2606. 28925v1 Announce Type: cross Abstract: Tool and agent routing from natural-language prompts is naturally a set-valued prediction problem: a single query may require multiple agents, while over-selection increases execution cost.

By Ananto Nayan Bala, Faisal Muhammad Shah

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

EVAF: A Test-Retest Protocol for Selective Parametric Consolidation

arXiv:2606. 29916v1 Announce Type: cross Abstract: Long-running language agents need mechanisms for deciding which experiences should persist after the working context is gone.

By Haoliang Han

llms agents fine-tuning

arXiv Machine Learning

Jun 30

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?

By Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 30

On the Vulnerability of Parameter-Level Defenses to Model Merging

arXiv:2606. 30360v1 Announce Type: new Abstract: The training-free integration of expert models via model merging has exposed significant security risks, enabling free-riders to combine specialized models without authorization.

By Kuangpu Guo, Qingyan Zheng, Jian Liang, Yongcan Yu, Zilei Wang, Ran He, Tieniu Tan

fine-tuning

arXiv Machine Learning

Jun 30

ITSPACE: Monotone Gaussian Optimal Transport Updates

arXiv:2606. 30523v1 Announce Type: new Abstract: Covariance matrices serve as compact descriptors of feature distributions in many machine-learning pipelines, including domain adaptation and Gaussian embeddings.

By Woojoo Na, Jennifer Dy

rag fine-tuning benchmarks safety

arXiv AI

Jun 30

Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning

arXiv:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.

By Chenyou Guo, Zongqi Liu, Yizhou Zhang, Zhaorui Jiang, Ze Liu

llms fine-tuning benchmarks

arXiv AI

Jun 30

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605. 09038v3 Announce Type: replace Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries.

By Jinchao Hu, Meizhi Zhong, Kehai Chen, Min Zhang

llms nlp fine-tuning benchmarks

arXiv AI

Jun 30

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale

arXiv:2407. 19633v4 Announce Type: replace Abstract: Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare.

By Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg, Shayan Talaei, Connor Lawless, Madeleine Udell

llms fine-tuning benchmarks

arXiv AI

Jun 30

LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training

arXiv:2606. 30642v1 Announce Type: cross Abstract: Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts.

By Shun Lei, Huaicheng Zhang, Dapeng Wu, Yaoxun Xu, Lishi Zuo, Wei Tan, Hangting Chen, Guangzheng Li, Jianwei Yu, Zhiyong Wu, Dong Yu

llms diffusion reinforcement-learning fine-tuning safety

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