arXiv Machine Learning

Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer

arXiv:2607. 05937v1 Announce Type: cross Abstract: Sentiment analysis with frozen pre-trained language model (PLM) backbones has become a common paradigm, yet the practical benefit of explicit domain adaptation remains unclear, particularly when backbones encode varying degrees of target-domain knowledge.

Hugging Face Trending Papers

Jul 7

Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer

Sentiment analysis with frozen pre-trained language model (PLM) backbones has become a common paradigm, yet the practical benefit of explicit domain adaptation remains unclear, particularly when backbones encode varying degrees of target-domain knowledge. We present a preliminary case study evaluating a controlled family of frozen embedding backbones (Qwen3-Embedding 0.

llms rag nlp fine-tuning safety

arXiv Machine Learning

Jun 19

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

arXiv:2104. 08928v4 Announce Type: replace-cross Abstract: Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare.

By Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani

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arXiv AI

Jun 10

Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune

arXiv:2606. 10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs.

By Wu Yuerong, Mingni Luo

llms rag fine-tuning

Hugging Face Trending Papers

Jun 21

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction

Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or privacy-sensitive settings. We investigate how far small language models (SLMs) can close this gap across general-domain and literary text.

llms fine-tuning benchmarks safety

arXiv Machine Learning

Aug 11

RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification

arXiv:2608. 09834v1 Announce Type: cross Abstract: Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making.

By Fan Zhang, Jiaming Li

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arXiv AI

Aug 10

Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes

arXiv:2608. 07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities.

By Luc Hazenoot, Zhaochun Ren, Amirhossein Zohrehvand

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arXiv AI

Jul 7

Beyond Multilingual Averages: MTEB-PT, a Benchmark for Portuguese Sentence Encoders

arXiv:2607. 04071v1 Announce Type: cross Abstract: Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world.

By Lucas Hideki Takeuchi Okamura, Alexandre Alcoforado, Anna Helena Reali Costa

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arXiv AI

Jul 7

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

arXiv:2607. 03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains.

By Sushant Dagaji Desale, Rahul Mishra, Ashutosh Kumar Sinha

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arXiv AI

Jun 16

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604. 25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure.

By Aditya Sharma, Vinti Agarwal, Rajesh Kumar

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arXiv Machine Learning

Aug 6

From Financial Sentiment Classification to Return Predictability: A QLoRA Benchmark of Large Language Models

arXiv:2608. 04200v1 Announce Type: cross Abstract: Financial sentiment classifiers are commonly evaluated against human labels, but strong linguistic performance does not necessarily imply economically useful return predictability.

By Fusheng Luo

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 14

A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol

arXiv:2607. 11873v1 Announce Type: cross Abstract: Institutions collect far more open-ended teaching-evaluation feedback than they read.

By Esteban U. Vega Barajas

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arXiv Machine Learning

1d ago

Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation

arXiv:2608. 16269v1 Announce Type: cross Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora.

By Seung-Won Seo, Won Ik Cho, Yongmin Yoo

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