[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
[Submitted on 14 Sep 2024 (v1), last revised 26 Jun 2025 (this version, v2)] View a PDF of the paper ...
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HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
arXiv:2506.21546v1 Announce Type: cross Abstract: Recent progress in vision-language segmentation has significantly advanced grounded visual understanding. However, these models often ...
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[2410.19494] Graph Linearization Methods for Reasoning on Graphs with Large Language Models

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
[Submitted on 25 Oct 2024 (v1), last revised 25 Jun 2025 (this version, v3)] View a PDF of the paper ...
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Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
arXiv:2506.19697v1 Announce Type: cross Abstract: Extreme activation outliers in Large Language Models (LLMs) critically degrade quantization performance, hindering efficient on-device ...
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TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
arXiv:2506.19441v1 Announce Type: cross Abstract: Evaluation of Text to Speech (TTS) systems is challenging and resource-intensive. Subjective metrics such as ...
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Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
arXiv:2506.18777v1 Announce Type: cross Abstract: Training large language models (LLMs) on source code significantly enhances their general-purpose reasoning abilities, but ...
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AI-Generated Song Detection via Lyrics Transcripts

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
arXiv:2506.18488v1 Announce Type: cross Abstract: The recent rise in capabilities of AI-based music generation tools has created an upheaval in ...
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An Interactive Benchmark for Evaluating LLMs’ Sequential Reasoning Ability

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
[Submitted on 14 Feb 2024 (v1), last revised 20 Jun 2025 (this version, v2)] View a PDF of the paper ...
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[2504.21016] Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
[Submitted on 21 Apr 2025 (v1), last revised 14 Jun 2025 (this version, v2)] View a PDF of the paper ...
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[2502.14718] Entity Framing and Role Portrayal in the News

[2409.09510] Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
[Submitted on 20 Feb 2025 (v1), last revised 15 Jun 2025 (this version, v2)] Authors:Tarek Mahmoud, Zhuohan Xie, Dimitar Dimitrov, ...
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