Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping | Paper Citation Record

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Learning to summarize from human feedback cites this paper.

Learning to summarize from human feedback Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

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Editing Models with Task Arithmetic cites this paper.

Editing Models with Task Arithmetic Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 18

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Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification cites this paper.

Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

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On the Privacy Risk of In-context Learning cites this paper.

On the Privacy Risk of In-context Learning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 14

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Text-to-Image Synthesis: A Decade Survey cites this paper.

Text-to-Image Synthesis: A Decade Survey Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 130

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Unified Parameter-Efficient Unlearning for LLMs cites this paper.

Unified Parameter-Efficient Unlearning for LLMs Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

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Towards AI-$45^{\circ}$ Law: A Roadmap to Trustworthy AGI cites this paper.

Towards AI-$45^{\circ}$ Law: A Roadmap to Trustworthy AGI Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 26

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Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation cites this paper.

Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 18

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Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models cites this paper.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 3

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Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees cites this paper.

Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 38

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LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently cites this paper.

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 16

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Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs cites this paper.

Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 28

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LIMO: Less is More for Reasoning cites this paper.

LIMO: Less is More for Reasoning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 177

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CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval cites this paper.

CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 6

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Less is More: Adaptive Coverage for Synthetic Training Data cites this paper.

Less is More: Adaptive Coverage for Synthetic Training Data Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 12

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TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning cites this paper.

TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

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Revisiting Bayesian Model Averaging in the Era of Foundation Models cites this paper.

Revisiting Bayesian Model Averaging in the Era of Foundation Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 48

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Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation cites this paper.

Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 16

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Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification cites this paper.

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 18

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RewardAnything: Generalizable Principle-Following Reward Models cites this paper.

RewardAnything: Generalizable Principle-Following Reward Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 55

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GeistBERT: Breathing Life into German NLP cites this paper.

GeistBERT: Breathing Life into German NLP Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

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Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods cites this paper.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 9

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Should We Still Pretrain Encoders with Masked Language Modeling? cites this paper.

Should We Still Pretrain Encoders with Masked Language Modeling? Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

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Can Interpretation Predict Behavior on Unseen Data? cites this paper.

Can Interpretation Predict Behavior on Unseen Data? Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 9

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Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis cites this paper.

Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 96

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Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 143

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UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning cites this paper.

UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 49

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Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation cites this paper.

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 14

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LobRA: Multi-tenant Fine-tuning over Heterogeneous Data cites this paper.

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 14

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SindBERT, the Sailor: Charting the Seas of Turkish NLP cites this paper.

SindBERT, the Sailor: Charting the Seas of Turkish NLP Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

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Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging cites this paper.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 12

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In-Context Probing for Membership Inference in Fine-Tuned Language Models cites this paper.

In-Context Probing for Membership Inference in Fine-Tuned Language Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 55

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Beyond Transfer Accuracy: Faithful Circuits for Controlled Low-Resource Adaptation cites this paper.

Beyond Transfer Accuracy: Faithful Circuits for Controlled Low-Resource Adaptation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 2023

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Robust Policy Optimization to Prevent Catastrophic Forgetting cites this paper.

Robust Policy Optimization to Prevent Catastrophic Forgetting Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 16

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If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models cites this paper.

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 49

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Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution cites this paper.

Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 1

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Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages cites this paper.

Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 38

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Instructions Shape Production of Language, not Processing cites this paper.

Instructions Shape Production of Language, not Processing Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 196

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Instructions Shape Production of Language, not Processing cites this paper.

Instructions Shape Production of Language, not Processing Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 196

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20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone cites this paper.

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

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20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone cites this paper.

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

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LoRA vs. Full Fine-Tuning: A Theoretical Perspective cites this paper.

LoRA vs. Full Fine-Tuning: A Theoretical Perspective Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 10

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PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling cites this paper.

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

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PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling cites this paper.

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

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Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias cites this paper.

Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:23:28.023366Z

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Observation 9bb93867-19eb-442a-8a51-673fb8b042b9 · inbound

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning cites this paper.

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:11.294800Z

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PortBERT: Navigating the Depths of Portuguese Language Models cites this paper.

PortBERT: Navigating the Depths of Portuguese Language Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T23:06:20.221903Z

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Observation 801769d5-86c1-4dff-b51d-0461a5974d56 · inbound

On the Geometry of On-Policy Distillation cites this paper.

On the Geometry of On-Policy Distillation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:27:08.862860Z

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Observation 5e248862-10b4-4d88-96c3-58754aed301f · inbound

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis cites this paper.

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:04:00.576999Z

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Observation ebd6ee2f-ea30-48b4-850b-6e8ad4d48ac0 · inbound

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis cites this paper.

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 10

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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents cites this paper.

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 272

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:15:05.450842Z

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Observation 184ff01d-adbf-4fc1-a310-130609772a5f · inbound

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging cites this paper.

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:57.821995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e927e833-4491-4ed6-961a-358ea600a34d · inbound

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection cites this paper.

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T01:19:20.994944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 461299ca-31ab-42f5-9b20-daf5450a4ce1 · inbound

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection cites this paper.

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T20:49:57.860299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 014aceff-902a-4c32-b1a8-af77508d665f · inbound

Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning cites this paper.

Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:49:34.445786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4cef2d21-e320-4d4b-863c-71bb436ec705 · inbound

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation cites this paper.

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:49:29.528251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2be36366-81bd-4a57-acdb-4dbf56f663f0 · inbound

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

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 249

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:19:42.859218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 75c89c94-3105-427c-ab92-09316fecc380 · inbound

GRAIN: Group Aggregation via Min-Norm Objective cites this paper.

GRAIN: Group Aggregation via Min-Norm Objective Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:45.269882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8039ae6c-1868-4493-9144-5659599b240b · inbound

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise cites this paper.

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:24:21.865183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3cfef4e6-f123-47ec-990d-3c71ffae3f54 · inbound

Training Large Language Models for Self-Explanation Faithfulness cites this paper.

Training Large Language Models for Self-Explanation Faithfulness Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T08:36:23.469133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0d39616-0efc-41c9-9614-13a0b18ab9e9 · inbound

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning cites this paper.

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-30T10:55:15.500484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4e97a7e-b0fe-44af-9d9e-2dfd72fd626e · inbound

Can Training Logs Make Model Comparisons More Precise? cites this paper.

Can Training Logs Make Model Comparisons More Precise? Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T15:08:17.493758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e787555d-0ada-4d70-89b4-998b2c209f89 · inbound

What We Observe as LLM Behavior Can Be a Side-effect of Inference Backend cites this paper.

What We Observe as LLM Behavior Can Be a Side-effect of Inference Backend Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T18:22:28.042785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f829368-ead4-40b3-b646-1b5d384e8c71 · inbound

Neuroevolution Arena: Nested Ecological Evaluation of Update-and-Inheritance Regimes across Neural Architectures cites this paper.

Neuroevolution Arena: Nested Ecological Evaluation of Update-and-Inheritance Regimes across Neural Architectures Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T04:15:48.602224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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