Data-Efficient Adaptation of LLMs via Attention Head Reweighting | Paper Citation Record

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This paper cites 16 Published as a conference paper at COLM 2026 Figure A.3: Effect of manually changing the β for only L15H14 and L15H3 of Llama-3.2-1B.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting 16 Published as a conference paper at COLM 2026 Figure A.3: Effect of manually changing the β for only L15H14 and L15H3 of Llama-3.2-1B

Reference 1

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This paper cites Saes are good for steering–if you select the right features.arXiv preprint arXiv:2505.20063,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Saes are good for steering–if you select the right features.arXiv preprint arXiv:2505.20063,

Reference 3

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This paper cites Benign Toxigen asians are trying to take over the world with their karate skills Toxic Table A.2: Data examples.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Benign Toxigen asians are trying to take over the world with their karate skills Toxic Table A.2: Data examples

Reference 5

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This paper cites Text-to-LoRA: Instant Transformer Adaption.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Text-to-LoRA: Instant Transformer Adaption

Reference 7

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This paper cites Why can gpt learn in-context? language models secretly perform gradient descent as meta- optimizers.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Why can gpt learn in-context? language models secretly perform gradient descent as meta- optimizers

Reference 8

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This paper cites LLMs for Cyber Security: New Opportunities.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting LLMs for Cyber Security: New Opportunities

Reference 9

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This paper cites The Llama 3 Herd of Models.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting The Llama 3 Herd of Models

Reference 10

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This paper cites A mathematical framework for transformer circuits.T ransformer Circuits Thread, 1,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting A mathematical framework for transformer circuits.T ransformer Circuits Thread, 1,

Reference 11

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This paper cites Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

Reference 14

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This paper cites LLMSteer: Improving Long-Context LLM Inference by Steering Attention on Reused Contexts.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting LLMSteer: Improving Long-Context LLM Inference by Steering Attention on Reused Contexts

Reference 15

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This paper cites Instruction following by boosting attention of large language models.arXiv preprint arXiv:2506.13734,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Instruction following by boosting attention of large language models.arXiv preprint arXiv:2506.13734,

Reference 16

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This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 17

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This paper cites Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar

Reference 18

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This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 19

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 20

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This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 21

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This paper cites Interpretable language modeling via induction-head ngram models.arXiv preprint arXiv:2411.00066,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Interpretable language modeling via induction-head ngram models.arXiv preprint arXiv:2411.00066,

Reference 22

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This paper cites Can language models learn from explanations in context?.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Can language models learn from explanations in context?

Reference 23

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This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 24

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This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965,

Reference 25

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 26

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Cutting down on prompts and parameters: Simple few-shot learning with language models

Reference 27

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This paper cites Rethinking the role of demonstrations: What makes in-context learning work? InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Rethinking the role of demonstrations: What makes in-context learning work? InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp

Reference 29

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Tree Prompting: Efficient Task Adaptation without Fine-Tuning

Reference 30

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Linear Explanations for Individual Neurons

Reference 31

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting In-context Learning and Induction Heads

Reference 32

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Towards Modular LLMs by Building and Reusing a Library of LoRAs

Reference 33

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Boosted Prompt Ensembles for Large Language Models

Reference 34

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This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Language models are unsupervised multitask learners.OpenAI blog, 1(8):9,

Reference 35

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Carer: Contextualized affect representations for emotion recognition

Reference 36

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting TOAST: Transfer Learning via Attention Steering

Reference 37

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 38

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Explaining black box text modules in natural language with language models

Reference 39

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Multiguard: An efficient approach for ai safety moderation across languages and modalities

Reference 41

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 42

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Unresolved cited work

Reference 43

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This paper cites Larger language models do in-context learning differently.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Larger language models do in-context learning differently

Reference 44

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting 14 Published as a conference paper at COLM 2026 An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al

Reference 45

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Jailbreak Attacks and Defenses Against Large Language Models: A Survey

Reference 46

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 47

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Large Language Models Are Human-Level Prompt Engineers

Reference 49

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Vector-icl: In-context learning with continuous vector representations

Reference 50

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting We can see it only pays attention to thePhishingtokens regardless of current input’s label

Reference 51

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Unresolved cited work

Reference 53

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This paper cites Reuters - Short-sellers, Wall Street’s dwindling band of ultra-cynics, are seeing green again.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Reuters - Short-sellers, Wall Street’s dwindling band of ultra-cynics, are seeing green again

Reference 54

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This paper cites In-context algebra.arXiv preprint arXiv:2512.16902,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting In-context algebra.arXiv preprint arXiv:2512.16902,

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Attention Reveals More Than Tokens: Training-Free Long-Context Reasoning with Attention-guided Retrieval

Reference 2015

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention

Reference 2019

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Improving Steering Vectors by Targeting Sparse Autoencoder Features

Reference 2020

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Human-AI Co-design for Clinical Prediction Models

Reference 2021

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting In-Context Language Learning: Architectures and Algorithms

Reference 2022

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This paper cites Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

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This paper cites What learning algorithm is in-context learning? Investigations with linear models.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting What learning algorithm is in-context learning? Investigations with linear models

Reference 2024

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions

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This paper cites Weight-sparse transformers have interpretable circuits.arXiv preprint arXiv:2511.13653,.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Weight-sparse transformers have interpretable circuits.arXiv preprint arXiv:2511.13653,

Reference 2026

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