ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models | Paper Citation Record

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This paper cites GPT-4 Technical Report.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models GPT-4 Technical Report

Reference 1

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Observation 109fe90c-179f-478a-aa74-12e82b054f5a · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2

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Observation 042da77b-9e69-40ed-a0b3-382a3e79f171 · outbound

This paper cites Wasserstein GAN.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Wasserstein GAN

Reference 3

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Observation 1d8d44ba-97b8-4dcd-91ab-526b961f2ad0 · outbound

This paper cites Program Synthesis with Large Language Models.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Program Synthesis with Large Language Models

Reference 4

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 5

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 6

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Observation 7fcd1e42-e9fc-4010-b60f-aae0e4afd8d4 · outbound

This paper cites Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 7

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 8

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Observation f03e2262-da91-4a08-9a8e-24f5a53f15f3 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 9

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Observation cba8f17b-506f-4bac-a170-a40787f36ad1 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Evaluating Large Language Models Trained on Code

Reference 10

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Observation c54b2d7e-63c3-4baa-854b-fce7cc2415ca · outbound

This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models What Does BERT Look At? An Analysis of BERT's Attention

Reference 11

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Observation e0931648-5682-4c23-a24c-86307ee0da96 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:28.838061Z

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Observation 60c08b52-5e69-4346-a441-45df0e39f1e5 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 13

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Observation 4de72ca9-7fec-4e40-881c-8c20eb00bf67 · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 14

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Observation a2f1bbd3-8407-4215-ae50-943f05838ca2 · outbound

This paper cites an unresolved cited work.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 15

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 16

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 17

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Observation 449f4f89-6271-44b4-842c-49fd262ae3ad · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 18

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Observation eccb1d5c-7590-45dd-9ae0-a37558cba8e2 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 19

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Observation 6deba5fe-bdc3-4692-b2bc-1b2846e5a728 · outbound

This paper cites Finding Neurons in a Haystack: Case Studies with Sparse Probing.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 20

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Observation 918385e5-c8fd-438b-af29-be0863fa735a · outbound

This paper cites What Matters in Transformers? Not All Attention is Needed.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models What Matters in Transformers? Not All Attention is Needed

Reference 21

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Observation 590bb7f6-01dd-43f0-9581-0b2568848054 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Measuring Massive Multitask Language Understanding

Reference 22

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Observation e5ef0ee0-6a1c-447a-83e8-4be1ca203720 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Measuring Mathematical Problem Solving With the MATH Dataset

Reference 23

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Observation 8c379141-7c9d-4a13-98e6-83088b10b25f · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Distilling the Knowledge in a Neural Network

Reference 24

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Observation 81f3a25d-a88f-4abb-88a7-d0195b4e09a4 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 25

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Observation 2704a0b4-d1c1-4c73-ab66-799454279de7 · outbound

This paper cites Qwen2.5-Coder Technical Report.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Qwen2.5-Coder Technical Report

Reference 26

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 27

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Observation 4b2f79d3-b2cc-495a-8905-6f0a1a999fd0 · outbound

This paper cites Attention is Not Only a Weight: Analyzing Transformers with Vector Norms.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Attention is Not Only a Weight: Analyzing Transformers with Vector Norms

Reference 28

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 29

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Observation d687d74e-1276-4ac5-b3a6-f3b05d130900 · outbound

This paper cites CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

Reference 30

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Observation 64a8319a-333e-491b-9423-ef1d4d148fef · outbound

This paper cites Self-Alignment with Instruction Backtranslation.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Self-Alignment with Instruction Backtranslation

Reference 31

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Observation 979f2315-7f74-4c7a-b4be-f9e0bbab45c0 · outbound

This paper cites Tracr: Compiled Transformers as a Laboratory for Interpretability.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Tracr: Compiled Transformers as a Laboratory for Interpretability

Reference 32

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 33

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

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Observation 486d1769-54ed-4ed6-814e-148ce77bf323 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Decoupled Weight Decay Regularization

Reference 35

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

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Observation 4f5a4533-f3d9-4977-952b-623bcea19b56 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 37

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

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This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 39

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Observation e64c05be-3c0d-4cb6-af46-53873a7bc191 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Code Llama: Open Foundation Models for Code

Reference 40

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 41

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Observation 981ad702-cc8d-4d45-bd77-2154f2fa67ba · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 42

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Observation 8dd4675c-195f-4605-bec4-b5cc7d9322ad · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Understanding Layer Significance in LLM Alignment

Reference 43

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 44

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 45

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 46

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models RazorAttention: Efficient KV Cache Compression Through Retrieval Heads

Reference 47

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Observation c2ccc7ab-5162-428e-b9e7-c882c54a67d0 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Hashimoto

Reference 48

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Observation 62ff896b-316e-4bae-bd48-54bc2cfc4364 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 49

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Observation 007b0abb-ea24-4321-b6f6-0491b3a7c4c4 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 50

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Observation 0347abd5-acf9-4974-b291-be8aaed9ab4e · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 51

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Efficient Large Language Models: A Survey

Reference 52

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Observation e501691c-1645-4f2f-8dba-2eb2428502a2 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 53

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Observation f4f0e6c6-28f6-4fdd-951a-3ef3c40320c6 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Magicoder: Empowering Code Generation with OSS-Instruct

Reference 54

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Observation 06e312a4-ee12-416a-9d35-8c51fd801692 · outbound

This paper cites Retrieval Head Mechanistically Explains Long-Context Factuality.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Retrieval Head Mechanistically Explains Long-Context Factuality

Reference 55

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Observation 67ea79f2-a1e4-4403-afe2-4ffb96e10d9f · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 56

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Observation 50f67d49-6bb9-4674-89df-c916ae0d09e3 · outbound

This paper cites Qwen2.5 Technical Report.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Qwen2.5 Technical Report

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:32.776824Z

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Observation bd355200-13da-49af-9c8e-91d838aae586 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 58

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Observation 055e1336-85e1-4ffe-8fa6-302af0834c01 · outbound

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:32.881126Z

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Observation a5f9e173-b3f6-45a3-bb56-2945050b15af · outbound

This paper cites an unresolved cited work.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:32.925153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3d3c1e9-2f27-44b9-b215-65696141f1e7 · outbound

This paper cites A Survey of Large Language Models.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models A Survey of Large Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:32.962678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 698fa79a-24db-4df3-a301-20b075a410ff · outbound

This paper cites Attention Heads of Large Language Models: A Survey.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Attention Heads of Large Language Models: A Survey

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.035007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35d71b34-931a-497d-afa2-bd2771374679 · outbound

This paper cites an unresolved cited work.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:31:34.560331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1422a35e-e4a2-479d-9136-1ef316589647 · outbound

This paper cites an unresolved cited work.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.117093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d76108f-24e9-41f6-9e0b-36c2bfc2f040 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Instruction-Following Evaluation for Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.160827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a4f6db7-a64c-4f3a-ab37-4ce4e0fb6e08 · outbound

This paper cites On the Role of Attention Heads in Large Language Model Safety.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models On the Role of Attention Heads in Large Language Model Safety

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.227557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 032b8859-a205-4604-9524-72f73f9d0da2 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Representation Engineering: A Top-Down Approach to AI Transparency

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.361615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b20fdda5-bf19-437c-9b1e-c958a83cb0c8 · outbound

This paper cites online" 'onlinestring :=.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models online" 'onlinestring :=

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.464952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1623b5a-688c-4ca2-b154-928c47276e75 · outbound

This paper cites write newline.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models write newline

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:33.577852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers