Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization | Paper Citation Record

Observation 7ac930d8-8a02-4ccb-8df5-6595d3e22288 · outbound

This paper cites The Llama 3 Herd of Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization The Llama 3 Herd of Models

Reference 1

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This paper cites Qwen3 Technical Report.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Qwen3 Technical Report

Reference 2

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This paper cites DeepSeek-V3 Technical Report.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization DeepSeek-V3 Technical Report

Reference 3

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This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization gpt-oss-120b & gpt-oss-20b Model Card

Reference 4

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Observation d7ad915d-16b2-4492-a0cb-307eb22ecf6c · outbound

This paper cites A Survey on Model Compression for Large Language Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization A Survey on Model Compression for Large Language Models

Reference 5

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This paper cites A Survey of Small Language Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization A Survey of Small Language Models

Reference 6

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Observation 6081fddf-19ce-4215-b5eb-adc26899ba8a · outbound

This paper cites Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model

Reference 7

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Observation e90524c1-ea53-4117-b3c7-e6fc0d72b528 · outbound

This paper cites Qlora: eﬀicient finetuning of quantized llms,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Qlora: eﬀicient finetuning of quantized llms,

Reference 8

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Observation 8468d2b7-74dc-4a7a-8618-9ce8c24a2637 · outbound

This paper cites CBQ: Cross-block quantization for large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization CBQ: Cross-block quantization for large language models,

Reference 9

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Observation 696c57bf-3d76-42fe-bb32-5c796c46da7f · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization MiniLLM: On-Policy Distillation of Large Language Models

Reference 10

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Observation 30cfe390-b67a-4c5a-a205-7b961f1f72cb · outbound

This paper cites A good learner can teach better: Teacher-student collaborative knowledge distillation,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization A good learner can teach better: Teacher-student collaborative knowledge distillation,

Reference 11

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Observation 31462cd9-75d9-41c4-8670-23cb9af894b0 · outbound

This paper cites Replaceme: Network simplification via depth pruning and transformer block linearization,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Replaceme: Network simplification via depth pruning and transformer block linearization,

Reference 12

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Observation 60d07a5e-d8af-4dd1-b833-012076a63079 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 13

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Observation 44a4c48d-f42f-4dcc-bce9-4c1b2f541578 · outbound

This paper cites SLEB: Streamlining LLMs through redundancy verification and elimination of transformer blocks,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization SLEB: Streamlining LLMs through redundancy verification and elimination of transformer blocks,

Reference 14

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Observation 96537e75-9780-4f30-8358-94a386e1bed7 · outbound

This paper cites The unreasonable ineffectiveness of the deeper layers,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization The unreasonable ineffectiveness of the deeper layers,

Reference 15

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Observation 8ae69e94-9a9e-45b7-80d5-e3685e77b77e · outbound

This paper cites Llm-pruner: on the structural pruning of large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Llm-pruner: on the structural pruning of large language models,

Reference 16

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Observation c53df1b6-c14d-4676-90cf-77154a9f8373 · outbound

This paper cites Y ou only prune once: Designing calibration-free model compression with policy learning,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Y ou only prune once: Designing calibration-free model compression with policy learning,

Reference 17

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Observation dbda6eff-0b50-45b3-bd3d-5ce35d707bf3 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 18

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Observation 3ec65aee-ff2f-4c5f-aada-73f8f4787b34 · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 19

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This paper cites Sliding-window merging for compacting patch-redundant layers in llms,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Sliding-window merging for compacting patch-redundant layers in llms,

Reference 20

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Observation d30ab97f-f96b-426d-b3d8-fdf00b9f82ac · outbound

This paper cites Beware of calibration data for pruning large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Beware of calibration data for pruning large language models,

Reference 21

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Observation 77e4b368-0511-4715-95ec-0bb061eecb1e · outbound

This paper cites I-bert: Integer-only bert quantization,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization I-bert: Integer-only bert quantization,

Reference 22

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Observation 11053eb2-9d2f-4fe2-a02d-431438d36867 · outbound

This paper cites Llm-fp4: 4-bit floating-point quantized transformers,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Llm-fp4: 4-bit floating-point quantized transformers,

Reference 23

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Observation 16e72b1a-077c-42cc-b8ad-a419a1ba646d · outbound

This paper cites Aptq: Attention-aware post-training mixed-precision quantization for large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Aptq: Attention-aware post-training mixed-precision quantization for large language models,

Reference 24

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Observation 983469a0-a314-4c1a-a01c-26747010387d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Distilling the Knowledge in a Neural Network

Reference 25

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Observation cbe78255-4706-4877-b4a8-f6e7e1a8f471 · outbound

This paper cites TinyBERT: Distilling BERT for natural language understanding,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization TinyBERT: Distilling BERT for natural language understanding,

Reference 26

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Observation 1c0e06d5-5097-4e5b-be58-b2f50810b682 · outbound

This paper cites Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Data.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Data

Reference 27

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Observation 4ba8e24d-987e-43dd-a05f-5318d20a94bb · outbound

This paper cites Activation sparsity opportunities for compressing general large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Activation sparsity opportunities for compressing general large language models,

Reference 28

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Observation 92b8990e-e227-4992-8cf1-27859f06b6a8 · outbound

This paper cites Sparsing Law: Towards Large Language Models with Greater Activation Sparsity.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Sparsing Law: Towards Large Language Models with Greater Activation Sparsity

Reference 29

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Observation e52f6848-aa3b-48c8-815a-a5e43eaebdba · outbound

This paper cites Training-Free Activation Sparsity in Large Language Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Training-Free Activation Sparsity in Large Language Models

Reference 30

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Observation 2cf05b86-2969-4ff9-b105-715602bd1e5d · outbound

This paper cites R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference

Reference 31

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Observation fe4bd0ea-41f5-42fc-a5c0-2b3634e67a60 · outbound

This paper cites Learning both Weights and Connections for Efficient Neural Networks.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Learning both Weights and Connections for Efficient Neural Networks

Reference 32

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This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization A Simple and Effective Pruning Approach for Large Language Models

Reference 33

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Observation 835b78f8-9602-40ad-b036-f6ce40090b7d · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Second order derivatives for network pruning: Optimal brain surgeon,

Reference 34

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Observation c5ab5b0f-ca71-495e-8f82-73d176392d45 · outbound

This paper cites Sparsegpt: massive language models can be accurately pruned in one-shot,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Sparsegpt: massive language models can be accurately pruned in one-shot,

Reference 35

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Observation 1682e9bf-4ff6-4e81-a939-a5222e11f2e1 · outbound

This paper cites The LLM surgeon,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization The LLM surgeon,

Reference 36

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Observation 8421378e-9aea-4a51-b3a2-6879a5967435 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Accelerating Sparse Deep Neural Networks

Reference 37

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Observation 02dab4da-1e69-4554-9f89-0c7b2d4d4aea · outbound

This paper cites Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned,

Reference 38

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Observation fb4c46f5-ebe6-48a9-a68a-bad1dbebd5d4 · outbound

This paper cites Are sixteen heads really better than one?.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Are sixteen heads really better than one?

Reference 39

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Observation 4ec01560-ca5f-4b71-bb29-47a64e0b9daf · outbound

This paper cites SlimLLM: Accurate structured pruning for large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization SlimLLM: Accurate structured pruning for large language models,

Reference 40

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Observation dc86c9f4-a687-4c65-8909-a1cd8d1aa1d0 · outbound

This paper cites Language model compression with weighted low-rank factorization.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Language model compression with weighted low-rank factorization

Reference 41

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Observation 5b9e766c-03db-4d4e-9b90-b9a693b5935d · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 42

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Observation 2b1c4c22-cf84-44f5-9dad-688c9dc5f683 · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 43

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Observation 7ac99b67-d594-4f52-b4e0-1485b782a3b2 · outbound

This paper cites Streamlining redundant layers to compress large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Streamlining redundant layers to compress large language models,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.960550Z

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Observation fd91df38-6daa-496f-94bd-2f4b04285787 · outbound

This paper cites DLP: Dynamic layerwise pruning in large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization DLP: Dynamic layerwise pruning in large language models,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.948869Z

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Observation 5ed00657-deb9-4148-a0d1-0b0db3e2e704 · outbound

This paper cites Prompt-based depth pruning of large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Prompt-based depth pruning of large language models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.936480Z

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Observation 2059058f-b6e7-4c2f-b0ba-056d15fe80e7 · outbound

This paper cites Less is More: Towards Green Code Large Language Models via Unified Structural Pruning.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Less is More: Towards Green Code Large Language Models via Unified Structural Pruning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.765828Z

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Observation 087b7255-e36b-4941-8129-62473ac4b613 · outbound

This paper cites Knapsack problems,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Knapsack problems,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.924997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 060cd6bb-8150-4525-b190-40c229a540bc · outbound

This paper cites Qwen2.5 Technical Report.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Qwen2.5 Technical Report

Reference 49

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Observation 6639f2d4-5c59-4891-8a64-e2ae1240c9fb · outbound

This paper cites Phi-4 Technical Report.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Phi-4 Technical Report

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.778138Z

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Observation 04294ee4-14a3-4a03-8844-dc2d96c24e2f · outbound

This paper cites 2SSP: A two-stage framework for structured pruning of LLMs,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization 2SSP: A two-stage framework for structured pruning of LLMs,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.912468Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59f66471-fb33-4429-87b2-b0c73133202c · outbound

This paper cites Pointer Sentinel Mixture Models.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Pointer Sentinel Mixture Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.786256Z

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Observation d4367532-176d-46cc-9394-10f8015ce7d1 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization The LAMBADA dataset: Word prediction requiring a broad discourse context,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.897570Z

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Observation 7df822b2-185b-4e73-a71e-aff1ecb1f928 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Piqa: Reasoning about physical commonsense in natural language,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.885339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dfe1fa7f-c28a-4ac3-b824-77d87ace3dec · outbound

This paper cites PROST: Physical reasoning about objects through space and time,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization PROST: Physical reasoning about objects through space and time,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.873511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d45432b5-cb0e-45cd-89c7-f9bf80b8c679 · outbound

This paper cites CommonsenseQA: A question answering challenge targeting commonsense knowledge,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization CommonsenseQA: A question answering challenge targeting commonsense knowledge,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.860632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2e065396-6d0a-4e1d-86b5-fb250ef047cc · outbound

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

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.807045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fa4547f-c2fe-48f5-b7d6-33795d28034f · outbound

This paper cites MathQA: Towards interpretable math word problem solving with operation-based formalisms,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization MathQA: Towards interpretable math word problem solving with operation-based formalisms,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.847022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 040ed4d2-2127-4e40-9e67-6ecc35da4df0 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Can a suit of armor conduct electricity? a new dataset for open book question answering,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.834274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ea146a8c-cf8e-413b-b7b5-8a2e57252d12 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization What disease does this patient have? a large-scale open domain question answering dataset from medical exams,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.822196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 947c4538-1edb-4c6d-af4f-b5ee71b5ae54 · outbound

This paper cites Blimp: The benchmark of linguistic minimal pairs for english,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Blimp: The benchmark of linguistic minimal pairs for english,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.810186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00917aab-eff4-4099-b414-4caf6c1bec0a · outbound

This paper cites BoolQ: Exploring the surprising diﬀiculty of natural yes/no questions,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization BoolQ: Exploring the surprising diﬀiculty of natural yes/no questions,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.794454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c75c707f-210c-40d9-afc6-1c3777098763 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Winogrande: An adversarial winograd schema challenge at scale,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.781362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 07774e3d-75af-4432-95fe-145439c9964c · outbound

This paper cites CoQA: A conversational question answering challenge,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization CoQA: A conversational question answering challenge,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.767056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8e5f0b28-0d2f-4409-b746-da03356590e8 · outbound

This paper cites TruthfulQA: Measuring how models mimic human falsehoods,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization TruthfulQA: Measuring how models mimic human falsehoods,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.754667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ef2c92c-d261-463d-8557-ff5c0d58bee2 · outbound

This paper cites Gender bias in coreference resolution,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Gender bias in coreference resolution,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.742087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e435c4d2-12af-403b-ad16-65440c8c7241 · outbound

This paper cites Moral stories: Situated reasoning about norms, intents, actions, and their consequences,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Moral stories: Situated reasoning about norms, intents, actions, and their consequences,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.728310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ec2ea693-7252-4349-9d7c-207f0e5115e2 · outbound

This paper cites Slimorca: An open dataset of gpt-4 augmented flan reasoning traces, with verification,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Slimorca: An open dataset of gpt-4 augmented flan reasoning traces, with verification,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.715333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d181749-1308-431b-8fe1-ed27e222be25 · outbound

This paper cites Stanford alpaca: An instruction-following llama model,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Stanford alpaca: An instruction-following llama model,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.701877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 998538de-8f42-4f0c-953e-bd7885d8d123 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.687441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 120cc6de-8606-4acb-83c4-2b9428d0a37f · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Pytorch: An imperative style, high-performance deep learning library,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.673814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8e8d49b0-d69f-4fad-9c77-dbca2ba280ce · outbound

This paper cites Transformers: State-of-the-art natural language processing,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Transformers: State-of-the-art natural language processing,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.660947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e36801dc-8371-4c8c-bd2e-738baaf5a2cc · outbound

This paper cites A framework for few-shot language model evaluation,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization A framework for few-shot language model evaluation,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.874721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c40f326-ec9a-4936-9e24-471e370a50d6 · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.878665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb88d44d-bca1-45d9-908c-b83c491f46a9 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization LoRA: Low-rank adaptation of large language models,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:18.648053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aa977938-fc61-427b-8d2a-38d9565740a3 · outbound

This paper cites he” changed to “she.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization he” changed to “she

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T19:58:18.633442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3ce8487c-f668-41b7-872e-21a077f33d1b · outbound

This paper cites Available: https://doi.org/10.1162/tacl_a_00321.

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization Available: https://doi.org/10.1162/tacl_a_00321

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:17.827149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers