Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability | Paper Citation Record

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This paper cites Language models are few-shot learners,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Language models are few-shot learners,

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This paper cites Language models are unsupervised multitask learners,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Language models are unsupervised multitask learners,

Reference 2

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This paper cites How many data points is a prompt worth?.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability How many data points is a prompt worth?

Reference 3

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This paper cites A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models

Reference 4

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This paper cites Orca 2: Teaching small language models how to reason,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Orca 2: Teaching small language models how to reason,

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This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Chain-of-thought prompting elicits reasoning in large language models,

Reference 6

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This paper cites Tree of thoughts: deliberate problem solving with large language models,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Tree of thoughts: deliberate problem solving with large language models,

Reference 7

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This paper cites Evaluating Consistency and Reasoning Capabilities of Large Language Models.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Evaluating Consistency and Reasoning Capabilities of Large Language Models

Reference 8

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This paper cites LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models,

Reference 9

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Observation e19bff66-49ed-4f99-af76-1264f20f4c32 · outbound

This paper cites Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models,

Reference 10

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This paper cites Large Language Models: A Survey.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Large Language Models: A Survey

Reference 11

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This paper cites PaLM: Scaling Language Modeling with Pathways.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability PaLM: Scaling Language Modeling with Pathways

Reference 12

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Observation 5f8d12ff-88b8-4e3b-ada2-ec0858bbe784 · outbound

This paper cites Distilling Task-Specific Knowledge from BERT into Simple Neural Networks.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

Reference 13

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This paper cites TinyBERT: Distilling BERT for Natural Language Understanding,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability TinyBERT: Distilling BERT for Natural Language Understanding,

Reference 14

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This paper cites A survey on model compression for large language models,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability A survey on model compression for large language models,

Reference 15

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This paper cites Robustness-Reinforced Knowledge Distillation With Correlation Distance and Network Pruning,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Robustness-Reinforced Knowledge Distillation With Correlation Distance and Network Pruning,

Reference 16

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This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 17

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This paper cites PanDa: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability PanDa: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation,

Reference 18

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Observation 7be016a7-3828-4fea-b47e-b9df5de62d82 · outbound

This paper cites Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights

Reference 19

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This paper cites Improve Student‘s Reasoning Generalizability through Cascading Decomposed CoTs Distillation,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Improve Student‘s Reasoning Generalizability through Cascading Decomposed CoTs Distillation,

Reference 20

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This paper cites Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillation.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillation

Reference 21

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This paper cites One teacher is enough? pre-trained language model distillation from multiple teachers,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability One teacher is enough? pre-trained language model distillation from multiple teachers,

Reference 22

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This paper cites Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillation.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillation

Reference 23

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability We’re getting a better idea of AI’s true carbon footprint,

Reference 24

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This paper cites Distilling Large Vision-Language Model with Out-of-Distribution Generalizability ,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Distilling Large Vision-Language Model with Out-of-Distribution Generalizability ,

Reference 25

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This paper cites How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

Reference 26

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This paper cites Energy and policy considerations for modern deep learning research,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Energy and policy considerations for modern deep learning research,

Reference 27

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Towards a rigorous science of interpretable machine learning,

Reference 28

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Code Llama: Open Foundation Models for Code

Reference 29

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information

Reference 30

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Explainability in AI Based Applications: A Framework for Comparing Different Techniques

Reference 31

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Multimodal explanations: Justifying decisions and pointing to the evidence,

Reference 32

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Textual explanations for self-driving vehicles,

Reference 33

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability SCOTT: Self-consistent chain-of-thought distillation,

Reference 34

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability The Impact of Imperfect XAI on Human-AI Decision-Making

Reference 35

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability GPT-NeoX-20B: An open-source autoregressive language model,

Reference 36

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Constitutional AI: Harmlessness from AI Feedback

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability MiniLLM: Knowledge distillation of large language models,

Reference 38

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Distilling the Knowledge in a Neural Network

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Collaborative Distillation for Ultra-Resolution Universal Style Transfer

Reference 40

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability ERDL: Efficient Retrieval Framework Based on Distillation from Large Language Models,

Reference 41

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This paper cites Learning Efficient Object Detection Models with Knowledge Distillation,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Learning Efficient Object Detection Models with Knowledge Distillation,

Reference 42

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Triplet Distillation for Deep Face Recognition

Reference 43

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This paper cites Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models,

Reference 44

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This paper cites A Survey on Knowledge Distillation of Large Language Models.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability A Survey on Knowledge Distillation of Large Language Models

Reference 45

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Lora: Low-rank adaptation of large language models,

Reference 46

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability On the effectiveness of adapter-based tuning for pretrained language model adaptation,

Reference 47

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability The Power of Scale for Parameter-Efficient Prompt Tuning,

Reference 48

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This paper cites Efficiency Optimization of Large-Scale Language Models Based on Deep Learning in Natural Language Processing Tasks,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Efficiency Optimization of Large-Scale Language Models Based on Deep Learning in Natural Language Processing Tasks,

Reference 49

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models

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This paper cites A survey on the explainability of su- pervised machine learning,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability A survey on the explainability of su- pervised machine learning,

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Baize: An Open- Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data,

Reference 52

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This paper cites Causability and explainability of artificial intelligence in medicine,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Causability and explainability of artificial intelligence in medicine,

Reference 53

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Large Language Models Are Reasoning Teachers

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Specializing Smaller Language Models towards Multi-Step Reasoning

Reference 55

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Teaching small language models to reason,

Reference 56

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 57

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This paper cites Explain Yourself! Leveraging Language Models for Commonsense Reasoning,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Explain Yourself! Leveraging Language Models for Commonsense Reasoning,

Reference 58

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This paper cites Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA,

Reference 59

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

Reference 60

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Measuring Association Between Labels and Free-Text Rationales,

Reference 61

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Self-consistency improves chain of thought reasoning in language models,

Reference 62

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 63

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability RLCD: Reinforcement learning from contrastive distillation for LM alignment,

Reference 64

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge,

Reference 65

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Transformers: State- of-the-art natural language processing,

Reference 66

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Generating visual explanations,

Reference 67

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This paper cites Available: https://openreview.net/forum?id=v3XXtxWK i6.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Available: https://openreview.net/forum?id=v3XXtxWK i6

Reference 68

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 69

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This paper cites Exploring Evaluation Methods for Interpretable Machine Learning: A Survey,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Exploring Evaluation Methods for Interpretable Machine Learning: A Survey,

Reference 70

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This paper cites Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics,.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics,

Reference 71

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This paper cites Prolific · Quickly find research participants you can trust.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Prolific · Quickly find research participants you can trust

Reference 72

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Oxford Learner’s Dictionaries | Find definitions, trans- lations, and grammar explanations at Oxford Learner’s Dictionaries,

Reference 73

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Interpretation Quality Score for Measuring the Quality of interpretability methods

Reference 74

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Machine Learning Interpretability: A Survey on Methods and Metrics,

Reference 75

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?

Reference 76

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability REV: Information-theoretic evaluation of free-text rationales,

Reference 77

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability A collection of principles for guiding and evaluating large language models

Reference 78

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This paper cites Available: https://www.oxfordlearnersdictionaries.com/.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Available: https://www.oxfordlearnersdictionaries.com/

Reference 79

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance,

Reference 80

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Exploratory Data Analysis,

Reference 81

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Likert scales, levels of measurement and the “laws

Reference 82

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Measuring the Quality of Explanations: The System Causability Scale (SCS): Comparing Human and Machine Explanations,

Reference 83

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability A Critique and Improvement of the

Reference 84

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Reference 85

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Bhattacherjee, Social Science Research: Principles, Methods and Practices

Reference 86

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Nonparametric Pairwise Multiple Comparisons in Independent Groups using Dunn’s Test,

Reference 89

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

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This paper cites Available: https://www.prolific.com/.

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Available: https://www.prolific.com/

Reference 2023

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Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability Available: https://aclanthology.org/2024.tacl-1.85/

Reference 2024

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