Fair-GPTQ: Bias-Aware Quantization for Large Language Models | Paper Citation Record

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This paper cites Pi QA : Reasoning about physical commonsense in natural language.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Pi QA : Reasoning about physical commonsense in natural language

Reference 1

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This paper cites Man is to computer programmer as woman is to homemaker? debiasing word embeddings.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Man is to computer programmer as woman is to homemaker? debiasing word embeddings

Reference 2

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unresolved cited work

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Language models are few-shot learners

Reference 4

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This paper cites Semantics derived automatically from language corpora contain human-like biases.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Semantics derived automatically from language corpora contain human-like biases

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This paper cites Quip: 2-bit quantization of large language models with guarantees.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Quip: 2-bit quantization of large language models with guarantees

Reference 6

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

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

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This paper cites Racial bias in hate speech and abusive language detection datasets.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Racial bias in hate speech and abusive language detection datasets

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This paper cites Gpt3.int8(): 8-bit matrix multiplication for transformers at scale.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Gpt3.int8(): 8-bit matrix multiplication for transformers at scale

Reference 9

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unresolved cited work

Reference 10

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This paper cites A mathematical framework for transformer circuits.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models A mathematical framework for transformer circuits

Reference 11

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This paper cites From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models

Reference 12

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This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Optimal brain compression: A framework for accurate post-training quantization and pruning

Reference 13

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This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 14

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Marlin: Mixed-precision auto-regressive parallel inference on large language models

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Transformer feed-forward layers are key-value memories

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This paper cites Understanding the effect of model compression on social bias in large language models.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Understanding the effect of model compression on social bias in large language models

Reference 17

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models The Llama 3 Herd of Models

Reference 18

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This paper cites Accelerate: Training and inference at scale made simple, efficient and adaptable.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Accelerate: Training and inference at scale made simple, efficient and adaptable

Reference 19

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Optimal brain surgeon and general network pruning

Reference 20

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This paper cites Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models

Reference 21

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Accurate post training quantization with small calibration sets

Reference 22

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 23

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Perplexity—a measure of the difficulty of speech recognition tasks

Reference 24

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Mistral 7B

Reference 25

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Scaling Laws for Neural Language Models

Reference 26

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Optimal brain damage

Reference 27

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This paper cites Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models

Reference 28

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Towards debiasing sentence representations

Reference 29

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

Reference 30

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models B lack is to criminal as C aucasian is to police: Detecting and removing multiclass bias in word embeddings

Reference 31

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Social bias probing: Fairness benchmarking for language models

Reference 32

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unresolved cited work

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models An empirical survey of the effectiveness of debiasing techniques for pre-trained language models

Reference 34

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Pointer Sentinel Mixture Models

Reference 35

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unresolved cited work

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models LSDS em 2017 shared task: The story cloze test

Reference 37

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models S tereo S et: Measuring stereotypical bias in pretrained language models

Reference 38

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Up or down? adaptive rounding for post-training quantization

Reference 39

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unresolved cited work

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Social-group-agnostic bias mitigation via the stereotype content model

Reference 41

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models BBQ : A hand-built bias benchmark for question answering

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Pytorch: An imperative style, high-performance deep learning library

Reference 43

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Layered bias: Interpreting bias in pretrained large language models

Reference 44

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J

Reference 45

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models A comparative study on the impact of model compression techniques on fairness in language models

Reference 46

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Null it out: Guarding protected attributes by iterative nullspace projection

Reference 47

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP

Reference 48

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Upstream mitigation is not all you need: Testing the bias transfer hypothesis in pre-trained language models

Reference 49

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Transformers: State-of-the-art natural language processing

Reference 50

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 51

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Beyond perplexity: Multi-dimensional safety evaluation of LLM compression

Reference 52

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

Reference 53

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unresolved cited work

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 55

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This paper cites Unibias: Unveiling and mitigating llm bias through internal attention and ffn manipulation.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Unibias: Unveiling and mitigating llm bias through internal attention and ffn manipulation

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models A survey on model compression for large language models

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Fair-GPTQ: Bias-Aware Quantization for Large Language Models Mielke, Hanna Wallach, and Ryan Cotterell

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