Những podcast Igor Melnyk hay nhất (2024)

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[QA] Does Prompt Formatting Have Any Impact on LLM Performance? 6:49

15h ago6:49

6:49

This paper investigates how different prompt templates impact the performance of Large Language Models, revealing significant variations in effectiveness, particularly in code translation tasks. https://arxiv.org/abs//2411.10541 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…

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Does Prompt Formatting Have Any Impact on LLM Performance? 9:14

15h ago9:14

9:14

This paper investigates how different prompt templates impact the performance of Large Language Models, revealing significant variations in effectiveness, particularly in code translation tasks. https://arxiv.org/abs//2411.10541 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…

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[QA] Steering Language Model Refusal with Sparse Autoencoders 7:45

15h ago7:45

7:45

The paper explores using sparse autoencoders to steer language model activations for safer responses, improving refusal behavior while noting potential negative impacts on overall performance. https://arxiv.org/abs//2411.11296 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…

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Steering Language Model Refusal with Sparse Autoencoders 24:02

15h ago24:02

24:02

The paper explores using sparse autoencoders to steer language model activations for safer responses, improving refusal behavior while noting potential negative impacts on overall performance. https://arxiv.org/abs//2411.11296 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…

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[QA] LLaVA-o1: Let Vision Language Models Reason Step-by-Step 7:53

2d ago7:53

7:53

LLaVA-o1 is a novel Vision-Language Model that enhances reasoning in visual question-answering through structured multistage processes, outperforming larger models with fewer training samples. https://arxiv.org/abs//2411.10440 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…

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LLaVA-o1: Let Vision Language Models Reason Step-by-Step 17:55

2d ago17:55

17:55

LLaVA-o1 is a novel Vision-Language Model that enhances reasoning in visual question-answering through structured multistage processes, outperforming larger models with fewer training samples. https://arxiv.org/abs//2411.10440 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…

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[QA] Refusal in LLMs is an Affine Function 7:49

5d ago7:49

7:49

The paper introduces affine concept editing (ACE) for controlling language model behavior through activation manipulation, demonstrating improved precision in managing refusal responses across various prompts. https://arxiv.org/abs//2411.09003 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts:…

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Refusal in LLMs is an Affine Function 8:52

5d ago8:52

8:52

The paper introduces affine concept editing (ACE) for controlling language model behavior through activation manipulation, demonstrating improved precision in managing refusal responses across various prompts. https://arxiv.org/abs//2411.09003 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts:…

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[QA] Cut Your Losses in Large-Vocabulary Language Models 8:00

5d ago8:00

8:00

The paper introduces Cut Cross-Entropy (CCE), a method that significantly reduces memory usage during training of large language models by optimizing cross-entropy loss computation without sacrificing performance. https://arxiv.org/abs//2411.09009 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…

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Cut Your Losses in Large-Vocabulary Language Models 17:27

5d ago17:27

17:27

The paper introduces Cut Cross-Entropy (CCE), a method that significantly reduces memory usage during training of large language models by optimizing cross-entropy loss computation without sacrificing performance. https://arxiv.org/abs//2411.09009 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…

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[QA] Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models 6:57

8d ago6:57

6:57

Add-it is a training-free approach for semantic image editing that seamlessly integrates objects into images using a weighted extended-attention mechanism, achieving state-of-the-art results without fine-tuning. https://arxiv.org/abs//2411.07232 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

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Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models 19:23

8d ago19:23

19:23

Add-it is a training-free approach for semantic image editing that seamlessly integrates objects into images using a weighted extended-attention mechanism, achieving state-of-the-art results without fine-tuning. https://arxiv.org/abs//2411.07232 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

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[QA] Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models 7:19

8d ago7:19

7:19

The SPA framework enhances user experience by generating diverse, high-quality responses from foundation models using synthetic data and data attribution methods, improving performance in code generation and natural language tasks. https://arxiv.org/abs//2411.06722 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_…

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Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models 16:56

8d ago16:56

16:56

The SPA framework enhances user experience by generating diverse, high-quality responses from foundation models using synthetic data and data attribution methods, improving performance in code generation and natural language tasks. https://arxiv.org/abs//2411.06722 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_…

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[QA] Aioli: A unified optimization framework for language model data mixing 6:40

9d ago6:40

6:40

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Aioli: A unified optimization framework for language model data mixing 29:42

9d ago29:42

29:42

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[QA] BALANCING PIPELINE PARALLELISM WITH VOCABULARY PARALLELISM 8:21

9d ago8:21

8:21

This paper addresses imbalanced computation and memory in pipeline parallelism for large language models by partitioning vocabulary layers, reducing communication barriers, and achieving improved throughput and memory balance. https://arxiv.org/abs//2411.05288 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_paper…

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BALANCING PIPELINE PARALLELISM WITH VOCABULARY PARALLELISM 20:22

9d ago20:22

20:22

This paper addresses imbalanced computation and memory in pipeline parallelism for large language models by partitioning vocabulary layers, reducing communication barriers, and achieving improved throughput and memory balance. https://arxiv.org/abs//2411.05288 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_paper…

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[QA] Can Transformers Smell Like Humans? 7:34

10d ago7:34

7:34

This study explores whether pre-trained transformer models of chemical structures align with human olfactory perception, demonstrating their ability to predict expert labels and human ratings of odorants. https://arxiv.org/abs//2411.03038 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: http…

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Can Transformers Smell Like Humans? 18:01

10d ago18:01

18:01

This study explores whether pre-trained transformer models of chemical structures align with human olfactory perception, demonstrating their ability to predict expert labels and human ratings of odorants. https://arxiv.org/abs//2411.03038 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: http…

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[QA] Mixtures of In-Context Learners 7:11

10d ago7:11

7:11

The paper introduces Mixtures of In-Context Learners (MOICL), enhancing in-context learning by optimizing demonstration subsets, improving performance, and reducing memory usage in Transformer LLMs. https://arxiv.org/abs//2411.02830 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://po…

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Mixtures of In-Context Learners 15:37

10d ago15:37

15:37

The paper introduces Mixtures of In-Context Learners (MOICL), enhancing in-context learning by optimizing demonstration subsets, improving performance, and reducing memory usage in Transformer LLMs. https://arxiv.org/abs//2411.02830 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://po…

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[QA] How Far Is Video Generation from World Model: A Physical Law Perspective 8:58

11d ago8:58

8:58

OpenAI's Sora evaluates video generation models' ability to learn physical laws, revealing limitations in generalization and suggesting scaling alone isn't enough for uncovering fundamental principles. https://arxiv.org/abs//2411.02385 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:/…

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How Far Is Video Generation from World Model: A Physical Law Perspective 27:51

11d ago27:51

27:51

OpenAI's Sora evaluates video generation models' ability to learn physical laws, revealing limitations in generalization and suggesting scaling alone isn't enough for uncovering fundamental principles. https://arxiv.org/abs//2411.02385 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:/…

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[QA] ADOPT: Modified Adam Can Converge with Any with the Optimal Rate 7:47

11d ago7:47

7:47

The paper introduces ADOPT, a new adaptive gradient method that resolves Adam's non-convergence issue without bounded noise assumptions, demonstrating superior performance across various deep learning tasks. https://arxiv.org/abs//2411.02853 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…

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ADOPT: Modified Adam Can Converge with Any with the Optimal Rate 15:16

11d ago15:16

15:16

The paper introduces ADOPT, a new adaptive gradient method that resolves Adam's non-convergence issue without bounded noise assumptions, demonstrating superior performance across various deep learning tasks. https://arxiv.org/abs//2411.02853 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…

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[QA] Needle Threading: Can LLMs Follow Threads through Near-Million-Scale Haystacks? 7:24

12d ago7:24

7:24

This study evaluates 17 leading Large Language Models' abilities in complex information retrieval, revealing many are thread-safe but have shorter effective context limits than supported lengths. https://arxiv.org/abs//2411.05000 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…

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Needle Threading: Can LLMs Follow Threads through Near-Million-Scale Haystacks? 14:03

12d ago14:03

14:03

This study evaluates 17 leading Large Language Models' abilities in complex information retrieval, revealing many are thread-safe but have shorter effective context limits than supported lengths. https://arxiv.org/abs//2411.05000 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…

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[QA] Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models 7:53

12d ago7:53

7:53

https://arxiv.org/abs//2411.04996 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…

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Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models 41:18

12d ago41:18

41:18

https://arxiv.org/abs//2411.04996 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/supp…

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[QA] Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex 10:52

13d ago10:52

10:52

The study reveals that task-specific representation learning continues in mice's piriform cortex during overtraining, enhancing classification accuracy despite behavior plateauing, suggesting hidden learning mechanisms at play. https://arxiv.org/abs//2411.03541 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pape…

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Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex 15:09

13d ago15:09

15:09

The study reveals that task-specific representation learning continues in mice's piriform cortex during overtraining, enhancing classification accuracy despite behavior plateauing, suggesting hidden learning mechanisms at play. https://arxiv.org/abs//2411.03541 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pape…

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[QA] How Transformers Solve Propositional Logic Problems: A Mechanistic Analysis 7:22

13d ago7:22

7:22

This study explores how transformers, both small and large, perform complex logical reasoning, identifying key circuits and mechanisms involved in planning and reasoning through a synthetic propositional logic problem. https://arxiv.org/abs//2411.04105 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple …

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How Transformers Solve Propositional Logic Problems: A Mechanistic Analysis 22:34

13d ago22:34

22:34

This study explores how transformers, both small and large, perform complex logical reasoning, identifying key circuits and mechanisms involved in planning and reasoning through a synthetic propositional logic problem. https://arxiv.org/abs//2411.04105 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple …

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[QA] Discovering Data Structures: Nearest Neighbor Search and Beyond 7:59

14d ago7:59

7:59

We present a framework for end-to-end learning of data structures, optimizing query and space complexity, applied to nearest neighbor search and frequency estimation in data streams. https://arxiv.org/abs//2411.03253 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com…

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Discovering Data Structures: Nearest Neighbor Search and Beyond 28:18

14d ago28:18

28:18

We present a framework for end-to-end learning of data structures, optimizing query and space complexity, applied to nearest neighbor search and frequency estimation in data streams. https://arxiv.org/abs//2411.03253 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com…

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[QA] BrainBits: How Much of the Brain are Generative Reconstruction Methods Using? 7:36

14d ago7:36

7:36

The paper examines factors influencing stimulus reconstruction fidelity, revealing that powerful generative models can mislead interpretations of neural signal extraction effectiveness. It proposes improved evaluation metrics for reconstruction methods. https://arxiv.org/abs//2411.02783 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://…

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BrainBits: How Much of the Brain are Generative Reconstruction Methods Using? 15:29

14d ago15:29

15:29

The paper examines factors influencing stimulus reconstruction fidelity, revealing that powerful generative models can mislead interpretations of neural signal extraction effectiveness. It proposes improved evaluation metrics for reconstruction methods. https://arxiv.org/abs//2411.02783 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://…

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[QA] Adapting Language Models via Token Translation 8:13

16d ago8:13

8:13

Sparse Sinkhorn Token Translation (S2T2) improves text compression and inference in new domains by training tailored tokenizers and enabling effective token translation, enhancing performance in language models. https://arxiv.org/abs//2411.00593 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

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Adapting Language Models via Token Translation 9:33

16d ago9:33

9:33

Sparse Sinkhorn Token Translation (S2T2) improves text compression and inference in new domains by training tailored tokenizers and enabling effective token translation, enhancing performance in language models. https://arxiv.org/abs//2411.00593 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

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[QA] Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models 8:29

16d ago8:29

8:29

Specialized Sparse Autoencoders (SSAEs) enhance interpretability of foundation models by effectively capturing rare concepts, improving classification accuracy, and revealing insights into subdomain representations. https://arxiv.org/abs//2411.00743 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

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Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models 26:54

16d ago26:54

26:54

Specialized Sparse Autoencoders (SSAEs) enhance interpretability of foundation models by effectively capturing rare concepts, improving classification accuracy, and revealing insights into subdomain representations. https://arxiv.org/abs//2411.00743 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

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[QA] Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters 7:51

18d ago7:51

7:51

Tokenformer introduces a scalable architecture that enhances Transformers' efficiency by using token-parameter attention, allowing for incremental scaling without retraining, thus reducing computational costs significantly. https://arxiv.org/abs//2410.23168 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers A…

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Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters 19:10

18d ago19:10

19:10

Tokenformer introduces a scalable architecture that enhances Transformers' efficiency by using token-parameter attention, allowing for incremental scaling without retraining, thus reducing computational costs significantly. https://arxiv.org/abs//2410.23168 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers A…

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[QA] $100K or 100 Days: Trade-offs when Pre-Training with Academic Resources 7:22

18d ago7:22

7:22

This paper challenges the assumption that academic researchers can't pre-train models, providing benchmarks and insights on optimizing GPU resources for efficient model training. https://arxiv.org/abs//2410.23261 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/…

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$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources 16:51

18d ago16:51

16:51

This paper challenges the assumption that academic researchers can't pre-train models, providing benchmarks and insights on optimizing GPU resources for efficient model training. https://arxiv.org/abs//2410.23261 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/…

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[QA] What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective 7:59

19d ago7:59

7:59

This study analyzes layer-wise gradients in LLMs, revealing that slow thinking enhances learning stability and response correctness, while fast thinking shows larger gradient variations. https://arxiv.org/abs//2410.23743 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple…

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What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective 15:27

19d ago15:27

15:27

This study analyzes layer-wise gradients in LLMs, revealing that slow thinking enhances learning stability and response correctness, while fast thinking shows larger gradient variations. https://arxiv.org/abs//2410.23743 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple…

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[QA] Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters 7:28

20d ago7:28

7:28

Tokenformer introduces a scalable architecture that enhances Transformers' efficiency by treating model parameters as tokens, allowing for flexible scaling without retraining, significantly reducing computational costs. https://arxiv.org/abs//2410.23168 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple…

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Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters 19:38

20d ago19:38

19:38

Tokenformer introduces a scalable architecture that enhances Transformers' efficiency by treating model parameters as tokens, allowing for flexible scaling without retraining, significantly reducing computational costs. https://arxiv.org/abs//2410.23168 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple…

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