Best Igor Melnyk Podcasts (2024)

1
[QA] SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection 8:29

19m ago8:29

8:29

This paper presents a framework using a small language model for initial hallucination detection, followed by a large language model for detailed explanations, optimizing real-time interpretable detection. https://arxiv.org/abs//2408.12748 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…

1
SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection 9:51

20m ago9:51

9:51

This paper presents a framework using a small language model for initial hallucination detection, followed by a large language model for detailed explanations, optimizing real-time interpretable detection. https://arxiv.org/abs//2408.12748 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…

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[QA] How Diffusion Models Learn to Factorize and Compose 8:14

18m ago8:14

8:14

This study explores how diffusion models learn compositional representations through controlled experiments, revealing their ability to encode features but limited interpolation over unseen values, enhancing training efficiency. https://arxiv.org/abs//2408.13256 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…

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How Diffusion Models Learn to Factorize and Compose 20:36

18m ago20:36

20:36

This study explores how diffusion models learn compositional representations through controlled experiments, revealing their ability to encode features but limited interpolation over unseen values, enhancing training efficiency. https://arxiv.org/abs//2408.13256 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…

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[QA] FERRET: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique 7:48

24m ago7:48

7:48

FERRET enhances adversarial prompt generation for large language models, improving attack success rates and efficiency over RAINBOW TEAMING while ensuring effective prompts across various model sizes. https://arxiv.org/abs//2408.10701 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://…

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FERRET: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique 17:44

25m ago17:44

17:44

FERRET enhances adversarial prompt generation for large language models, improving attack success rates and efficiency over RAINBOW TEAMING while ensuring effective prompts across various model sizes. https://arxiv.org/abs//2408.10701 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://…

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[QA] Scalable Autoregressive Image Generation with Mamba 7:11

29m ago7:11

7:11

AiM is an autoregressive image generative model using Mamba architecture, achieving superior quality and speed in image generation while maintaining efficient long-sequence modeling capabilities. https://arxiv.org/abs//2408.12245 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…

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Scalable Autoregressive Image Generation with Mamba 17:27

30m ago17:27

17:27

AiM is an autoregressive image generative model using Mamba architecture, achieving superior quality and speed in image generation while maintaining efficient long-sequence modeling capabilities. https://arxiv.org/abs//2408.12245 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…

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[QA] TableBench: A Comprehensive and Complex Benchmark for Table Question Answering 7:55

1h ago7:55

7:55

The paper investigates LLMs' challenges with real-world tabular data, proposing the TableBench benchmark and TABLELLM model, highlighting significant gaps between academic performance and industrial application. https://arxiv.org/abs//2408.09174 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

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TableBench: A Comprehensive and Complex Benchmark for Table Question Answering 21:59

1h ago21:59

21:59

The paper investigates LLMs' challenges with real-world tabular data, proposing the TableBench benchmark and TABLELLM model, highlighting significant gaps between academic performance and industrial application. https://arxiv.org/abs//2408.09174 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

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[QA] FocusLLM: Scaling LLM's Context by Parallel Decoding 7:35

1h ago7:35

7:35

FocusLLM enhances decoder-only LLMs by efficiently processing long contexts, improving performance on long-context tasks while reducing training costs and maintaining strong language modeling capabilities. https://arxiv.org/abs//2408.11745 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…

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FocusLLM: Scaling LLM's Context by Parallel Decoding 20:55

1h ago20:55

20:55

FocusLLM enhances decoder-only LLMs by efficiently processing long contexts, improving performance on long-context tasks while reducing training costs and maintaining strong language modeling capabilities. https://arxiv.org/abs//2408.11745 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…

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[QA] Sapiens: Foundation for Human Vision Models 7:49

21m ago7:49

7:49

Sapiens is a versatile model family for human-centric vision tasks, achieving state-of-the-art performance through self-supervised pretraining and scalable design, excelling in pose estimation, segmentation, depth, and normal prediction. https://arxiv.org/abs//2408.12569 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@…

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Sapiens: Foundation for Human Vision Models 22:52

21m ago22:52

22:52

Sapiens is a versatile model family for human-centric vision tasks, achieving state-of-the-art performance through self-supervised pretraining and scalable design, excelling in pose estimation, segmentation, depth, and normal prediction. https://arxiv.org/abs//2408.12569 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@…

1
[QA] Show-o: One Single Transformer to Unify Multimodal Understanding and Generation 7:25

36m ago7:25

7:25

Show-o is a unified transformer model that integrates multimodal understanding and generation, outperforming existing models in various vision-language tasks while supporting diverse input-output modalities. https://arxiv.org/abs//2408.12528 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…

1
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation 28:14

37m ago28:14

28:14

Show-o is a unified transformer model that integrates multimodal understanding and generation, outperforming existing models in various vision-language tasks while supporting diverse input-output modalities. https://arxiv.org/abs//2408.12528 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…

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[QA] Jamba-1.5: Hybrid Transformer-Mamba Models at Scale 7:22

3h ago7:22

7:22

Jamba-1.5 introduces instruction-tuned large language models with high throughput, low memory usage, and extensive context length, outperforming competitors while being publicly available under an open model license. https://arxiv.org/abs//2408.12570 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…

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Jamba-1.5: Hybrid Transformer-Mamba Models at Scale 16:53

3h ago16:53

16:53

Jamba-1.5 introduces instruction-tuned large language models with high throughput, low memory usage, and extensive context length, outperforming competitors while being publicly available under an open model license. https://arxiv.org/abs//2408.12570 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…

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[QA] Hermes 3 Technical Report 7:45

3h ago7:45

7:45

Hermes 3 is a neutrally-aligned instruct-tuned model with strong reasoning and creativity, achieving state-of-the-art performance on benchmarks, with weights available on Hugging Face. https://arxiv.org/abs//2408.11857 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…

1
Hermes 3 Technical Report 11:21

3h ago11:21

11:21

Hermes 3 is a neutrally-aligned instruct-tuned model with strong reasoning and creativity, achieving state-of-the-art performance on benchmarks, with weights available on Hugging Face. https://arxiv.org/abs//2408.11857 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…

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[QA] LLM Pruning and Distillation in Practice: The Minitron Approach 7:23

18m ago7:23

7:23

https://arxiv.org/abs//2408.11796 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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LLM Pruning and Distillation in Practice: The Minitron Approach 12:36

2d ago12:36

12:36

https://arxiv.org/abs//2408.11796 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] Approaching Deep Learning through the Spectral Dynamics of Weights 7:26

20m ago7:26

7:26

This paper explores spectral dynamics of weights in deep learning, revealing optimization biases, enhancing weight decay effects, and distinguishing between memorizing and generalizing networks across various tasks. https://arxiv.org/abs//2408.11804 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

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Approaching Deep Learning through the Spectral Dynamics of Weights 26:45

21m ago26:45

26:45

This paper explores spectral dynamics of weights in deep learning, revealing optimization biases, enhancing weight decay effects, and distinguishing between memorizing and generalizing networks across various tasks. https://arxiv.org/abs//2408.11804 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

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[QA] Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations 8:04

2d ago8:04

8:04

The paper challenges the Linear Representation Hypothesis, showing that gated recurrent neural networks encode token sequences using magnitude rather than direction, suggesting broader interpretability in neural network research. https://arxiv.org/abs//2408.10920 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pa…

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Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations 21:03

1h ago21:03

21:03

The paper challenges the Linear Representation Hypothesis, showing that gated recurrent neural networks encode token sequences using magnitude rather than direction, suggesting broader interpretability in neural network research. https://arxiv.org/abs//2408.10920 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pa…

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[QA] Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model 7:53

1h ago7:53

7:53

Transfusion is a multi-modal training method combining language modeling and diffusion, achieving superior performance in generating images and text with models up to 7B parameters. https://arxiv.org/abs//2408.11039 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…

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Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model 24:23

1h ago24:23

24:23

Transfusion is a multi-modal training method combining language modeling and diffusion, achieving superior performance in generating images and text with models up to 7B parameters. https://arxiv.org/abs//2408.11039 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…

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[QA] Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models 8:37

2d ago8:37

8:37

The paper presents MOHAWK, a method for distilling Transformers into state space models, achieving strong performance with significantly less training data and computational resources. https://arxiv.org/abs//2408.10189 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…

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Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models 31:52

1h ago31:52

31:52

The paper presents MOHAWK, a method for distilling Transformers into state space models, achieving strong performance with significantly less training data and computational resources. https://arxiv.org/abs//2408.10189 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.c…

1
[QA] JPEG-LM: LLMs as Image Generators with Canonical Codec Representations 7:47

5d ago7:47

7:47

This paper proposes using canonical codecs for image and video generation in autoregressive models, demonstrating improved efficiency and effectiveness over traditional pixel-based and vector quantization methods. https://arxiv.org/abs//2408.08459 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…

1
JPEG-LM: LLMs as Image Generators with Canonical Codec Representations 20:04

1d ago20:04

20:04

This paper proposes using canonical codecs for image and video generation in autoregressive models, demonstrating improved efficiency and effectiveness over traditional pixel-based and vector quantization methods. https://arxiv.org/abs//2408.08459 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…

1
[QA] TextCAVs: Debugging vision models using text 7:19

24m ago7:19

7:19

TextCAVs is a novel method for generating concept activation vectors using text descriptions, reducing the need for labeled image data in deep learning model interpretability, particularly in medical applications. https://arxiv.org/abs//2408.08652 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…

1
TextCAVs: Debugging vision models using text 9:33

6d ago9:33

9:33

TextCAVs is a novel method for generating concept activation vectors using text descriptions, reducing the need for labeled image data in deep learning model interpretability, particularly in medical applications. https://arxiv.org/abs//2408.08652 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…

1
[QA] Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers 9:08

3d ago9:08

9:08

The paper presents rStar, a self-play mutual reasoning method that enhances small language models' reasoning abilities without fine-tuning, achieving significant accuracy improvements across various reasoning tasks. https://arxiv.org/abs//2408.06195 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

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Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers 16:46

3d ago16:46

16:46

The paper presents rStar, a self-play mutual reasoning method that enhances small language models' reasoning abilities without fine-tuning, achieving significant accuracy improvements across various reasoning tasks. https://arxiv.org/abs//2408.06195 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

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[QA] Towards flexible perception with visual memory 7:18

2h ago7:18

7:18

https://arxiv.org/abs//2408.08172 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…

1
Towards flexible perception with visual memory 17:09

2h ago17:09

17:09

https://arxiv.org/abs//2408.08172 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…

1
[QA] I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm 7:45

4d ago7:45

7:45

The paper introduces I-SHEEP, a continuous self-alignment paradigm for LLMs, significantly improving performance on various benchmarks compared to traditional one-time alignment methods. https://arxiv.org/abs//2408.08072 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple…

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I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm 17:47

2h ago17:47

17:47

The paper introduces I-SHEEP, a continuous self-alignment paradigm for LLMs, significantly improving performance on various benchmarks compared to traditional one-time alignment methods. https://arxiv.org/abs//2408.08072 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple…

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[QA] BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts 7:43

7d ago7:43

7:43

BAM enhances Mixture of Experts by fully utilizing dense model parameters, improving efficiency and performance in large language models, surpassing baselines in perplexity and downstream tasks. https://arxiv.org/abs//2408.08274 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…

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BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts 20:07

5d ago20:07

20:07

BAM enhances Mixture of Experts by fully utilizing dense model parameters, improving efficiency and performance in large language models, surpassing baselines in perplexity and downstream tasks. https://arxiv.org/abs//2408.08274 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…

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[QA] Can Large Language Models Understand Symbolic Graphics Programs? 7:14

9d ago7:14

7:14

This paper evaluates large language models' understanding of symbolic graphics programs, introducing a benchmark and a method, Symbolic Instruction Tuning, to enhance their visual reasoning capabilities. https://arxiv.org/abs//2408.08313 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https…

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Can Large Language Models Understand Symbolic Graphics Programs? 23:48

5m ago23:48

23:48

This paper evaluates large language models' understanding of symbolic graphics programs, introducing a benchmark and a method, Symbolic Instruction Tuning, to enhance their visual reasoning capabilities. https://arxiv.org/abs//2408.08313 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https…

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[QA] Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents 7:55

34m ago7:55

7:55

This paper presents a Monte-Carlo Tree Search approach to enhance LLMs' performance in multi-step reasoning tasks, achieving significant improvements in web navigation and decision-making capabilities. https://arxiv.org/abs//2408.07199 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:/…

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Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents 29:15

6d ago29:15

29:15

This paper presents a Monte-Carlo Tree Search approach to enhance LLMs' performance in multi-step reasoning tasks, achieving significant improvements in web navigation and decision-making capabilities. https://arxiv.org/abs//2408.07199 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:/…

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[QA] Generative Photomontage 9:11

16m ago9:11

9:11

This paper presents a framework for creating desired images by compositing user-selected parts from generated images, enhancing flexibility and quality in image generation through a novel blending technique. https://arxiv.org/abs//2408.07116 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…

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Generative Photomontage 24:05

16m ago24:05

24:05

This paper presents a framework for creating desired images by compositing user-selected parts from generated images, enhancing flexibility and quality in image generation through a novel blending technique. https://arxiv.org/abs//2408.07116 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…

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[QA] Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models 7:26

8d ago7:26

7:26

The paper identifies "semantic leakage" in language models, revealing how irrelevant prompt information influences outputs, and proposes methods for detection and evaluation across multiple languages and scenarios. https://arxiv.org/abs//2408.06518 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podc…

1
Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models 16:13

9d ago16:13

16:13

The paper identifies "semantic leakage" in language models, revealing how irrelevant prompt information influences outputs, and proposes methods for detection and evaluation across multiple languages and scenarios. https://arxiv.org/abs//2408.06518 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podc…

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