Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers Support this podcast: https://podcasters.spotify.com/pod/s ...
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[QA] Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon
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Memorization in language models is complex and influenced by various factors. A taxonomy approach helps understand and predict memorization patterns. https://arxiv.org/abs//2406.17746 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id169247…
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Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon
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Memorization in language models is complex and influenced by various factors. A taxonomy approach helps understand and predict memorization patterns. https://arxiv.org/abs//2406.17746 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id169247…
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[QA] Adam-mini: Use Fewer Learning Rates To Gain More
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Adam-mini optimizer reduces memory footprint by using average learning rates within parameter blocks, achieving performance comparable to AdamW with significantly less memory. https://arxiv.org/abs//2406.16793 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/pod…
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Adam-mini: Use Fewer Learning Rates To Gain More
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Adam-mini optimizer reduces memory footprint by using average learning rates within parameter blocks, achieving performance comparable to AdamW with significantly less memory. https://arxiv.org/abs//2406.16793 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/pod…
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[QA] Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
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SEPs offer a cost-effective method for detecting hallucinations in Large Language Models by approximating semantic entropy from hidden states, improving efficiency and generalization. https://arxiv.org/abs//2406.15927 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.co…
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Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
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SEPs offer a cost-effective method for detecting hallucinations in Large Language Models by approximating semantic entropy from hidden states, improving efficiency and generalization. https://arxiv.org/abs//2406.15927 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.co…
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[QA] Evaluating Numerical Reasoning in Text-to-Image Models
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Text-to-image models struggle with numerical reasoning tasks, showing limitations in generating exact numbers, understanding quantifiers, zero, and advanced concepts. GECKONUM benchmark is introduced for evaluation. https://arxiv.org/abs//2406.14774 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…
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Evaluating Numerical Reasoning in Text-to-Image Models
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Text-to-image models struggle with numerical reasoning tasks, showing limitations in generating exact numbers, understanding quantifiers, zero, and advanced concepts. GECKONUM benchmark is introduced for evaluation. https://arxiv.org/abs//2406.14774 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…
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The paper introduces Advantage Alignment, an algorithm for opponent shaping in AI agents to find socially beneficial equilibria efficiently, proving its effectiveness in various social dilemmas. https://arxiv.org/abs//2406.14662 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…
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The paper introduces Advantage Alignment, an algorithm for opponent shaping in AI agents to find socially beneficial equilibria efficiently, proving its effectiveness in various social dilemmas. https://arxiv.org/abs//2406.14662 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…
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[QA] Transcendence: Generative Models Can Outperform The Experts That Train Them
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Generative models can surpass human performance when trained on data generated by humans, demonstrated by a chess-playing transformer model achieving better performance than human players. https://arxiv.org/abs//2406.11741 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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Transcendence: Generative Models Can Outperform The Experts That Train Them
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Generative models can surpass human performance when trained on data generated by humans, demonstrated by a chess-playing transformer model achieving better performance than human players. https://arxiv.org/abs//2406.11741 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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[QA] Refusal in Language Models Is Mediated by a Single Direction
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Study explores refusal behavior in chat models, identifying a one-dimensional subspace mediating refusal. Proposes a method to disable refusal while preserving other capabilities, highlighting safety fine-tuning limitations. https://arxiv.org/abs//2406.11717 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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Refusal in Language Models Is Mediated by a Single Direction
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Study explores refusal behavior in chat models, identifying a one-dimensional subspace mediating refusal. Proposes a method to disable refusal while preserving other capabilities, highlighting safety fine-tuning limitations. https://arxiv.org/abs//2406.11717 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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[QA] Instruction Pre-Training: Language Models are Supervised Multitask Learners
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The paper introduces Instruction Pre-Training, a framework for supervised multitask pre-training of language models using instruction-response pairs, showing improved generalization and performance. https://arxiv.org/abs//2406.14491 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://po…
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Instruction Pre-Training: Language Models are Supervised Multitask Learners
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The paper introduces Instruction Pre-Training, a framework for supervised multitask pre-training of language models using instruction-response pairs, showing improved generalization and performance. https://arxiv.org/abs//2406.14491 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://po…
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[QA] Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
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Long-context language models (LCLMs) show promise in revolutionizing tasks without external tools, as demonstrated by LOFT benchmark's evaluation of LCLMs' performance in complex contexts. https://arxiv.org/abs//2406.13121 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
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Long-context language models (LCLMs) show promise in revolutionizing tasks without external tools, as demonstrated by LOFT benchmark's evaluation of LCLMs' performance in complex contexts. https://arxiv.org/abs//2406.13121 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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[QA] RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
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https://arxiv.org/abs//2406.14532 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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RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
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https://arxiv.org/abs//2406.14532 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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Proposes Easy Consistency Tuning (ECT) for training consistency models, improving efficiency significantly. Achieves high quality results on CIFAR-10 in just 1 hour on a single GPU. https://arxiv.org/abs//2406.14548 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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Proposes Easy Consistency Tuning (ECT) for training consistency models, improving efficiency significantly. Achieves high quality results on CIFAR-10 in just 1 hour on a single GPU. https://arxiv.org/abs//2406.14548 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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[QA] What Are the Odds? Language Models Are Capable of Probabilistic Reasoning
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Paper evaluates language models' probabilistic reasoning abilities using statistical distributions. Three tasks assessed with different contextual inputs. Models can infer distributions with real-world context and simplified assumptions. https://arxiv.org/abs//2406.12830 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@…
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What Are the Odds? Language Models Are Capable of Probabilistic Reasoning
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Paper evaluates language models' probabilistic reasoning abilities using statistical distributions. Three tasks assessed with different contextual inputs. Models can infer distributions with real-world context and simplified assumptions. https://arxiv.org/abs//2406.12830 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@…
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[QA] Adversarial Attacks on Multimodal Agents
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The paper explores safety risks posed by multimodal agents and demonstrates attacks using adversarial text strings to manipulate VLMs, with varying success rates based on different models. https://arxiv.org/abs//2406.12814 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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Adversarial Attacks on Multimodal Agents
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The paper explores safety risks posed by multimodal agents and demonstrates attacks using adversarial text strings to manipulate VLMs, with varying success rates based on different models. https://arxiv.org/abs//2406.12814 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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The paper explores defenses to improve KataGo's performance against adversarial attacks in Go, finding some defenses effective but none able to withstand adaptive attacks. https://arxiv.org/abs//2406.12843 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast…
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The paper explores defenses to improve KataGo's performance against adversarial attacks in Go, finding some defenses effective but none able to withstand adaptive attacks. https://arxiv.org/abs//2406.12843 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast…
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[QA] Autoregressive Image Generation without Vector Quantization
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Proposing a diffusion-based approach for autoregressive modeling in continuous-valued space, eliminating the need for discrete tokens and achieving strong results in image generation. https://arxiv.org/abs//2406.11838 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.co…
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Autoregressive Image Generation without Vector Quantization
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Proposing a diffusion-based approach for autoregressive modeling in continuous-valued space, eliminating the need for discrete tokens and achieving strong results in image generation. https://arxiv.org/abs//2406.11838 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.co…
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[QA] Measuring memorization in RLHF for code completion
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https://arxiv.org/abs//2406.11715 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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Measuring memorization in RLHF for code completion
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https://arxiv.org/abs//2406.11715 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] Bootstrapping Language Models with DPO Implicit Rewards
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The paper introduces DICE, a method for aligning large language models using implicit rewards from DPO. DICE outperforms Gemini Pro on AlpacaEval 2 with 8B parameters and no external feedback. https://arxiv.org/abs//2406.09760 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…
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Bootstrapping Language Models with DPO Implicit Rewards
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The paper introduces DICE, a method for aligning large language models using implicit rewards from DPO. DICE outperforms Gemini Pro on AlpacaEval 2 with 8B parameters and no external feedback. https://arxiv.org/abs//2406.09760 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…
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[QA] Ad Auctions for LLMs via Retrieval Augmented Generation
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Novel auction mechanisms for ad allocation and pricing in large language models (LLMs) are proposed, maximizing social welfare and ensuring fairness. Empirical evaluation supports the approach's feasibility and effectiveness. https://arxiv.org/abs//2406.09459 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers…
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Ad Auctions for LLMs via Retrieval Augmented Generation
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Novel auction mechanisms for ad allocation and pricing in large language models (LLMs) are proposed, maximizing social welfare and ensuring fairness. Empirical evaluation supports the approach's feasibility and effectiveness. https://arxiv.org/abs//2406.09459 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers…
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[QA] An Empirical Study of Mamba-based Language Models
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Mamba models challenge Transformers at larger scales, with Mamba-2-Hybrid surpassing Transformers on various tasks, showing potential for efficient token generation. https://arxiv.org/abs//2406.07887 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv…
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An Empirical Study of Mamba-based Language Models
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Mamba models challenge Transformers at larger scales, with Mamba-2-Hybrid surpassing Transformers on various tasks, showing potential for efficient token generation. https://arxiv.org/abs//2406.07887 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv…
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[QA] Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
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Preference-based learning for language models is crucial for enhancing generation quality. This study explores key components' impact and suggests strategies for effective learning. https://arxiv.org/abs//2406.09279 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
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Preference-based learning for language models is crucial for enhancing generation quality. This study explores key components' impact and suggests strategies for effective learning. https://arxiv.org/abs//2406.09279 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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[QA] What If We Recaption Billions of Web Images with LLaMA-3?
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The paper introduces Recap-DataComp-1B, an enhanced dataset created using LLaMA-3-8B to improve vision-language model training, showing benefits in performance across various tasks. https://arxiv.org/abs//2406.08478 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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What If We Recaption Billions of Web Images with LLaMA-3?
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The paper introduces Recap-DataComp-1B, an enhanced dataset created using LLaMA-3-8B to improve vision-language model training, showing benefits in performance across various tasks. https://arxiv.org/abs//2406.08478 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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[QA] SAMBA: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
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SAMBA is a hybrid model combining Mamba and Sliding Window Attention for efficient sequence modeling with infinite context length, outperforming existing models. https://arxiv.org/abs//2406.07522 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-pap…
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SAMBA: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
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SAMBA is a hybrid model combining Mamba and Sliding Window Attention for efficient sequence modeling with infinite context length, outperforming existing models. https://arxiv.org/abs//2406.07522 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-pap…
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[QA] Why Warmup the Learning Rate? Underlying Mechanisms and Improvements
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The paper explores the benefits of warmup in deep learning, showing how it improves performance by allowing networks to handle larger learning rates and suggesting alternative initialization methods. https://arxiv.org/abs//2406.09405 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…
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Why Warmup the Learning Rate? Underlying Mechanisms and Improvements
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The paper explores the benefits of warmup in deep learning, showing how it improves performance by allowing networks to handle larger learning rates and suggesting alternative initialization methods. https://arxiv.org/abs//2406.09405 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…
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[QA] An Image is Worth More Than 1616 Patches: Exploring Transformers on Individual Pixels
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Vanilla Transformers can achieve high performance in computer vision by treating individual pixels as tokens, challenging the necessity of locality bias in modern architectures. https://arxiv.org/abs//2406.09415 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/p…
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An Image is Worth More Than 1616 Patches: Exploring Transformers on Individual Pixels
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Vanilla Transformers can achieve high performance in computer vision by treating individual pixels as tokens, challenging the necessity of locality bias in modern architectures. https://arxiv.org/abs//2406.09415 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/p…
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[QA] Large Language Models Must Be Taught to Know What They Don't Know
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Prompting alone is insufficient for reliable uncertainty estimation in large language models. Fine-tuning on a small dataset of correct and incorrect answers can provide better calibration with low computational cost. https://arxiv.org/abs//2406.08391 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple P…
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Large Language Models Must Be Taught to Know What They Don't Know
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Prompting alone is insufficient for reliable uncertainty estimation in large language models. Fine-tuning on a small dataset of correct and incorrect answers can provide better calibration with low computational cost. https://arxiv.org/abs//2406.08391 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple P…
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