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] Law of the Weakest Link: Cross Capabilities of Large Language Models
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https://arxiv.org/abs//2409.19951 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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Law of the Weakest Link: Cross Capabilities of Large Language Models
16:18
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https://arxiv.org/abs//2409.19951 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] Realistic Evaluation of Model Merging for Compositional Generalization
8:29
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This paper evaluates various model merging methods for compositional generalization in image classification, generation, and NLP, clarifying their merits, requirements, and computational costs in a shared experimental setting. https://arxiv.org/abs//2409.18314 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_paper…
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Realistic Evaluation of Model Merging for Compositional Generalization
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This paper evaluates various model merging methods for compositional generalization in image classification, generation, and NLP, clarifying their merits, requirements, and computational costs in a shared experimental setting. https://arxiv.org/abs//2409.18314 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_paper…
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[QA] Emu3: Next-Token Prediction is All You Need
7:43
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Emu3 introduces a next-token prediction model for multimodal tasks, outperforming existing models and simplifying design by focusing on tokenization of images, text, and videos. https://arxiv.org/abs//2409.18869 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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Emu3: Next-Token Prediction is All You Need
17:28
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Emu3 introduces a next-token prediction model for multimodal tasks, outperforming existing models and simplifying design by focusing on tokenization of images, text, and videos. https://arxiv.org/abs//2409.18869 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] MIO: A Foundation Model on Multimodal Tokens
8:38
8:38
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MIO is a novel multimodal foundation model that excels in understanding and generating speech, text, images, and videos, outperforming existing models in any-to-any capabilities and diverse tasks. https://arxiv.org/abs//2409.17692 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podc…
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MIO: A Foundation Model on Multimodal Tokens
19:09
19:09
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MIO is a novel multimodal foundation model that excels in understanding and generating speech, text, images, and videos, outperforming existing models in any-to-any capabilities and diverse tasks. https://arxiv.org/abs//2409.17692 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podc…
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[QA] A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor ?
7:52
7:52
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The paper evaluates OpenAI's o1 model in medical scenarios, highlighting its enhanced reasoning and accuracy over GPT-4, while also identifying weaknesses and releasing data for further research. https://arxiv.org/abs//2409.15277 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…
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A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor ?
8:52
8:52
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The paper evaluates OpenAI's o1 model in medical scenarios, highlighting its enhanced reasoning and accuracy over GPT-4, while also identifying weaknesses and releasing data for further research. https://arxiv.org/abs//2409.15277 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…
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[QA] Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models
8:44
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The Logic-of-Thought (LoT) prompting method enhances logical reasoning in Large Language Models by integrating propositional logic, significantly improving performance across various reasoning tasks. https://arxiv.org/abs//2409.17539 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…
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Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models
16:05
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The Logic-of-Thought (LoT) prompting method enhances logical reasoning in Large Language Models by integrating propositional logic, significantly improving performance across various reasoning tasks. https://arxiv.org/abs//2409.17539 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…
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[QA] Making Text Embedders Few-Shot Learners
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We propose bge-en-icl, a model leveraging in-context learning in LLMs for high-quality text embeddings, achieving state-of-the-art performance on MTEB and AIR-Bench benchmarks. https://arxiv.org/abs//2409.15700 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/po…
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We propose bge-en-icl, a model leveraging in-context learning in LLMs for high-quality text embeddings, achieving state-of-the-art performance on MTEB and AIR-Bench benchmarks. https://arxiv.org/abs//2409.15700 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/po…
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[QA] Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
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The paper introduces PROX, a framework enabling small language models to refine data effectively, outperforming human-crafted methods and enhancing efficiency in LLM pre-training across various benchmarks. https://arxiv.org/abs//2409.17115 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
8:57
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The paper introduces PROX, a framework enabling small language models to refine data effectively, outperforming human-crafted methods and enhancing efficiency in LLM pre-training across various benchmarks. https://arxiv.org/abs//2409.17115 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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[QA] Infer Human's Intentions Before Following Natural Language Instruction
8:18
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The FISER framework enhances AI's ability to follow ambiguous human instructions by inferring intentions, outperforming traditional methods in collaborative tasks, particularly on the HandMeThat benchmark. https://arxiv.org/abs//2409.18073 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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Infer Human's Intentions Before Following Natural Language Instruction
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The FISER framework enhances AI's ability to follow ambiguous human instructions by inferring intentions, outperforming traditional methods in collaborative tasks, particularly on the HandMeThat benchmark. https://arxiv.org/abs//2409.18073 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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[QA] MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
7:05
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This paper presents a learnable pruning method for Large Language Models, achieving efficient N:M sparsity, improved mask quality, and transferability across tasks, outperforming existing techniques in empirical evaluations. https://arxiv.org/abs//2409.17481 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
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This paper presents a learnable pruning method for Large Language Models, achieving efficient N:M sparsity, improved mask quality, and transferability across tasks, outperforming existing techniques in empirical evaluations. https://arxiv.org/abs//2409.17481 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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[QA] Counterfactual Token Generation in Large Language Models
7:53
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This paper presents a method to enable large language models to perform counterfactual token generation, enhancing their capabilities without fine-tuning, and applying it for bias detection. https://arxiv.org/abs//2409.17027 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.a…
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Counterfactual Token Generation in Large Language Models
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This paper presents a method to enable large language models to perform counterfactual token generation, enhancing their capabilities without fine-tuning, and applying it for bias detection. https://arxiv.org/abs//2409.17027 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.a…
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[QA] Characterizing stable regions in the residual stream of LLMs
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The paper identifies stable regions in Transformers' residual streams, showing insensitivity to small changes but high sensitivity at boundaries, aligning with semantic distinctions and clustering similar prompts. https://arxiv.org/abs//2409.17113 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…
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Characterizing stable regions in the residual stream of LLMs
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The paper identifies stable regions in Transformers' residual streams, showing insensitivity to small changes but high sensitivity at boundaries, aligning with semantic distinctions and clustering similar prompts. https://arxiv.org/abs//2409.17113 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podca…
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[QA] Watch Your Steps: Observable and Modular Chains of Thought
7:30
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We introduce Program Trace Prompting, enhancing chain of thought explanations with formal syntax, improving observability, and enabling analysis of reasoning errors across diverse tasks in the BIG-Bench Hard benchmark. https://arxiv.org/abs//2409.15359 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple …
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Watch Your Steps: Observable and Modular Chains of Thought
29:35
29:35
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We introduce Program Trace Prompting, enhancing chain of thought explanations with formal syntax, improving observability, and enabling analysis of reasoning errors across diverse tasks in the BIG-Bench Hard benchmark. https://arxiv.org/abs//2409.15359 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple …
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[QA] Seeing Faces in Things: A Model and Dataset for Pareidolia
7:38
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This paper explores face pareidolia in computer vision, presenting a dataset of annotated images and analyzing the differences in face detection between humans and machines. https://arxiv.org/abs//2409.16143 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podca…
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Seeing Faces in Things: A Model and Dataset for Pareidolia
10:54
10:54
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This paper explores face pareidolia in computer vision, presenting a dataset of annotated images and analyzing the differences in face detection between humans and machines. https://arxiv.org/abs//2409.16143 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podca…
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[QA] Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts
8:20
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The paper investigates out-of-distribution behavior in autoregressive LLMs through rule extrapolation in formal languages, analyzing various architectures and proposing a normative theory inspired by algorithmic information theory. https://arxiv.org/abs//2409.13728 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_…
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Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts
29:04
29:04
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The paper investigates out-of-distribution behavior in autoregressive LLMs through rule extrapolation in formal languages, analyzing various architectures and proposing a normative theory inspired by algorithmic information theory. https://arxiv.org/abs//2409.13728 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_…
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[QA] Style over Substance: Failure Modes of LLM Judges in Alignment Benchmarking
7:40
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This study evaluates the effectiveness of LLM-judge preferences in improving alignment, finding no correlation with concrete metrics and highlighting biases in LLM judgments. https://arxiv.org/abs//2409.15268 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podc…
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Style over Substance: Failure Modes of LLM Judges in Alignment Benchmarking
11:39
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This study evaluates the effectiveness of LLM-judge preferences in improving alignment, finding no correlation with concrete metrics and highlighting biases in LLM judgments. https://arxiv.org/abs//2409.15268 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podc…
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[QA] LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
7:46
7:46
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This paper introduces LLM Surgery, a framework for efficiently modifying large language models to unlearn outdated information and integrate new knowledge without complete retraining, demonstrating significant performance improvements. https://arxiv.org/abs//2409.13054 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@ar…
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LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
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This paper introduces LLM Surgery, a framework for efficiently modifying large language models to unlearn outdated information and integrate new knowledge without complete retraining, demonstrating significant performance improvements. https://arxiv.org/abs//2409.13054 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@ar…
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[QA] Embedding Geometries of Contrastive Language-Image Pre-Training
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This paper explores alternative geometries and softmax logits for language-image pre-training, finding that Euclidean CLIP (EuCLIP) performs as well as or better than the original CLIP. https://arxiv.org/abs//2409.13079 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.…
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Embedding Geometries of Contrastive Language-Image Pre-Training
15:25
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This paper explores alternative geometries and softmax logits for language-image pre-training, finding that Euclidean CLIP (EuCLIP) performs as well as or better than the original CLIP. https://arxiv.org/abs//2409.13079 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.…
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The Kolmogorov–Arnold Transformer (KAT) enhances transformer performance by replacing MLP layers with Kolmogorov-Arnold Network layers, addressing key challenges and demonstrating superior results in various tasks. https://arxiv.org/abs//2409.10594 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podc…
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The Kolmogorov–Arnold Transformer (KAT) enhances transformer performance by replacing MLP layers with Kolmogorov-Arnold Network layers, addressing key challenges and demonstrating superior results in various tasks. https://arxiv.org/abs//2409.10594 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podc…
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Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
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This paper reveals a flaw in the inference pipeline of diffusion models for depth estimation, leading to a 2002#2 speed improvement and superior performance through end-to-end fine-tuning. https://arxiv.org/abs//2409.11355 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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[QA] Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
6:42
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This paper reveals a flaw in the inference pipeline of diffusion models for depth estimation, leading to a 2002#2 speed improvement and superior performance through end-to-end fine-tuning. https://arxiv.org/abs//2409.11355 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.app…
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[QA] Re-Introducing LayerNorm: Geometric Meaning, Irreversibility and a Comparative Study with RMSNorm
7:03
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This paper explores the geometric implications of LayerNorm in transformers, revealing its irreversibility and redundancy, and advocates for RMSNorm as a more efficient alternative with similar performance. https://arxiv.org/abs//2409.12951 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: ht…
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Re-Introducing LayerNorm: Geometric Meaning, Irreversibility and a Comparative Study with RMSNorm
12:28
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This paper explores the geometric implications of LayerNorm in transformers, revealing its irreversibility and redundancy, and advocates for RMSNorm as a more efficient alternative with similar performance. https://arxiv.org/abs//2409.12951 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: ht…
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[QA] Is Tokenization Needed for Masked Particle Modelling?
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This paper enhances masked particle modeling (MPM) for high-energy physics, improving performance through better implementation and a powerful decoder, outperforming previous methods in various jet physics tasks. https://arxiv.org/abs//2409.12589 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…
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Is Tokenization Needed for Masked Particle Modelling?
20:39
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This paper enhances masked particle modeling (MPM) for high-energy physics, improving performance through better implementation and a powerful decoder, outperforming previous methods in various jet physics tasks. https://arxiv.org/abs//2409.12589 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…
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[QA] Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
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https://arxiv.org/abs//2409.12180 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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Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
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https://arxiv.org/abs//2409.12180 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] To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
7:23
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Chain-of-thought prompting enhances reasoning in large language models, particularly for math and logic tasks, but shows limited benefits for other tasks, suggesting a need for new computational paradigms. https://arxiv.org/abs//2409.12183 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
26:23
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Chain-of-thought prompting enhances reasoning in large language models, particularly for math and logic tasks, but shows limited benefits for other tasks, suggesting a need for new computational paradigms. https://arxiv.org/abs//2409.12183 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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[QA] On the limits of agency in agent-based models
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AgentTorch is a framework that enhances agent-based modeling by using large language models to simulate millions of agents, demonstrating its utility in analyzing complex systems like the COVID-19 pandemic. https://arxiv.org/abs//2409.10568 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: ht…
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On the limits of agency in agent-based models
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AgentTorch is a framework that enhances agent-based modeling by using large language models to simulate millions of agents, demonstrating its utility in analyzing complex systems like the COVID-19 pandemic. https://arxiv.org/abs//2409.10568 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: ht…
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