A podcast empowering women to cure loneliness and make new friends.
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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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A weekly podcast discussing a host of topics over a glass of fizz or two!
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1
[QA] Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2
7:45
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https://arxiv.org/abs//2408.05147 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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1
[QA] Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
7:55
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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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1
Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
29:15
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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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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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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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1
[QA] Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models
7:26
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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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1
Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models
16:13
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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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1
[QA] Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
8:28
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The Learned Ranking Function (LRF) optimizes recommendations for long-term user satisfaction using short-term behavior predictions, employing a novel constraint optimization algorithm, and is tested on YouTube. https://arxiv.org/abs//2408.06512 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts…
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1
Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
15:10
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The Learned Ranking Function (LRF) optimizes recommendations for long-term user satisfaction using short-term behavior predictions, employing a novel constraint optimization algorithm, and is tested on YouTube. https://arxiv.org/abs//2408.06512 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts…
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1
[QA] The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
8:34
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The paper introduces THE AI SCIENTIST, a framework enabling LLMs to autonomously conduct scientific research, generate ideas, write papers, and evaluate findings, significantly advancing scientific discovery and democratizing research. https://arxiv.org/abs//2408.06292 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@ar…
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1
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
30:36
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The paper introduces THE AI SCIENTIST, a framework enabling LLMs to autonomously conduct scientific research, generate ideas, write papers, and evaluate findings, significantly advancing scientific discovery and democratizing research. https://arxiv.org/abs//2408.06292 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@ar…
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1
[QA] Body Transformer: Leveraging Robot Embodiment for Policy Learning
7:59
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The Body Transformer (BoT) enhances robot learning by leveraging robot embodiment, outperforming vanilla transformers and multilayer perceptrons in task completion and efficiency. Open-source code is available. https://arxiv.org/abs//2408.06316 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts…
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1
Body Transformer: Leveraging Robot Embodiment for Policy Learning
15:21
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The Body Transformer (BoT) enhances robot learning by leveraging robot embodiment, outperforming vanilla transformers and multilayer perceptrons in task completion and efficiency. Open-source code is available. https://arxiv.org/abs//2408.06316 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts…
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1
[QA] VITA: Towards Open-Source Interactive Omni Multimodal LLM
8:16
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VITA is the first open-source Multimodal Large Language Model, integrating video, image, text, and audio processing, enhancing human-computer interaction with innovative features like non-awakening and audio interrupt interactions. https://arxiv.org/abs//2408.05211 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_…
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1
VITA: Towards Open-Source Interactive Omni Multimodal LLM
13:18
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VITA is the first open-source Multimodal Large Language Model, integrating video, image, text, and audio processing, enhancing human-computer interaction with innovative features like non-awakening and audio interrupt interactions. https://arxiv.org/abs//2408.05211 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_…
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1
Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2
22:04
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https://arxiv.org/abs//2408.05147 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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This paper presents a guided diffusion model that enhances auto-regressive language models, enabling controlled text generation with improved fluency and flexibility, outperforming existing guidance methods. https://arxiv.org/abs//2408.04220 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: h…
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Conversational Prompt Engineering (CPE) simplifies prompt creation for LLMs, enabling personalized, efficient outputs through user interaction, ultimately saving time and enhancing performance in summarization tasks. https://arxiv.org/abs//2408.04560 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…
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Conversational Prompt Engineering (CPE) simplifies prompt creation for LLMs, enabling personalized, efficient outputs through user interaction, ultimately saving time and enhancing performance in summarization tasks. https://arxiv.org/abs//2408.04560 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…
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1
[QA] Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
8:49
8:49
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This study presents Img-Diff, a novel dataset for fine-grained image recognition in MLLMs, enhancing performance through contrastive learning and image difference captioning, outperforming existing models. https://arxiv.org/abs//2408.04594 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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1
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
23:13
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This study presents Img-Diff, a novel dataset for fine-grained image recognition in MLLMs, enhancing performance through contrastive learning and image difference captioning, outperforming existing models. https://arxiv.org/abs//2408.04594 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: htt…
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1
[QA] Better Alignment with Instruction Back-and-Forth Translation
7:13
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The paper introduces instruction back-and-forth translation for generating high-quality synthetic data, enhancing large language model alignment through improved instruction and response quality compared to existing datasets. https://arxiv.org/abs//2408.04614 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers…
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1
Better Alignment with Instruction Back-and-Forth Translation
23:23
23:23
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The paper introduces instruction back-and-forth translation for generating high-quality synthetic data, enhancing large language model alignment through improved instruction and response quality compared to existing datasets. https://arxiv.org/abs//2408.04614 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers…
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This paper addresses The Ungrounded Alignment Problem, proposing a method for unsupervised learners to associate images with class labels using letter bigram frequencies, enabling innate behavior in modality-agnostic models. https://arxiv.org/abs//2408.04242 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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This paper addresses The Ungrounded Alignment Problem, proposing a method for unsupervised learners to associate images with class labels using letter bigram frequencies, enabling innate behavior in modality-agnostic models. https://arxiv.org/abs//2408.04242 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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1
[QA] Prioritize Alignment in Dataset Distillation
8:19
8:19
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The paper introduces Prioritize Alignment in Dataset Distillation (PAD), enhancing dataset compression by aligning information extraction and embedding, leading to significant performance improvements in distillation algorithms. https://arxiv.org/abs//2408.03360 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…
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1
Prioritize Alignment in Dataset Distillation
20:57
20:57
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The paper introduces Prioritize Alignment in Dataset Distillation (PAD), enhancing dataset compression by aligning information extraction and embedding, leading to significant performance improvements in distillation algorithms. https://arxiv.org/abs//2408.03360 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_pap…
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1
[QA] Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
7:57
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The paper presents Optimus-1, a multimodal agent utilizing a Hybrid Multimodal Memory module to enhance long-horizon task performance in Minecraft, outperforming existing agents and achieving near human-level results. https://arxiv.org/abs//2408.03615 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple P…
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1
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
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The paper presents Optimus-1, a multimodal agent utilizing a Hybrid Multimodal Memory module to enhance long-horizon task performance in Minecraft, outperforming existing agents and achieving near human-level results. https://arxiv.org/abs//2408.03615 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple P…
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1
[QA] Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
7:17
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https://arxiv.org/abs//2408.03314 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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1
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
29:43
29:43
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https://arxiv.org/abs//2408.03314 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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1
[QA] Language Model Can Listen While Speaking
7:07
7:07
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The paper presents the listening-while-speaking language model (LSLM), enhancing real-time human-computer interaction through full duplex modeling, enabling effective interruptions and improved conversational AI performance. https://arxiv.org/abs//2408.02622 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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The paper presents the listening-while-speaking language model (LSLM), enhancing real-time human-computer interaction through full duplex modeling, enabling effective interruptions and improved conversational AI performance. https://arxiv.org/abs//2408.02622 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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This work introduces a self-improvement method for LLM evaluators using synthetic data, enhancing performance significantly without human annotations, surpassing GPT-4 and matching top reward models. https://arxiv.org/abs//2408.02666 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…
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This work introduces a self-improvement method for LLM evaluators using synthetic data, enhancing performance significantly without human annotations, surpassing GPT-4 and matching top reward models. https://arxiv.org/abs//2408.02666 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…
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The paper introduces COND P-DIFF, a method for generating high-performance neural network parameters using controllable latent diffusion, enhancing task-specific adaptation in computer vision and natural language processing. https://arxiv.org/abs//2408.01415 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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The paper introduces COND P-DIFF, a method for generating high-performance neural network parameters using controllable latent diffusion, enhancing task-specific adaptation in computer vision and natural language processing. https://arxiv.org/abs//2408.01415 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers …
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1
[QA] Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
7:33
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This paper analyzes preference alignment and jailbreaking in large language models, proposing E-RLHF as a cost-effective method to enhance safety without compromising performance. https://arxiv.org/abs//2408.01420 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us…
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1
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
10:56
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This paper analyzes preference alignment and jailbreaking in large language models, proposing E-RLHF as a cost-effective method to enhance safety without compromising performance. https://arxiv.org/abs//2408.01420 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us…
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1
[QA] Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
7:19
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7:19
The paper introduces a Meta-Rewarding mechanism for LLMs, enhancing their self-judgment capabilities, leading to significant performance improvements without relying on human data. https://arxiv.org/abs//2407.19594 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/u…
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1
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
22:41
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The paper introduces a Meta-Rewarding mechanism for LLMs, enhancing their self-judgment capabilities, leading to significant performance improvements without relying on human data. https://arxiv.org/abs//2407.19594 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/u…
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1
[QA] MindSearch : Mimicking Human Minds Elicits Deep AI Searcher
7:05
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MindSearch mimics human cognitive processes for information seeking and integration, using a multi-agent framework to enhance search engine performance and improve response quality significantly. https://arxiv.org/abs//2407.20183 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…
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1
MindSearch : Mimicking Human Minds Elicits Deep AI Searcher
14:16
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MindSearch mimics human cognitive processes for information seeking and integration, using a multi-agent framework to enhance search engine performance and improve response quality significantly. https://arxiv.org/abs//2407.20183 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podca…
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1
[QA] Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
7:50
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7:50
The paper analyzes AI safety benchmarks, revealing their correlation with general capabilities, and proposes a clearer framework for defining and measuring AI safety research goals. https://arxiv.org/abs//2407.21792 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
15:20
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The paper analyzes AI safety benchmarks, revealing their correlation with general capabilities, and proposes a clearer framework for defining and measuring AI safety research goals. https://arxiv.org/abs//2407.21792 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/…
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[QA] Gemma 2: Improving Open Language Models at a Practical Size
7:30
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https://arxiv.org/abs//2408.00118 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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1
Gemma 2: Improving Open Language Models at a Practical Size
21:55
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https://arxiv.org/abs//2408.00118 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] AI-Assisted Generation of Difficult Math Questions
8:36
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The paper presents a framework combining LLMs and human input to generate diverse, challenging math questions, enhancing quality through iterative refinement and skill-based question generation. https://arxiv.org/abs//2407.21009 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…
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AI-Assisted Generation of Difficult Math Questions
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The paper presents a framework combining LLMs and human input to generate diverse, challenging math questions, enhancing quality through iterative refinement and skill-based question generation. https://arxiv.org/abs//2407.21009 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcas…
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