The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes

Neural Search Talks — Zeta Alpha

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Richard (Kudo) Couto: The Hidden Horror Behind a Billion-Dollar Brand 42:18

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42:18

“I used to be the largest dairy consumer on the planet. I used to eat so much dairy and meat. The more that I looked into the dairy industry, the more that I saw that it was the singular, most inhumane industry on the planet, that we've all been lied to, including myself, for years. I always believed that the picture on the milk carton, the cow standing next to her calf in the green field with the red barn in the back was true. It’s certainly the complete opposite.” – Richard (Kudo) Couto Richard (Kudo) Couto is the founder of Animal Recovery Mission (ARM), an organization solely dedicated to investigating extreme animal cruelty cases. ARM has led high-risk undercover operations that have resulted in the shutdown of illegal slaughterhouses, animal fighting rings, and horse meat trafficking networks. Recently, they released a damning investigation into two industrial dairy farms outside of Phoenix, Arizona supplying milk to Coca-Cola’s Fairlife brand. What they uncovered was systemic animal abuse, environmental violations, and a devastating betrayal of consumer trust. While Fairlife markets its products as being sourced "humanely," ARM’s footage tells a very different story—one of suffering, abuse, and corporate complicity. Despite the evidence, this story has been largely ignored by mainstream media—likely due to Coca-Cola’s massive influence and advertising dollars.…

לפני 3 שנים 54:13

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Timestamps:

00:00 Co-host introduction

00:26 Paper introduction

02:18 Dense vs. Sparse retrieval

05:46 Theoretical analysis of false positives(1)

08:17 What is low vs. high dimensional representations

11:49 Theoretical analysis o false positives (2)

20:10 First results: growing the MS-Marco index

28:35 Adding random strings to the index

39:17 Discussion, takeaways

44:26 Will dense retrieval replace or coexist with sparse methods?

50:50 Sparse, Dense and Attentional Representations for Text Retrieval

Referenced work:

Sparse, Dense and Attentional Representations for Text Retrieval by Yi Luan et al. 2020.

21 פרקים

#Tech #Zeta Alpha

The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes

Neural Search Talks — Zeta Alpha

published לפני 3 שנים

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M4A•בית הפרקים

Timestamps:

00:00 Co-host introduction

00:26 Paper introduction

02:18 Dense vs. Sparse retrieval

05:46 Theoretical analysis of false positives(1)

08:17 What is low vs. high dimensional representations

11:49 Theoretical analysis o false positives (2)

20:10 First results: growing the MS-Marco index

28:35 Adding random strings to the index

39:17 Discussion, takeaways

44:26 Will dense retrieval replace or coexist with sparse methods?

50:50 Sparse, Dense and Attentional Representations for Text Retrieval

Referenced work:

Sparse, Dense and Attentional Representations for Text Retrieval by Yi Luan et al. 2020.

21 פרקים

#Tech #Zeta Alpha

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In this first episode of Neural Information Retrieval Talks, Andrew Yates and Sergi Castellla discuss the paper "Shallow Pooling for Sparse Labels" by Negar Arabzadeh, Alexandra Vtyurina, Xinyi Yan and Charles L. A. Clarke from the University of Waterloo, Canada. This paper puts the spotlight on the popular IR benchmark MS MARCO and investigates whether modern neural retrieval models retrieve documents that are even more relevant than the original top relevance annotations. The results have important implications and raise the question of to what degree this benchmark is still an informative north star to follow. Contact: castella@zeta-alpha.com Timestamps: 00:00 — Introduction. 01:52 — Overview and motivation of the paper. 04:00 — Origins of MS MARCO. 07:30 — Modern approaches to IR: keyword-based, dense retrieval, rerankers and learned sparse representations. 13:40 — What is "better than perfect" performance on MS MARCO? 17:15 — Results and discussion: how often are neural rankers preferred over original annotations on MS MARCO? How should we interpret these results? 26:55 — The authors' proposal to "fix" MS MARCO: shallow pooling 32:40 — How does TREC Deep Learning compare? 38:30 — How do models compare after re-annotating MS MARCO passages? 45:00 — Figure 5 audio description. 47:00 — Discussion on models' performance after re-annotations. 51:50 — Exciting directions in the space of IR benchmarking. 1:06:20 — Outro. Related material: - Leo Boystov paper critique blog post: http://searchivarius.org/blog/ir-leaderboards-never-tell-full-story-they-are-still-useful-and-what-can-be-done-make-them-even - "MS MARCO Chameleons: Challenging the MS MARCO Leaderboard with Extremely Obstinate Queries" https://dl.acm.org/doi/abs/10.1145/3459637.3482011…

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The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes