ColBERT + ColBERTv2: late interaction at a reasonable inference cost

Neural Search Talks — Zeta Alpha

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#11: From OJ Simpson Case to Best Selling Author - Marcia Clark Shares Latest Real Crime Book Release and How Resilience Is Key to Success and Reinvention 34:35

לפני 21 ימים34:35

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34:35

Marcia Clark, best known as the lead prosecutor in the O.J. Simpson trial, has become a trailblazer for women in law and beyond. Her journey from courtroom to bestselling author reflects her resilience and determination to redefine herself amidst intense public scrutiny. On this episode of Worth Knowing, Clark dives into her latest book, *Trial by Ambush*, which examines the 1953 Barbara Graham case—a story that highlights gender bias, media sensationalism, and the notion that all cases are subject to societal, cultural, and political winds. Clark shares how her experiences during the Simpson trial shaped her perspective on societal pressures and the role of women in high-stakes professions. Her reflections on how media, forensic science, and legal practices have evolved over decades offer valuable insights into the intersection of law and culture. This conversation is a compelling exploration of true crime, personal growth, and how Clark’s groundbreaking career continues to inspire a new generation of women to challenge norms and forge their own paths. Marcia Clark is a bestselling author and a criminal lawyer who began her career in law as a criminal defense attorney and went on to become a prosecutor in the L.A. District Attorney's Office in 1981. She spent ten years in the Special Trials Unit, where she handled a number of high-profile cases, including the prosecution of stalker/murderer Robert Bardo, whose conviction for the murder of actress Rebecca Schaeffer resulted in legislation that offered victims better protection from stalkers as well as increased punishment for the offenders. She was lead prosecutor for the O.J. Simpson murder trial. In May of 1997 her book on the Simpson case, "Without a Doubt," was published and reached #1 on the New York Times, Wall St. Journal, Washington Post, Los Angeles Times, and Publishers Weekly bestsellers lists. In February 2016, Clark re-released the book with a new foreword. Resources Sign up for the Worth Knowing LinkedIn Newsletter to stay up to date: https://www.linkedin.com/newsletters/worth-knowing-7236433935503618048/ Follow Bonnie on LinkedIn: https://www.linkedin.com/in/bonnie-habyan/ Go to the Worth Knowing website: https://www.worthknowing.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company…

לפני 3 שנים 57:30

M4A•בית הפרקים

📄 ColBERT: "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT" by Omar Khattab and Matei Zaharia. https://arxiv.org/abs/2004.12832

📄 ColBERTv2: "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction" by Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. https://arxiv.org/abs/2112.01488

📄 PLAID: "An Efficient Engine for Late Interaction Retrieval" by Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. https://arxiv.org/abs/2205.09707

📄 CEDR: "CEDR: Contextualized Embeddings for Document Ranking" by Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. https://arxiv.org/abs/1904.07094

🪃 Feedback form: https://scastella.typeform.com/to/rg7a5GfJ

Timestamps:

00:00 Introduction

00:42 Why ColBERT?

03:34 Retrieval paradigms recap

08:04 ColBERT query formulation and architecture

09:04 Using ColBERT as a reranker or as an end-to-end retriever

11:28 Space Footprint vs. MRR on MS MARCO

12:24 Methodology: datasets and negative sampling

14:37 Terminology for cross encoders, interaction-based models, etc.

16:12 Results (ColBERT v1) on MS MARCO

18:41 Ablations on model components

20:34 Max pooling vs. mean pooling

22:54 Why did ColBERT have a big impact?

26:31 ColBERTv2: knowledge distillation

29:34 ColBERTv2: indexing improvements

33:59 Effects of clustering compression in performance

35:19 Results (ColBERT v2): MS MARCO

38:54 Results (ColBERT v2): BEIR

41:27 Takeaway: strong specially in out-of-domain evaluation

43:59 Qualitatively how do ColBERT scores look like?

46:21 What's the most promising of all current neural IR paradigms

49:34 How come there's still so much interest in Dense retrieval?

51:09 Many to many similarity at different granularities

53:44 What would ColBERT v3 include?

56:39 PLAID: An Efficient Engine for Late Interaction Retrieval

Contact: castella@zeta-alpha.com

21 פרקים

#Tech #Zeta Alpha

ColBERT + ColBERTv2: late interaction at a reasonable inference cost

Neural Search Talks — Zeta Alpha

published לפני 3 שנים

שתפו

M4A•בית הפרקים

📄 ColBERT: "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT" by Omar Khattab and Matei Zaharia. https://arxiv.org/abs/2004.12832

📄 PLAID: "An Efficient Engine for Late Interaction Retrieval" by Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. https://arxiv.org/abs/2205.09707

📄 CEDR: "CEDR: Contextualized Embeddings for Document Ranking" by Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. https://arxiv.org/abs/1904.07094

🪃 Feedback form: https://scastella.typeform.com/to/rg7a5GfJ

Timestamps:

00:00 Introduction

00:42 Why ColBERT?

03:34 Retrieval paradigms recap

08:04 ColBERT query formulation and architecture

09:04 Using ColBERT as a reranker or as an end-to-end retriever

11:28 Space Footprint vs. MRR on MS MARCO

12:24 Methodology: datasets and negative sampling

14:37 Terminology for cross encoders, interaction-based models, etc.

16:12 Results (ColBERT v1) on MS MARCO

18:41 Ablations on model components

20:34 Max pooling vs. mean pooling

22:54 Why did ColBERT have a big impact?

26:31 ColBERTv2: knowledge distillation

29:34 ColBERTv2: indexing improvements

33:59 Effects of clustering compression in performance

35:19 Results (ColBERT v2): MS MARCO

38:54 Results (ColBERT v2): BEIR

41:27 Takeaway: strong specially in out-of-domain evaluation

43:59 Qualitatively how do ColBERT scores look like?

46:21 What's the most promising of all current neural IR paradigms

49:34 How come there's still so much interest in Dense retrieval?

51:09 Many to many similarity at different granularities

53:44 What would ColBERT v3 include?

56:39 PLAID: An Efficient Engine for Late Interaction Retrieval

Contact: castella@zeta-alpha.com

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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ColBERT + ColBERTv2: late interaction at a reasonable inference cost

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ColBERT + ColBERTv2: late interaction at a reasonable inference cost