ColPali: Document Retrieval with Vision-Language Models only (with Manuel Faysse)

Neural Search Talks — Zeta Alpha

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<div class="span index">1</div> <span><a class="" data-remote="true" data-type="html" href="/series/lost-cultures-living-legacies">Lost Cultures: Living Legacies</a></span>

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Lost Cultures: Living Legacies

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לפני 3 ימיםלפני 3d ago

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What happens to a place when the cultures within it change over time? Lost Cultures: Living Legacies, a new podcast from Travel + Leisure, spotlights destinations that have experienced significant cultural shifts throughout history. We reveal how they build upon and complement one another, while preserving their traditions. Through conversations with archeologists, academics, artists, and local members of the communities, we recount the evolution of these enduring cultures from their beginnings to modern day. Upcoming episodes feature the Ainu in Japan, the Maya in Mexico, the Tongva in California, the Nubians in Egypt, and many more. New episodes every Wednesday starting April 19.

לפני שנה 34:48

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In this episode of Neural Search Talks, we're chatting with Manuel Faysse, a 2nd year PhD student from CentraleSupélec & Illuin Technology, who is the first author of the paper "ColPali: Efficient Document Retrieval with Vision Language Models". ColPali is making waves in the IR community as a simple but effective new take on embedding documents using their image patches and the late-interaction paradigm popularized by ColBERT. Tune in to learn how Manu conceptualized ColPali, his methodology for tackling new research ideas, and why this new approach outperforms all classic multimodal embedding models. A must-watch episode! Timestamps: 0:00 Introduction with Jakub & Manu 4:09 The "Aha!" moment that led to ColPali 7:06 Challenges that had to be solved 9:16 The main idea behind ColPali 13:20 How ColPali simplifies the IR pipeline 15:54 The ViDoRe benchmark 18:23 Why ColPali is superior to CLIP-based retrievers 20:41 The training setup used for ColPali 24:00 Optimizations to make ColPali more efficient 29:00 How ColPali could work with text-only datasets 31:21 Outro: The next steps for this line of research

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Neural Search Talks — Zeta Alpha

ColPali: Document Retrieval with Vision-Language Models only (with Manuel Faysse)

Neural Search Talks — Zeta Alpha

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Neural Search Talks — Zeta Alpha

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54:42

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Neural Search Talks — Zeta Alpha

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ColPali: Document Retrieval with Vision-Language Models only (with Manuel Faysse) 34:48

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לפני 40 weeks22:15

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לפני 42 weeks11:45

11:45

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Neural Search Talks — Zeta Alpha

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לפני 43 weeks21:55

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Neural Search Talks — Zeta Alpha

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לפני 1 year27:05

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לפני 1 year38:04

38:04

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Neural Search Talks — Zeta Alpha

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The Promise of Language Models for Search: Generative Information Retrieval 1:07:31

לפני 1 year1:07:31

1:07:31

In this episode of Neural Search Talks, Andrew Yates (Assistant Prof at the University of Amsterdam) Sergi Castella (Analyst at Zeta Alpha), and Gabriel Bénédict (PhD student at the University of Amsterdam) discuss the prospect of using GPT-like models as a replacement for conventional search engines. Generative Information Retrieval (Gen IR) SIGIR Workshop Workshop organized by Gabriel Bénédict, Ruqing Zhang, and Donald Metzler https://coda.io/@sigir/gen-ir Resources on Gen IR: https://github.com/gabriben/awesome-generative-information-retrieval References Rethinking Search: https://arxiv.org/abs/2105.02274 Survey on Augmented Language Models: https://arxiv.org/abs/2302.07842 Differentiable Search Index: https://arxiv.org/abs/2202.06991 Recommender Systems with Generative Retrieval: https://shashankrajput.github.io/Generative.pdf Timestamps: 00:00 Introduction, ChatGPT Plugins 02:01 ChatGPT plugins, LangChain 04:37 What is even Information Retrieval? 06:14 Index-centric vs. model-centric Retrieval 12:22 Generative Information Retrieval (Gen IR) 21:34 Gen IR emerging applications 24:19 How Retrieval Augmented LMs incorporate external knowledge 29:19 What is hallucination? 35:04 Factuality and Faithfulness 41:04 Evaluating generation of Language Models 47:44 Do we even need to "measure" performance? 54:07 How would you evaluate Bing's Sydney? 57:22 Will language models take over commercial search? 1:01:44 NLP academic research in the times of GPT-4 1:06:59 Outro…

Neural Search Talks — Zeta Alpha

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Task-aware Retrieval with Instructions 1:11:13

לפני 2 years1:11:13

1:11:13

Andrew Yates (Assistant Prof at University of Amsterdam) and Sergi Castella (Analyst at Zeta Alpha) discuss the paper "Task-aware Retrieval with Instructions" by Akari Asai et al. This paper proposes to augment a conglomerate of existing retrieval and NLP datasets with natural language instructions (BERRI, Bank of Explicit RetRieval Instructions) and use it to train TART (Multi-task Instructed Retriever). 📄 Paper: https://arxiv.org/abs/2211.09260 🍻 BEIR benchmark: https://arxiv.org/abs/2104.08663 📈 LOTTE (Long-Tail Topic-stratified Evaluation, introduced in ColBERT v2): https://arxiv.org/abs/2112.01488 Timestamps: 00:00 Intro: "Task-aware Retrieval with Instructions" 02:20 BERRI, TART, X^2 evaluation 04:00 Background: recent works in domain adaptation 06:50 Instruction Tuning 08:50 Retrieval with descriptions 11:30 Retrieval with instructions 17:28 BERRI, Bank of Explicit RetRieval Instructions 21:48 Repurposing NLP tasks as retrieval tasks 23:53 Negative document selection 27:47 TART, Multi-task Instructed Retriever 31:50 Evaluation: Zero-shot and X^2 evaluation 39:20 Results on Table 3 (BEIR, LOTTE) 50:30 Results on Table 4 (X^2-Retrieval) 55:50 Ablations 57:17 Discussion: user modeling, future work, scale…

Neural Search Talks — Zeta Alpha

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Generating Training Data with Large Language Models w/ Special Guest Marzieh Fadaee 1:16:14

לפני 2 years1:16:14

1:16:14

Marzieh Fadaee — NLP Research Lead at Zeta Alpha — joins Andrew Yates and Sergi Castella to chat about her work in using large Language Models like GPT-3 to generate domain-specific training data for retrieval models with little-to-no human input. The two papers discussed are "InPars: Data Augmentation for Information Retrieval using Large Language Models" and "Promptagator: Few-shot Dense Retrieval From 8 Examples". InPars: https://arxiv.org/abs/2202.05144 Promptagator: https://arxiv.org/abs/2209.11755 Timestamps: 00:00 Introduction 02:00 Background and journey of Marzieh Fadaee 03:10 Challenges of leveraging Large LMs in Information Retrieval 05:20 InPars, motivation and method 14:30 Vanilla vs GBQ prompting 24:40 Evaluation and Benchmark 26:30 Baselines 27:40 Main results and takeaways (Table 1, InPars) 35:40 Ablations: prompting, in-domain vs. MSMARCO input documents 40:40 Promptagator overview and main differences with InPars 48:40 Retriever training and filtering in Promptagator 54:37 Main Results (Table 2, Promptagator) 1:02:30 Ablations on consistency filtering (Figure 2, Promptagator) 1:07:39 Is this the magic black-box pipeline for neural retrieval on any documents 1:11:14 Limitations of using LMs for synthetic data 1:13:00 Future directions for this line of research…

Neural Search Talks — Zeta Alpha

1
ColBERT + ColBERTv2: late interaction at a reasonable inference cost 57:30

לפני 3 years57:30

57:30

Andrew Yates (Assistant Professor at the University of Amsterdam) and Sergi Castella (Analyst at Zeta Alpha) discus the two influential papers introducing ColBERT (from 2020) and ColBERT v2 (from 2022), which mainly propose a fast late interaction operation to achieve a performance close to full cross-encoders but at a more manageable computational cost at inference; along with many other optimizations. 📄 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…

Neural Search Talks — Zeta Alpha

1
Evaluating Extrapolation Performance of Dense Retrieval: How does DR compare to cross encoders when it comes to generalization? 58:30

לפני 3 years58:30

58:30

How much of the training and test sets in TREC or MS Marco overlap? Can we evaluate on different splits of the data to isolate the extrapolation performance? In this episode of Neural Information Retrieval Talks, Andrew Yates and Sergi Castella i Sapé discuss the paper "Evaluating Extrapolation Performance of Dense Retrieval" byJingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 📄 Paper: https://arxiv.org/abs/2204.11447 ❓ About MS Marco: https://microsoft.github.io/msmarco/ ❓About TREC: https://trec.nist.gov/ 🪃 Feedback form: https://scastella.typeform.com/to/rg7a5GfJ Timestamps: 00:00 Introduction 01:08 Evaluation in Information Retrieval, why is it exciting 07:40 Extrapolation Performance in Dense Retrieval 10:30 Learning in High Dimension Always Amounts to Extrapolation 11:40 3 Research questions 16:18 Defining Train-Test label overlap: entity and query intent overlap 21:00 Train-test Overlap in existing benchmarks TREC 23:29 Resampling evaluation methods: constructing distinct train-test sets 25:37 Baselines and results: ColBERT, SPLADE 29:36 Table 6: interpolation vs. extrapolation performance in TREC 33:06 Table 7: interplation vs. extrapolation in MS Marco 35:55 Table 8: Comparing different DR training approaches 40:00 Research Question 1 resolved: cross encoders are more robust than dense retrieval in extrapolation 42:00 Extrapolation and Domain Transfer: BEIR benchmark. 44:46 Figure 2: correlation between extrapolation performance and domain transfer performance 48:35 Broad strokes takeaways from this work 52:30 Is there any intuition behind the results where Dense Retrieval generalizes worse than Cross Encoders? 56:14 Will this have an impact on the IR benchmarking culture? 57:40 Outro Contact: castella@zeta-alpha.com…

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