Task-aware Retrieval with Instructions

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2 years ago 1:11:13

M4A•Laman utama episod

📄 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

21 episod

#Tech #Zeta Alpha

Task-aware Retrieval with Instructions

Neural Search Talks — Zeta Alpha

published 2 years ago

Kongsi

M4A•Laman utama episod

📄 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

21 episod

#Tech #Zeta Alpha

Semua episod

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35 weeks ago22:15

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36 weeks ago59:57

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1 year ago1:07:31

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…

1
Task-aware Retrieval with Instructions 1:11:13

2 years ago1:11:13

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…

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

2 years ago1:16:14

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…

3 years ago1:07:17

1:07:17

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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Task-aware Retrieval with Instructions