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Task-aware Retrieval with Instructions

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Manage episode 355037182 series 3446693
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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

  continue reading

21 episoder

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Manage episode 355037182 series 3446693
Indhold leveret af Zeta Alpha. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Zeta Alpha eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

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

  continue reading

21 episoder

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