Summary of In-context Learning For Extreme Multi-label Classification, by Karel D’oosterlinck et al.
In-Context Learning for Extreme Multi-Label Classification
by Karel D’Oosterlinck, Omar Khattab, François Remy, Thomas Demeester, Chris Develder, Christopher Potts
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach, called Infer–Retrieve–Rank, to tackle multi-label classification problems with thousands of classes. The method leverages the strengths of language models and retrievers in a multi-step interaction, allowing for efficient solution of such problems without requiring prior knowledge about specific classes or finetuning. The authors implement their program using the DSPy programming model and optimize it for specific datasets using only tens of few-shot examples. Experimental results show state-of-the-art performance across three benchmarks: HOUSE, TECH, and TECHWOLF, with competitive performance on a benchmark with vastly different characteristics (BioDEX). The proposed solution requires no finetuning, is easily applicable to new tasks, alleviates prompt engineering, and only needs tens of labeled examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve very difficult problems where we have many categories to classify things into. It creates a special way to work with language models and retrievers to make this easier. The authors show that their method works well on several different datasets and is better than other methods in some cases. This means it could be used for new tasks without needing to adjust it first, which makes it more useful. |
Keywords
» Artificial intelligence » Classification » Few shot » Prompt
