In-Context Learning for Extreme Multi-Label Classification
Karel D'Oosterlinck Omar Khattab François Remy Thomas Demeester Chris Develder Christopher Potts
Multi-label classification problems with thousands of classes are hard to
solve with in-context learning alone, as language models (LMs) might lack prior
knowledge about the precise classes or how to assign them, and it is generally
infeasible to demonstrate every class in a prompt. We propose a general
program, $\texttt{Infer--Retrieve--Rank}$, that defines multi-step interactions
between LMs and retrievers to efficiently tackle such problems. We implement
this program using the $\texttt{DSPy}$ programming model, which specifies
in-context systems in a declarative manner, and use $\texttt{DSPy}$ optimizers
to tune it towards specific datasets by bootstrapping only tens of few-shot
examples. Our primary extreme classification program, optimized separately for
each task, attains state-of-the-art results across three benchmarks (HOUSE,
TECH, TECHWOLF). We apply the same program to a benchmark with vastly different
characteristics and attain competitive performance as well (BioDEX). Unlike
prior work, our proposed solution requires no finetuning, is easily applicable
to new tasks, alleviates prompt engineering, and requires only tens of labeled
examples. Our code is public at https://github.com/KarelDO/xmc.dspy.