Faith and Fate: Limits of Transformers on Compositionality
- URL: http://arxiv.org/abs/2305.18654v3
- Date: Tue, 31 Oct 2023 16:35:07 GMT
- Title: Faith and Fate: Limits of Transformers on Compositionality
- Authors: Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang,
Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D.
Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid
Harchaoui, Yejin Choi
- Abstract summary: We investigate the limits of transformer large language models across three representative compositional tasks.
These tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer.
Our empirical findings suggest that transformer LLMs solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching.
- Score: 109.79516190693415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Transformer large language models (LLMs) have sparked admiration for their
exceptional performance on tasks that demand intricate multi-step reasoning.
Yet, these models simultaneously show failures on surprisingly trivial
problems. This begs the question: Are these errors incidental, or do they
signal more substantial limitations? In an attempt to demystify transformer
LLMs, we investigate the limits of these models across three representative
compositional tasks -- multi-digit multiplication, logic grid puzzles, and a
classic dynamic programming problem. These tasks require breaking problems down
into sub-steps and synthesizing these steps into a precise answer. We formulate
compositional tasks as computation graphs to systematically quantify the level
of complexity, and break down reasoning steps into intermediate sub-procedures.
Our empirical findings suggest that transformer LLMs solve compositional tasks
by reducing multi-step compositional reasoning into linearized subgraph
matching, without necessarily developing systematic problem-solving skills. To
round off our empirical study, we provide theoretical arguments on abstract
multi-step reasoning problems that highlight how autoregressive generations'
performance can rapidly decay with\,increased\,task\,complexity.
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