Table of Links
Abstract and 1. Introduction
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Background
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Method
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Experiments
4.1 Multi-hop Reasoning Performance
4.2 Reasoning with Distractors
4.3 Generalization to Real-World knowledge
4.4 Run-time Analysis
4.5 Memorizing Knowledge
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Related Work
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Conclusion, Acknowledgements, and References
A. Dataset
B. In-context Reasoning with Distractors
C. Implementation Details
D. Adaptive Learning Rate
E. Experiments with Large Language Models
6 Conclusion
We present RECKONING, a bi-level learning framework for multi-hop reasoning that encodes knowledge verbalized using natural language into a model’s parameters through gradient updates. During training, the inner loop encodes the contextual knowledge into the model parameters by backpropagating a language modeling loss. In the outer loop, given only the question as input, the model solves reasoning problems using the memorized knowledge. Through bi-level optimization, RECKONING finds a set of meta-parameters that allows it to perform quick knowledge-based updates for reasoning. Our experiments show that RECKONING learns to reason only by relying on its parametric knowledge after the external knowledge has been encoded. Using a multi-task objective that jointly optimizes reasoning and knowledge memorization in the outer loop, RECKONING outperforms ICR baselines that are trained to encode external knowledge as part of the context. Through our analysis, we show that RECKONING is more generalizable to problems with longer reasoning chains, less susceptible to irrelevant distractor knowledge, and that RECKONING is more efficient than the baseline when answering multiple questions that require common knowledge.
Acknowledgements
We thank Shikhar Murty and Christopher Manning for helpful discussions in crafting ideas for this project. We also gratefully acknowledge the support of the Swiss National Science Foundation (No. 215390), Innosuisse (PFFS-21-29), the EPFL Science Seed Fund, the EPFL Center for Imaging, Sony Group Corporation, and the Allen Institute for AI.
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Authors:
(1) Zeming Chen, EPFL ([email protected]);
(2) Gail Weiss, EPFL ([email protected]);
(3) Eric Mitchell, Stanford University ([email protected])’;
(4) Asli Celikyilmaz, Meta AI Research ([email protected]);
(5) Antoine Bosselut, EPFL ([email protected]).
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This paper is available on arxiv under CC BY 4.0 DEED license.
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