The Strength of Dynamic Encoding: RECKONING Outperforms Zero-Shot GPT-3.5 in Distractor Robustness

Table of Links

Abstract and 1. Introduction

1. Background

2. Method

3. 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

4. Related Work

5. 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

E Experiments with Large Language Models

\ Recently, Large Language Models (LLMs) with large parameter sizes learned from human preferences have shown remarkable performance in language understanding and generation. These LLMs are powerful zero-shot and few-shot reasoners. Recent works find that LLMs learn to perform multi-step reasoning by first generating new reasoning chains and then predicting the answers. In this experiment, we benchmark the performance of a popular new LLM, GPT-3.5, on the two multi-hop reasoning datasets we used in our paper. We first evaluate GPT-3.5’s zero-shot reasoning performance in predicting the correct answers. As Table 10 shows, zero-shot prompting GPT-3.5 significantly underperforms RECKONING’s performance. GPT-3.5’s performance improves on ProofWriter without distractors but still is behind the performance of RECKONING. When distractors are present in the context, RECKONING performs much better than zero-shot and few-shot GPT-3.5 prompting. This highlights RECKONING’s strength in disentangling irrelevant information from useful knowledge, an ability that even powerful LLMs like GPT-3.5 lack.

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:::info Authors:

(1) Zeming Chen, EPFL ([e-mail]);

(2) Gail Weiss, EPFL ([e-mail]);

(3) Eric Mitchell, Stanford University ([e-mail])';

(4) Asli Celikyilmaz, Meta AI Research ([e-mail]);

(5) Antoine Bosselut, EPFL ([e-mail]).

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:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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