Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background


Method


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


Related Work


Conclusion, Acknowledgements, and References Background Background Background Method Method Method 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 Experiments Experiments 4.1 Multi-hop Reasoning Performance 4.1 Multi-hop Reasoning Performance 4.2 Reasoning with Distractors 4.2 Reasoning with Distractors 4.3 Generalization to Real-World knowledge 4.3 Generalization to Real-World knowledge 4.4 Run-time Analysis 4.4 Run-time Analysis 4.5 Memorizing Knowledge 4.5 Memorizing Knowledge Related Work Related Work Related Work Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References A. Dataset Dataset B. In-context Reasoning with Distractors B. In-context Reasoning with Distractors C. Implementation Details C. Implementation Details D. Adaptive Learning Rate D. Adaptive Learning Rate E. Experiments with Large Language Models E. Experiments with Large Language Models 4.3 Generalization to Real-World knowledge To investigate how generalizable our method is to real-world knowledge beyond the synthetic setting, we evaluate RECKONING on a more real-world multi-hop logical reasoning task, FOLIO [29], and report the result in Table 2. The dataset has a rich vocabulary, diverse logic patterns, and abundant language variations. It has been shown to challenge LLMs in both supervised fine-tuning and in-context learning settings. We fine-tune the GPT-2 model following the in-context reasoning setting as the baseline. As before, we train the GPT-2 model and RECKONING using the multi-task objective. We also compare to more advanced baselines, including GPT-3.5 (text-davinci-003 [55]) and ChatGPT(gpt-3.5-turbo[2]), two popular large language models with around 175B parameters. For these two large models, we evaluate both in the zero-shot and few-shot settings. In the few-shot setting, we prompt the model with 8 single-task examples randomly sampled from the training set to perform in-context learning. We find that RECKONING’s performance (which is initiated here from GPT-2) is better than the GPT-2 in-context reasoning baseline. Compared to the two advanced large language models, RECKONING outperforms them by a significant margin (12% 0-shot and 7% 8-shot). We conclude that RECKONING is effective and significantly benefits reasoning tasks using real-world knowledge. Authors:
(1) Zeming Chen, EPFL (zeming.chen@epfl.ch);
(2) Gail Weiss, EPFL (antoine.bosselut@epfl.ch);
(3) Eric Mitchell, Stanford University (eric.mitchell@cs.stanford.edu)';
(4) Asli Celikyilmaz, Meta AI Research (aslic@meta.com);
(5) Antoine Bosselut, EPFL (antoine.bosselut@epfl.ch). Authors: Authors: (1) Zeming Chen, EPFL (zeming.chen@epfl.ch); (2) Gail Weiss, EPFL (antoine.bosselut@epfl.ch); (3) Eric Mitchell, Stanford University (eric.mitchell@cs.stanford.edu)'; (4) Asli Celikyilmaz, Meta AI Research (aslic@meta.com); (5) Antoine Bosselut, EPFL (antoine.bosselut@epfl.ch). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv 2 https://openai.com/blog/chatgpt

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RECKONING: Reasoning through Dynamic Knowledge Encoding: Generalization to Real-World knowledge

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