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Orca 2: Enhancing Reasoning in Smaller Language Models - Conclusions and Referencesby@textmodels
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Orca 2: Enhancing Reasoning in Smaller Language Models - Conclusions and References

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Orca 2 models, by implementing a variety of reasoning techniques and recognizing the most effective solution strategy for each task, achieve performance levels comparable to, and often exceeding, models that are much larger, especially on zero-shot reasoning tasks. The study has demonstrated that improving the reasoning capabilities of smaller language models is not only possible, but also attainable through training on tailored synthetic data.
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Authors:

(1) Arindam Mitra;

(2) Luciano Del Corro, work done while at Microsoft;

(3) Shweti Mahajan, work done while at Microsoft;

(4) Andres Codas, denote equal contributions;

(5) Clarisse Simoes, denote equal contributions;

(6) Sahaj Agarwal;

(7) Xuxi Chen, work done while at Microsoft;;

(8) Anastasia Razdaibiedina, work done while at Microsoft;

(9) Erik Jones, work done while at Microsoft;

(10) Kriti Aggarwal, work done while at Microsoft;

(11) Hamid Palangi;

(12) Guoqing Zheng;

(13) Corby Rosset;

(14) Hamed Khanpour;

(15) Ahmed Awadall.

Abstract and Introduction

Preliminaries

Teaching Orca 2 to be a Cautious Reasoner

Technical Details

Experimental Setup

Evaluation Results

Limitations

Conclusions and References

A. AGIEval Subtask Metrics

B. BigBench-Hard Subtask Metrics

C. Evaluation of Grounding in Abstractive Summarization

D. Evaluation of Safety

E. Prompts used in Evaluation

F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Outpu

8 Conclusions

Our study has demonstrated that improving the reasoning capabilities of smaller language models is not only possible, but also attainable through training on tailored synthetic data. Orca 2 models, by implementing a variety of reasoning techniques and recognizing the most effective solution strategy for each task, achieve performance levels comparable to, and often exceeding, models that are much larger, especially on zero-shot reasoning tasks. Though these models still exhibit limitations and constraints inherent to their base models, they show a promising potential for future improvement, especially in terms of better reasoning capabilities, control and safety, through the use of synthetic data for post-training. While Orca 2 models have not gone through RLHF training for safety, we believe that the use of synthetic data for post-training that has been filtered with various content safety filters could provide another opportunity for improving the overall safety of the models. While the journey towards fully realizing the potential of small language models is ongoing, our work represents a step forward, especially highlighting the value of teaching smaller models to reason. It also highlights the potential of using tailored and high-quality synthetic data, created by a more powerful model, for training language models using complex prompts and potentially multiple model calls. While frontier models will continue to demonstrate superior capabilities, we believe that research toward building more capable smaller models will help pave the way for new applications that require different deployment scenarios and trade offs between efficiency and capability.

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