Self-consistency for open-ended generations
In this paper, we present a novel approach for improving the quality and
consistency of generated outputs from large-scale pre-trained language models
(LLMs). Self-consistency has emerged as an effective approach for prompts with
fixed answers, selecting the answer with the highest number of votes. In this
paper, we introduce a generalized framework for self-consistency that extends
its applicability beyond problems that have fixed-answer answers. Through
extensive simulations, we demonstrate that our approach consistently recovers
the optimal or near-optimal generation from a set of candidates. We also
propose lightweight parameter-free similarity functions that show significant
and consistent improvements across code generation, autoformalization, and
summarization tasks, even without access to token log probabilities. Our method
incurs minimal computational overhead, requiring no auxiliary reranker models
or modifications to the existing model.
64b0b90a2a98a6d6494aa823
Siddhartha Jain
64b0b90a2a98a6d6494aa824
Xiaofei Ma
64b0b90a2a98a6d6494aa825
Anoop Deoras
64b0b90a2a98a6d6494aa826
Bing Xiang
Self-consistency for open-ended generations
In this paper, we present a novel approach for improving the quality and
consistency of generated outputs from large-scale pre-trained language models
(LLMs). Self-consistency has emerged as an effective approach for prompts with
fixed answers, selecting the answer with the highest number of votes. In this
paper, we introduce a generalized framework for self-consistency that extends
its applicability beyond problems that have fixed-answer answers. Through
extensive simulations, we demonstrate that our approach consistently recovers
the optimal or near-optimal generation from a set of candidates. We also
propose lightweight parameter-free similarity functions that show significant
and consistent improvements across code generation, autoformalization, and
summarization tasks, even without access to token log probabilities. Our method
incurs minimal computational overhead, requiring no auxiliary reranker models
or modifications to the existing model.
A generalized self-consistency framework enhances the quality and consistency of large-scale language model outputs across various tasks with minimal computational overhead.
self-consistency large-scale pre-trained language models LLMs token log probabilities code generation autoformalization summarization