開放式生成的自洽性

摘要

本文提出了一種新方法，用於改善大規模預訓練語言模型（LLMs）生成輸出的質量和一致性。自一致性已被證明是一種有效的方法，適用於具有固定答案提示的情況，選擇得票數最高的答案。本文介紹了一個廣義的自一致性框架，擴展了其適用範圍，超越了具有固定答案的問題。通過大量模擬，我們展示了我們的方法能夠穩定地從一組候選生成中恢復最優或接近最優的生成。我們還提出了輕量級無參數相似性函數，即使沒有訪問令牌日誌概率，也在代碼生成、自動正規化和摘要任務中顯示出顯著且一致的改進。我們的方法帶來了極小的計算開銷，無需輔助的重新排名模型或對現有模型進行修改。

English

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.

開放式生成的自洽性

Self-consistency for open-ended generations

摘要

Support