Iteratively improving and repairing source code with large language models
(LLMs), known as refinement, has emerged as a popular way of generating
programs that would be too complex to construct in one shot. Given a bank of
test cases, together with a candidate program, an LLM can improve that program
by being prompted with failed test cases. But it remains an open question how
to best iteratively refine code, with prior work employing simple greedy or
breadth-first strategies. We show here that refinement exposes an
explore-exploit tradeoff: exploit by refining the program that passes the most
test cases, or explore by refining a lesser considered program. We frame this
as an arm-acquiring bandit problem, which we solve with Thompson Sampling. The
resulting LLM-based program synthesis algorithm is broadly applicable: Across
loop invariant synthesis, visual reasoning puzzles, and competition programming
problems, we find that our new method can solve more problems using fewer
language model calls.

利用大型语言模型（LLMs）迭代改进和修复源代码已成为一种流行的方法，该方法被称为细化，可生成过于复杂无法一次构建的程序。我们发现细化代码暴露了探索与利用的权衡：通过改进通过测试用例的程序进行利用，或通过改进较少考虑的程序进行探索。我们将其视为一种获取臂的赌博机问题，并用汤普森采样解决。得到的基于 LLM 的程序合成算法具有广泛的适用性：在循环不变量合成、视觉推理谜题和竞赛编程问题中，我们发现我们的新方法可以在更少的语言模型调用下解决更多问题。

使用 LLMs 进行代码修复的探索 - 利用权衡

Code Repair with LLMs gives an Exploration-Exploitation Tradeoff

Many existing works on voice conversion (VC) tasks use automatic speech
recognition (ASR) models for ensuring linguistic consistency between source and
converted samples. However, for the low-data resource domains, training a
high-quality ASR remains to be a challenging task. In this work, we propose a
novel iterative way of improving both the ASR and VC models. We first train an
ASR model which is used to ensure content preservation while training a VC
model. In the next iteration, the VC model is used as a data augmentation
method to further fine-tune the ASR model and generalize it to diverse
speakers. By iteratively leveraging the improved ASR model to train VC model
and vice-versa, we experimentally show improvement in both the models. Our
proposed framework outperforms the ASR and one-shot VC baseline models on
English singing and Hindi speech domains in subjective and objective
evaluations in low-data resource settings.

本论文中，我们提出了一种新颖的迭代方法，用于同时提高自动语音识别模型和语音转换模型。我们在低数据资源情况下，通过使用语音转换模型作为数据增强方法来进一步微调 ASR 模型，从而实验性地展示了两个模型的性能提高。

逐步提升语音识别和语音转换

Iteratively Improving Speech Recognition and Voice Conversion

Constructing the adjacency graph is fundamental to graph-based clustering.
Graph learning in kernel space has shown impressive performance on a number of
benchmark data sets. However, its performance is largely determined by the
chosen kernel matrix. To address this issue, the previous multiple kernel
learning algorithm has been applied to learn an optimal kernel from a group of
predefined kernels. This approach might be sensitive to noise and limits the
representation ability of the consensus kernel. In contrast to existing
methods, we propose to learn a low-rank kernel matrix which exploits the
similarity nature of the kernel matrix and seeks an optimal kernel from the
neighborhood of candidate kernels. By formulating graph construction and kernel
learning in a unified framework, the graph and consensus kernel can be
iteratively enhanced by each other. Extensive experimental results validate the
efficacy of the proposed method.

通过将图构建和核学习统一框架，可以通过彼此迭代的方式增强图及共识核，而我们提出了一种学习低秩核矩阵的方法，从候选核之间的邻域中寻找最优核矩阵，进而解决了现有多核学习算法中的一些问题，并得到了验证。