GUT：量化优化LLM推理不确定性
GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity
GUT方法通过图复杂度量化LLM推理不确定性，并用强化学习优化，解决了LLM推理过程中的分支发散问题。
研究人员提出Graph-complexity-based UncerTainty (GUT)方法，用于研究大语言模型的推理不确定性。GUT通过有向无环图表征每个推理链的潜在分支，确保全面覆盖所有可能性。GUT-Q模块通过图复杂度近似推理空间复杂度来测量LLM推理不确定性。GUT-O模块将负不确定性作为强化学习的奖励函数来实现不确定性优化。实验在四个LLM和五个数据集上验证了GUT的有效性。
GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity
Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhibit uncertainty, where LLMs often produce a proliferation of divergent branches at each reasoning step even when fed the same prompting inputs, and certain branches exhibit evidently incredible, even nonsensical, reasoning chains and results. In this paper, we propose the Graph-complexity-based UncerTainty (GUT) method for investigating the reasoning uncertainty of LLMs. The key idea of GUT is to characterize the potential branches of each reasoning chain with a directed acyclic graph, thereby ensuring that all potential branches are comprehensively covered within the graph space. Building upon this recognition, we further build two modules of GUT, that is, a Quantification (GUT-Q) module and an Optimization (GUT-O) module, for quantifying and reducing the reasoning uncertainty of LLMs, respectively. GUT-Q measures LLM reasoning uncertainty by approximating the reasoning space complexity with graph complexity. GUT-O implements uncertainty optimization by treating negative uncertainty as the reward function in reinforcement learning. Experimental results conducted on four LLMs and five datasets validate the effectiveness of GUT.