Ensuring trustworthiness has consistently remained a critically important yet challenging issue within
the field of deep learning [633; 634; 635]. Deep neural networks have garnered significant attention
for their remarkable performance across various tasks [41; 262; 636]. However, their black-box
nature has masked the fundamental factors for superior performance. Similar to other neural networks,
LLMs struggle to express the certainty of their predictions precisely [635; 637]. This uncertainty,
referred to as the calibration problem, raises concerns for applications involving language model-
based agents. In interactive real-world scenarios, this can lead to agent outputs misaligned with
human intentions [94]. Moreover, biases inherent in training data can infiltrate neural networks
[638; 639]. For instance, biased language models might generate discourse involving racial or gender
discrimination, which could be amplified in LLM-based agent applications, resulting in adverse
TheRiseandPotentialofLargeLanguageModel BasedAgents