Qualitative feedback. Text feedback is usually offered in natural language, particularly for re-
sponses that may need improvement. The format of this feedback is quite flexible. Humans provide
advice on how to modify outputs generated by agents, and the agents then incorporate these sug-
gestions to refine their subsequent outputs [470; 471]. For agents without multimodal perception
capabilities, humans can also act as critics, offering visual critiques [190], for instance. Additionally,
agents can utilize a memory module to store feedback for future reuse [472]. In [473], humans give
feedback on the initial output generated by agents, prompting the agents to formulate various improve-
ment proposals. The agents then discern and adopt the most suitable proposal, harmonizing with the
human feedback. While this approach can better convey human intention compared to quantitative
feedback, it might be more challenging for the agents to comprehend. Xu et al. [474] compare various
TheRiseandPotentialofLargeLanguageModel BasedAgents