Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback. (arXiv:2304.10750v1 [cs.CL])

Many approaches to Natural Language Processing (NLP) tasks often treat them
as single-step problems, where an agent receives an instruction, executes it,
and is evaluated based on the final outcome. However, human language is
inherently interactive, as evidenced by the back-and-forth nature of human
conversations. In light of this, we posit that human-AI collaboration should
also be interactive, with humans monitoring the work of AI agents and providing
feedback that the agent can understand and utilize. Further, the AI agent …

agents ai agents ai collaboration arxiv collaboration collaborative conversations environment feedback human humans interactive language language processing language understanding light monitoring natural natural language natural language processing nature nlp posit processing through understanding work