When One LLM Drools, Multi- LLM Collaboration Rules
Shangbin Feng · Wenxuan Ding · Alisa Liu · Zifeng Wang · Weijia Shi · Yike Wang · Shannon Zejiang Shen · Xiaochuang Han · Hunter Lang · Chen-Yu Lee · Tomas Pfister · Yejin Choi · Yulia Tsvetkov
摘要
This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status quo of relying solely on a single general-purpose LLM and argue for multi-LLM collaboration to better represent the...