6 papers
MoCo: A One-Stop Shop for Model Collaboration Research
Shangbin Feng, Yuyang Bai, Ziyuan Yang +17
Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multiple LMs collaborate, compose, an…
Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems
Ziyuan Yang, Wenxuan Ding, Shangbin Feng +1
Language models (LMs) are increasingly used in collaboration: multiple LMs trained by different parties collaborate through routing systems, multi-agent debate, model merging, and…
SPARTA ALIGNMENT: Collectively Aligning Multiple Language Models through Combat
Yuru Jiang, Wenxuan Ding, Shangbin Feng +2
We propose SPARTA ALIGNMENT, an algorithm to collectively align multiple LLMs through competition and combat. To complement a single model's lack of diversity in generation and bia…
Teaching LLMs to Abstain across Languages via Multilingual Feedback
Shangbin Feng, Weijia Shi, Yike Wang +6
Multilingual LLMs often have knowledge disparities across languages, with larger gaps in under-resourced languages. Teaching LLMs to abstain in the face of knowledge gaps is thus a…
When One LLM Drools, Multi-LLM Collaboration Rules
Shangbin Feng, Wenxuan Ding, Alisa Liu +10
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…
Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only
Jihan Yao, Wenxuan Ding, Shangbin Feng +2
In the absence of abundant reliable annotations for challenging tasks and contexts, how can we expand the frontier of LLM capabilities with potentially wrong answers? We focus on t…