8 citations · 9 across the 2 of their papers we have counts for
3 papers
cs.CL2025
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
cs.CL2024★ 1 cited
Steering Without Side Effects: Improving Post-Deployment Control of Language Models
Asa Cooper Stickland, Alexander Lyzhov, Jacob Pfau +2
Language models (LMs) have been shown to behave unexpectedly post-deployment. For example, new jailbreaks continually arise, allowing model misuse, despite extensive red-teaming an…
cs.AI2023★ 8 cited
Debate Helps Supervise Unreliable Experts
Julian Michael, Salsabila Mahdi, David Rein +4
As AI systems are used to answer more difficult questions and potentially help create new knowledge, judging the truthfulness of their outputs becomes more difficult and more impor…