76 citations · 82 across the 10 of their papers we have counts for
5 papers · 1 filter
A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users
Nishant Balepur, Matthew Shu, Yoo Yeon Sung +5
To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or e…
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models
Bang An, Shiyue Zhang, Mark Dredze
Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…
Stream Aligner: Efficient Sentence-Level Alignment via Distribution Induction
Hantao Lou, Jiaming Ji, Kaile Wang +1
The rapid advancement of large language models (LLMs) has led to significant improvements in their capabilities, but also to increased concerns about their alignment with human val…
Language Models Resist Alignment: Evidence From Data Compression
Jiaming Ji, Kaile Wang, Tianyi Qiu +7
Large language models (LLMs) may exhibit unintended or undesirable behaviors. Recent works have concentrated on aligning LLMs to mitigate harmful outputs. Despite these efforts, so…
Aligner: Efficient Alignment by Learning to Correct
Jiaming Ji, Boyuan Chen, Hantao Lou +6
With the rapid development of large language models (LLMs) and ever-evolving practical requirements, finding an efficient and effective alignment method has never been more critica…