Aligning Large Language Models with Human Preferences through Representation Engineering (2024 · 37 citations)
Aligning LLMs with human preferences usually relies on RLHF. RLHF needs extra reward and value models, is expensive and is often unstable to train. Cheaper reward-free alternatives such as DPO and HIR react badly to noisy or mislabeled preference pairs. The authors propose RAHF (Representation Alignment from Human…
Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective (2025 · 17 citations)
The paper asks why LLMs that have been through safety alignment can still be jailbroken. It approaches the question through representation engineering. The authors argue that refusals of harmful requests come from particular activation signatures inside the model, which they call "safety patterns". To find them, they…
Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling (2026)
Long-context LLMs that use RoPE tend to concentrate attention on the start and end of the input. As a result, they underuse content in the middle, a problem known as "lost-in-the-middle." Earlier fixes such as Attention Buckets, MoICE and Ms-PoE mix several RoPE bases or scales. However, they need repeated forward…

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Papers of our library cite their works 47 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated 1 November 2026