RoFormer: Enhanced Transformer with Rotary Position Embedding (2021 · 3,028 citations)
Self-attention has no built-in sense of word order, so transformers need position information injected from outside.
LLMs + Persona-Plug = Personalized LLMs (2025 · 31 citations)
Making LLMs adapt to individual users usually means either training a separate model per user, which is costly and fails when user data is scarce, or retrieving a few relevant past texts as demonstrations.
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026 · 8 citations)
Generative reward models (GRMs) judge LLM outputs using chain-of-thought (CoT) reasoning. They have two weaknesses. They spend reasoning effort on every input, including easy ones. They also pick among sampled reasoning chains by voting, which cannot separate sound chains from ones that only look convincing. The…
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026 · 6 citations)
Supervised fine-tuning (SFT) is usually treated as a reliable way to specialize LLMs. The authors show, however, that converged models still get part of their own training set wrong. They call this the Incomplete Learning Phenomenon (ILP), distinguish it from catastrophic forgetting and machine unlearning, and…

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Papers of our library cite their works 1,177 times (of the 149,439 papers whose bibliographies we read); 256 citing papers with a page here →.

Updated 30 October 2026