Linear attention enhances inference efficiency of Transformer and has attracted research interests as an efficient backbone of language models. Existing linear attention based models usually exploit decay factor based positional encoding (PE), where attention scores decay exponentially with increasing relative distance. However, most work manually designs a non-trainable decay factor of exponential calculation, which limits further optimization. Our analysis reveals directly training decay factor is unstable because of large gradients. To address this, we propose a novel PE for linear attention named Disentangle to Decay (D2D). D2D disentangles decay factor into two parts to achieve further optimization and stable training. Moreover, D2D can be transformed into recurrent form for efficient inference. Experiments demonstrate that D2D achieves stable training of decay factor, and enhances performance of linear attention in both normal context length and length extrapolation scenarios.

Large language models are prone to generating hallucination that deviates from factual information. Existing studies mainly focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics. To address this, we introduce the concept of belief state, which quantifies the model’s confidence in its own responses. We define the belief state of the model based on self-consistency, leveraging answer repetition rates to label confident and uncertain states. Based on this, we categorize factuality hallucination into two types: Overconfident Hallucination and Unaware Hallucination. Furthermore, we propose BAFH, a factuality hallucination type detection method. By training a classifier on model’s hidden states, we establish a link between hidden states and belief states, enabling efficient and automatic hallucination type detection. Experimental results demonstrate the effectiveness of BAFH and the differences between hallucination types.

Multi-Aspect Controllable Text Generation (MCTG) introduces fine-grained multiple constraints in natural language generation, i.e. control attributes in topics, sentiments, and detoxification.MCTG demonstrates application prospects for trustworthy generation of Large Language Models (LLMs) but is limited by generalization issues.Existing work exploits additional structures and strategies for solutions, requiring LLMs’ modifications.To activate LLMs’ MCTG ability, we propose a lightweight MCTG pipeline based on data augmentation and instruction tuning.We analyze aspect bias and correlations in traditional datasets and address these concerns with augmented control attributes and sentences.Augmented datasets are feasible for instruction tuning.We conduct experiments for various LLMs backbone and parameter sizes, demonstrating general effectiveness on MCTG performance.