Recent advancements in large language models (LLMs) have demonstrated their impressive generative capabilities, primarily due to their extensive parameterization, which enables them to encode vast knowledge. However, effectively integrating new knowledge into LLMs remains a major challenge. Current research typically first constructs novel knowledge datasets and then injects this knowledge into LLMs through various techniques. However, existing methods for constructing new datasets either rely on timestamps, which lack rigor, or use simple templates for synthesis, which are simplistic and do not accurately reflect the real world. To address this issue, we propose a novel knowledge dataset construction approach that simulates biological evolution using knowledge graphs to generate synthetic entities with diverse attributes, resulting in a dataset, NovelHuman. Systematic analysis on NovelHuman reveals that the intra-sentence position of knowledge significantly affects the acquisition of knowledge. Therefore, we introduce an intra-sentence permutation to enhance knowledge acquisition. Furthermore, given that potential conflicts exist between autoregressive (AR) training objectives and permutation-based learning, we propose PermAR, a permutation-based language modeling framework for AR models. PermAR seamlessly integrates with mainstream AR architectures, endowing them with bidirectional knowledge acquisition capabilities. Extensive experiments demonstrate the superiority of PermAR, outperforming knowledge augmentation methods by 3.3%-38%.

Topic modeling aims to discover the distribution of topics within a corpus. The advanced comprehension and generative capabilities of large language models (LLMs) have introduced new avenues for topic modeling, particularly by prompting LLMs to generate topics and refine them by merging similar ones. However, this approach necessitates that LLMs generate topics with consistent granularity, thus relying on the exceptional instruction-following capabilities of closed-source LLMs (such as GPT-4) or requiring additional training. Moreover, merging based only on topic words and neglecting the fine-grained semantics within documents might fail to fully uncover the underlying topic structure. In this work, we propose a semi-supervised topic modeling method, LiSA, that combines LLMs with clustering to improve topic generation and distribution. Specifically, we begin with prompting LLMs to generate a candidate topic word for each document, thereby constructing a topic-level semantic space. To further utilize the mutual complementarity between them, we first cluster documents and candidate topic words, and then establish a mapping from document to topic in the LLM-guided assignment stage. Subsequently, we introduce a collaborative enhancement strategy to align the two semantic spaces and establish a better topic distribution. Experimental results demonstrate that LiSA outperforms state-of-the-art methods that utilize GPT-4 on topic alignment, and exhibits competitive performance compared to Neural Topic Models on topic quality. The codes are available at https://github.com/ljh986/LiSA.