World models achieve remarkable success in predicting future states and planning in complex environments and Large Language Models (LLMs) serve as promising foundation to build general world models. However, their performances are usually constrained by the limited external knowledge to specific environments. Existing research attempts to enhance LLM-based world models through prompting or fine-tuning approaches, which are either requiring human knowledge or computationally extensive. Therefore, we introduce Retrieval-Augmented World Models (RAWM), a novel framework that leverages retrieval-augmented generation to efficiently integrate the external knowledge to LLM-based world models. Our main contributions are threefold: (i) We introduce a memory system and design an embedding model to retrieve relevant experiences as the in-context examples to improve the world model’s predictive accuracy. (ii) We develop a reinforcement learning (RL) training pipeline that fine-tunes a small MLP head on the pre-trained embedding model using Proximal Policy Optimization (PPO), further enhancing prediction performance. (iii) We conduct extensive experiments across three diverse environments, i.e., Game24, BlocksWorld, and BabyAI, demonstrating that RAWM consistently outperforms baseline models and exhibits strong generalizability. By leveraging the retrieval-augmented generation and the efficient RL training pipeline, RAWM dynamically utilizes relevant historical experiences and equips LLMs with environment-specific external knowledge without retraining, enabling more accurate and generalizable predictions.

Idioms condense complex semantics into fixed phrases, and their meaning is often not directly connected to the literal meaning of their constituent words, making idiom comprehension a test of metaphor competence. Metaphor, as a cognitive process in human beings, has not yet found an effective evaluation method to assess the metaphor competence of LLMs (Large Language Models). In this paper, we propose a method to evaluate the metaphor competence of LLMs for the idiom understanding task: the Consistency Rating of Semantic Transparency (CR-ST). This strategy assesses the difficulty of understanding idioms through two dimensions: overall semantic transparency and constituent semantic transparency, aiming to gauge LLMs’ mastery of metaphor competence. Subsequently, we introduce a prompt mechanism-Paraphrase Augmentation Strategy with Self-checking (PASS), based on human language logic, which guides the model to enhance its metaphor competence by explicitly generating idiom paraphrases. We conducted a baseline evaluation of seven LLMs on the CINLID and ChID datasets and analyzed the effectiveness of PASS on different subsets of semantic transparency. The experimental results demonstrate that LLMs can achieve performance comparable to PLMs (Pre-trained Language Models) without additional training, and PASS has a positive effect on the metaphor competence of LLMs.

Natural language has been extensively used for modeling text-attributed graphs with LLMs. Natural language is used to describe the graph for LLMs to understand or serve as component of the graph, e.g., textual attributes for embedding generation. However, natural language is inherently redundant and unstructured, making it unsuitable for modeling high-order neighbors with LLMs. Specifically, (i) graph descriptions become verbose, overwhelming LLMs, and (ii) only relying on attribute embeddings limits LLM’s ability to capture the adequate graph structural information. These limitations make it difficult to model graphs both concisely and adequately using sole natural language with LLMs.Inspired by the observation that LLMs pre-trained on one language can achieve exceptional performance on another with minimal additional training, we propose Graph-Defined Language for Large Language Model (GDL4LLM). This novel framework enables LLMs to transfer their powerful language understanding capabilities to graph-structured data. GDL4LLM translates the graph into a graph language corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. This corpus represents the subgraph centered around target nodes concisely with only a few tokens during fine-tuning on downstream tasks. By treating the graph as a new language, GDL4LLM enables LLMs to model text-attributed graph adequately and concisely. Extensive experiments on five datasets demonstrate that GDL4LLM outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors.

In the era of widespread dissemination through social media, the task of rumor detection plays a pivotal role in establishing a trustworthy and reliable information environment. Nonetheless, existing research on rumor detection confronts several challenges: the limited expressive power of text encoding sequences, difficulties in domain knowledge coverage and effective information extraction with knowledge graph-based methods, and insufficient mining of semantic structural information. To address these issues, we propose a Crowd Intelligence and ChatGPT-Assisted Network(CICAN) for rumor classification. Specifically, we present a crowd intelligence-based semantic feature learning module to capture textual content’s sequential and hierarchical features. Then, we design a knowledge-based semantic structural mining module that leverages ChatGPT for knowledge enhancement. Finally, we construct an entity-sentence heterogeneous graph and design Entity-Aware Heterogeneous Attention to effectively integrate diverse structural information meta-paths. Experimental results demonstrate that CICAN achieves performance improvement in rumor detection tasks, validating the effectiveness and rationality of using large language models as auxiliary tools.