Evaluating World Models with LLM for Decision Making

World model emerges as a key module in decision making, where MuZero and Dreamer achieve remarkable successes in complex tasks. Recent work leverages Large Language Models (LLMs) as general world simulators to simulate the dynamics of the world due to their generalizability. LLMs also serve as the world model for deliberative reasoning in Reasoning via Planning (RAP) and Tree of Thought (ToT). However, the world models are either evaluated as a general world simulator, or as a functional module of the agent, i.e., predicting the transitions to assist the planning. In this work, we propose a comprehensive evaluation of the world models with LLMs from the decision making perspective. Specifically, we leverage the 31 diverse environments from (Wang et al., 2023;2024) and curate the rule-based policy of each environment for the diverse evaluation. Then, we design three main tasks, i.e., policy verification, action proposal, and policy planning, where the world models can be used for decision making solely. Finally, we conduct the comprehensive evaluation of the advanced LLMs, i.e., GPT-4o and GPT-4o-mini, on the environments for the three main tasks under various settings. The key observations include: i) GPT-4o significantly outperforms GPT-4o-mini on the three main tasks, especially for the tasks which require the domain knowledge, ii) the performance of the world model with LLM will be decreased for long-term decision-making tasks, and iii) the combination of different functionalities of the world model will brings additional unstabilities of the performance.

Neuro-Symbolic Entity Alignment via Variational Inference

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic models, while precise, struggle with substructure heterogeneity and sparsity, whereas neural models, although effective, generally lack interpretability and cannot handle uncertainty. We propose NeuSymEA, a probabilistic neuro-symbolic framework that combines the strengths of both methods. NeuSymEA models the joint probability of all possible pairs' truth scores in a Markov random field, regulated by a set of rules, and optimizes it with the variational EM algorithm. In the E-step, a neural model parameterizes the truth score distributions and infers missing alignments. In the M-step, the rule weights are updated based on the observed and inferred alignments. To facilitate interpretability, we further design a path-ranking-based explainer upon this framework that generates supporting rules for the inferred alignments. Experiments on benchmarks demonstrate that NeuSymEA not only significantly outperforms baselines in terms of effectiveness and robustness, but also provides interpretable results.

Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL

Generating accurate SQL according to natural language questions (text-to-SQL) is a long-standing problem since it is challenging in user question understanding, database schema comprehension, and SQL generation. Conventional text-to-SQL systems include human engineering and deep neural networks. Subsequently, pre-trained language models (PLMs) have been developed and utilized for text-to-SQL tasks, achieving promising performance. As modern databases become more complex and corresponding user questions more challenging, PLMs with limited comprehension capabilities can lead to incorrect SQL generation. This necessitates more sophisticated and tailored optimization methods, which, in turn, restricts the applications of PLM-based systems. Most recently, large language models (LLMs) have demonstrated significant abilities in natural language understanding as the model scale remains increasing. Therefore, integrating the LLM-based implementation can bring unique opportunities, challenges, and solutions to text-to-SQL research. In this survey, we present a comprehensive review of LLM-based text-to-SQL. Specifically, we propose a brief overview of the current challenges and the evolutionary process of text-to-SQL. Then, we provide a detailed introduction to the datasets and metrics designed to evaluate text-to-SQL systems. After that, we present a systematic analysis of recent advances in LLM-based text-to-SQL. Finally, we discuss the remaining challenges in this field and propose expectations for future directions.

Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion

Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are constantly evolving and introducing new knowledge. Recent studies have shown promising results using message passing over subgraphs to embed newly-coming entities for inductive KGC. However, the inductive capability of these methods is usually limited by two key issues. (i) KGC always suffers from data sparsity, and the situation is even exacerbated in inductive KGC where new entities often have few or no connections to the original KG. (ii) Cold-start problem. It is over coarse-grained for accurate KG reasoning to generate representations for new entities by gathering the local information from few neighbors. To this end, we propose a novel iNfOmax RelAtion Network, namely NORAN, for inductive KG completion. It aims to mine latent relation patterns for inductive KG completion. Specifically, by centering on relations, NORAN provides a hyper view towards KG modeling, where the correlations between relations can be naturally captured as entity-independent logical evidence to conduct inductive KGC. Extensive experiment results on five benchmarks show that our framework substantially outperforms the state-of-the-art KGC methods.

Entity Alignment with Noisy Annotations from Large Language Models

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibitively expensive to incorporate cross-domain experts for annotation in real-world scenarios. The advent of Large Language Models (LLMs) presents new avenues for automating EA with annotations, inspired by their comprehensive capability to process semantic information. However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels that may mislead the alignment. To this end, we propose a unified framework, LLM4EA, to effectively leverage LLMs for EA. Specifically, we design a novel active learning policy to significantly reduce the annotation space by prioritizing the most valuable entities based on the entire inter-KG and intra-KG structure. Moreover, we introduce an unsupervised label refiner to continuously enhance label accuracy through in-depth probabilistic reasoning. We iteratively optimize the policy based on the feedback from a base EA model. Extensive experiments demonstrate the advantages of LLM4EA on four benchmark datasets in terms of effectiveness, robustness, and efficiency. Codes are available via https://github.com/chensyCN/llm4ea_official.

Cost-efficient Knowledge-based Question Answering with Large Language Models

Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving. However, it remains challenging since accuracy and cost are not readily combined in the optimization as two distinct metrics. It is also laborious for model selection since different models excel in diverse knowledge. To this end, we propose Coke, a novel cost-efficient strategy for KBQA with LLMs, modeled as a tailored multi-armed bandit problem to minimize calls to LLMs within limited budgets. We first formulate the accuracy expectation with a cluster-level Thompson Sampling for either KGMs or LLMs. A context-aware policy is optimized to further distinguish the expert model subject to the question semantics. The overall decision is bounded by the cost regret according to historical expenditure on failures. Extensive experiments showcase the superior performance of Coke, which moves the Pareto frontier with up to 20.89% saving of GPT-4 fees while achieving a 2.74% higher accuracy on the benchmark datasets.

Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering

Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LLMs) as an implicit knowledge source, it remains challenging since LLMs may generate hallucinations. Moreover, multiple knowledge sources, e.g., images, KGs and LLMs, cannot be readily aligned for complex scenarios. To tackle these, we present a novel modality-aware integration with LLMs for KVQA (MAIL). It carefully leverages multimodal knowledge for both image understanding and knowledge reasoning. Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a scene graph with detailed visual features; (ii) We construct a coupled concept graph by linking the mentioned entities with external facts. (iii) A tailored pseudo-siamese graph medium fusion is designed for sufficient multimodal fusion. We utilize the shared mentioned entities in two graphs as mediums to bridge a tight inter-modal exchange, while maximally preserving insightful intra-modal learning by constraining the fusion within mediums. Extensive experiments on two benchmark datasets show the superiority of MAIL with 24x less resources.

Structure Guided Large Language Model for SQL Generation

Generating accurate Structured Querying Language (SQL) is a long-standing problem, especially in matching users' semantic queries with structured databases and then generating structured SQL. Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL. However, such solutions overlook the structural information within user queries and databases, which can be utilized to enhance the generation of structured SQL. This oversight can lead to inaccurate or unexecutable SQL generation. To fully exploit the structure, we propose a structure-to-SQL framework, which leverages the inherent structure information to improve the SQL generation of LLMs. Specifically, we introduce our Structure Guided SQL~(SGU-SQL) generation model. SGU-SQL first links user queries and databases in a structure-enhanced manner. It then decomposes complicated linked structures with grammar trees to guide the LLM to generate the SQL step by step. Extensive experiments on two benchmark datasets illustrate that SGU-SQL can outperform sixteen SQL generation baselines.

Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM

Generating accurate SQL for user queries (text-to-SQL) is a long-standing problem since the generation of the SQL requires comprehending the query and database and retrivale the accurate data from the database accordingly. Existing models rely on the comprehensive ability of Large Language Models (LLMs) to generate the SQL according to the database schema. However, there is some necessary knowledge that is not explicitly included in the database schema or has been learned by LLMs. Thus, the generated SQL of the knowledge-insufficient queries may be inaccurate, which negatively impacts the robustness of the text-to-SQL models. To deal with this situation, we propose the Knowledge-to-SQL framework, which employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all types of text-to-SQL models. Specifically, we provide the detailed design of DELLM, in terms of table reading, and the basic fine-tuning process. We further provide a Reinforcement Learning via Database Feedback (RLDBF) training strategy to guide the DELLM to generate more helpful knowledge for LLMs. Extensive experiments verify DELLM can enhance the state-of-the-art LLMs on text-to-SQL tasks. The model structure and the parameter weight of DELLM are released for further research.

KnowGPT: Black-Box Knowledge Injection for Large Language Models

Generative Large Language Models (LLMs), such as ChatGPT, offer interactive APIs that can answer common questions at a human-expert level. However, these models often give inaccurate or incorrect responses when faced with questions requiring domain-specific or professional-specific knowledge not covered in their training corpus. Furthermore, many state-of-the-art LLMs are not open-source, making it challenging to inject knowledge with model APIs only. In this work, we introduce KnowGPT, a black-box knowledge injection framework for LLMs in question answering. KnowGPT leverages deep reinforcement learning (RL) to extract relevant knowledge from Knowledge Graphs (KGs) and use Multi-Armed Bandit (MAB) to construct the most suitable prompt for each question. Our extensive experiments on three benchmark datasets showcase that KnowGPT significantly enhances the existing methods. Notably, KnowGPT achieves an average improvement of 23.7% over ChatGPT and an average improvement of 2.9% over GPT-4. Additionally, KnowGPT attains a 91.6% accuracy on the OpenbookQA official leaderboard, which is comparable to human-level performance.