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Dingmin Wang

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10 papers

2 author rows

ICML Conference 2025 Conference Paper

C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation

Guoxin Chen
Minpeng Liao
Peiying Yu
Dingmin Wang
Zile Qiao
Chao Yang
Xin Zhao 0018
Kai Fan 0002

Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs). Existing approaches typically involve modifying either component or introducing simple intermediate modules, resulting in practical limitations and sub-optimal performance. Inspired by human search behavior—typically involving a back-and-forth process of proposing search queries and reviewing documents, we propose C-3PO, a proxy-centric framework that facilitates communication between retrievers and LLMs through a lightweight multi-agent system. Our framework implements three specialized agents that collaboratively optimize the entire RAG pipeline without altering the retriever and LLMs. These agents work together to assess the need for retrieval, generate effective queries, and select information suitable for the LLMs. To enable effective multi-agent coordination, we develop a tree-structured rollout approach for reward credit assignment in reinforcement learning. Extensive experiments in both in-domain and out-of-distribution scenarios demonstrate that C-3PO significantly enhances RAG performance while maintaining plug-and-play flexibility and superior generalization capabilities.

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AAAI Conference 2025 Conference Paper

Goal-Driven Reasoning in DatalogMTL with Magic Sets

Shaoyu Wang
Kaiyue Zhao
Dongliang Wei
Przemysław Andrzej Wałęga
Dingmin Wang
Hongming Cai
Pan Hu

DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applications, including tasks from industrial and financial sectors. However, due its high computational complexity, practical reasoning in DatalogMTL is highly challenging. To address this difficulty, we introduce a new reasoning method for DatalogMTL which exploits the magic sets technique—a rewriting approach developed for (non-temporal) Datalog to simulate top-down evaluation with bottom-up reasoning. We have implemented this approach and evaluated it on publicly available benchmarks, showing that the proposed approach significantly and consistently outperformed state-of-the-art reasoning techniques.

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KR Conference 2024 Conference Paper

MTLearn: Extracting Temporal Rules Using Datalog Rule Learners

Dingmin Wang
Przemysław Andrzej Wałęga
Bernardo Cuenca Grau

We propose a framework for temporal rule learning from datasets, which capitalises on the availability of increasingly mature Datalog rule learners. Our approach is based on the idea of splitting a temporal dataset into windows, extracting static rules from each window with an off-the-shelf Datalog rule learner, and then combining the obtained static rules into temporal rules corresponding to the whole dataset. Temporal rules generated by our approach are expressed in DatalogMTL and are assigned time-sensitive confidence scores. We have implemented our approach in a system MTLearn compatible with any Datalog rule learner, as well as with a range of strategies for scoring the output temporal rules. The evaluation results on the task of temporal link prediction show that our proposed approach is highly competitive, achieve performance comparable to that of state-of-the-art machine learning models for both the extrapolation and the interpolation settings, while at the same time providing interpretable results.

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AAAI Conference 2024 Conference Paper

Working Memory Capacity of ChatGPT: An Empirical Study

Dongyu Gong
Xingchen Wan
Dingmin Wang

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory.

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ECAI Conference 2023 Conference Paper

An Empirical Study of Retrieval-Enhanced Graph Neural Networks

Dingmin Wang
Shengchao Liu
Hanchen Wang 0002
Bernardo Cuenca Grau
Linfeng Song
Jian Tang 0005
Le Song
Qi Liu 0049

Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first-order Weisfeiler-Lehman test (1-WL). An effective approach to this challenge is to explicitly retrieve some annotated examples used to enhance GNN models. While retrieval-enhanced models have been proved to be effective in many language and vision domains, it remains an open question how effective retrieval-enhanced GNNs are when applied to graph datasets. Motivated by this, we want to explore how the retrieval idea can help augment the useful information learned in the graph neural networks, and we design a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. In GRAPHRETRIEVAL, for each input graph, similar graphs together with their ground-true labels are retrieved from an existing database. Thus they can act as a potential enhancement to complete various graph property predictive tasks. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs. Moreover, our empirical study also illustrates that retrieval enhancement is a promising remedy for alleviating the long-tailed label distribution problem.

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NeurIPS Conference 2023 Conference Paper

Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs

Dingmin Wang
Yeyuan Chen

As a powerful framework for graph representation learning, Graph Neural Networks (GNNs) have garnered significant attention in recent years. However, to the best of our knowledge, there has been no formal analysis of the logical expressiveness of GNNs as Boolean node classifiers over multi-relational graphs, where each edge carries a specific relation type. In this paper, we investigate $\mathcal{FOC}_2$, a fragment of first-order logic with two variables and counting quantifiers. On the negative side, we demonstrate that the R$^2$-GNN architecture, which extends the local message passing GNN by incorporating global readout, fails to capture $\mathcal{FOC}_2$ classifiers in the general case. Nevertheless, on the positive side, we establish that R$^2$-GNNs models are equivalent to $\mathcal{FOC}_2$ classifiers under certain restricted yet reasonable scenarios. To address the limitations of R$^2$-GNNs regarding expressiveness, we propose a simple graph transformation technique, akin to a preprocessing step, which can be executed in linear time. This transformation enables R$^2$-GNNs to effectively capture any $\mathcal{FOC}_2$ classifiers when applied to the "transformed" input graph. Moreover, we extend our analysis of expressiveness and graph transformation to temporal graphs, exploring several temporal GNN architectures and providing an expressiveness hierarchy for them. To validate our findings, we implement R$^2$-GNNs and the graph transformation technique and conduct empirical tests in node classification tasks against various well-known GNN architectures that support multi-relational or temporal graphs. Our experimental results consistently demonstrate that R$^2$-GNN with the graph transformation outperforms the baseline methods on both synthetic and real-world datasets

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AAAI Conference 2023 Conference Paper

Efficient Embeddings of Logical Variables for Query Answering over Incomplete Knowledge Graphs

Dingmin Wang
Yeyuan Chen
Bernardo Cuenca Grau

The problem of answering complex First-order Logic queries over incomplete knowledge graphs is receiving growing attention in the literature. A promising recent approach to this problem has been to exploit neural link predictors, which can be effective in identifying individual missing triples in the incomplete graph, in order to efficiently answer complex queries. A crucial advantage of this approach over other methods is that it does not require example answers to complex queries for training, as it relies only on the availability of a trained link predictor for the knowledge graph at hand. This approach, however, can be computationally expensive during inference, and cannot deal with queries involving negation. In this paper, we propose a novel approach that addresses all of these limitations. Experiments on established benchmark datasets demonstrate that our approach offers superior performance while significantly reducing inference times.

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AAAI Conference 2023 Conference Paper

Materialisation-Based Reasoning in DatalogMTL with Bounded Intervals

Przemysław A. Wałęga
Michał Zawidzki
Dingmin Wang
Bernardo Cuenca Grau

DatalogMTL is a powerful extension of Datalog with operators from metric temporal logic (MTL), which has received significant attention in recent years. In this paper, we investigate materialisation-based reasoning (a.k.a. forward chaining) in the context of DatalogMTL programs and datasets with bounded intervals, where partial representations of the canonical model are obtained through successive rounds of rule applications. Although materialisation does not naturally terminate in this setting, it is known that the structure of canonical models is ultimately periodic. Our first contribution in this paper is a detailed analysis of the periodic structure of canonical models; in particular, we formulate saturation conditions whose satisfaction by a partial materialisation implies an ability to recover the full canonical model via unfolding; this allows us to compute the actual periods describing the repeating parts of the canonical model as well as to establish concrete bounds on the number of rounds of rule applications required to achieve saturation. Based on these theoretical results, we propose a practical reasoning algorithm where saturation can be efficiently detected as materialisation progresses, and where the relevant periods used to evaluate entailment of queries via unfolding are efficiently computed. We have implemented our algorithm and our experiments suggest that our approach is both scalable and robust.

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AAAI Conference 2022 Conference Paper

MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

Dingmin Wang
Pan Hu
Przemysław Andrzej Wałęga
Bernardo Cuenca Grau

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a. k. a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.

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AAAI Conference 2018 Short Paper

A New Benchmark and Evaluation Schema for Chinese Typo Detection and Correction

Dingmin Wang
Gabriel Pui Cheong Fung
Maxime Debosschere
Shichao Dong
Jia Zhu
Kam-Fai Wong

Despite the vast amount of research related to Chinese typo detection, we still lack a publicly available benchmark dataset for evaluation. Furthermore, no precise evaluation schema for Chinese typo detection has been deﬁned. In response to these problems: (1) we release a benchmark dataset to assist research on Chinese typo correction; (2) we present an evaluation schema which was adopted in our NLPTEA 2017 Shared Task on Chinese Spelling Check; and (3) we report new improvements to our Chinese typo detection system ACT.

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