Knowledge Graphs

• A Few Words Can Distort Graphs: Knowledge Poisoning Attacks on Graph-based Retrieval-Augmented Generation of Large Language Models
  Graph-based Retrieval-Augmented Generation (GraphRAG) has recently emerged as a promising paradigm for enhancing large language models (LLMs) by converting raw text into structured knowledge graphs, i…
• A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
  we introduce a hybrid RAG system enhanced through a comprehensive suite of optimizations that significantly improve retrieval quality, augment reasoning capabilities, and refine numerical computation …
• A Personalized Recommender System based-on Knowledge Graph Embeddings
  “A knowledge graph by means of ontology creates a structured framework for a set of concepts or terms within a specific domain by arranging them in a hierarchical manner, and by using relation descrip…
• Affordable AI Assistants with Knowledge Graph of Thoughts
  Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven agents face significa…
• An Automatic Graph Construction Framework based on Large Language Models for Recommendation
  Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optim…
• Argument Summarization and its Evaluation in the Era of Large Language Models
  Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining (AM). This paper investig…
• Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
  Attention mechanisms are critical to the success of large language models (LLMs), driving significant advancements in multiple fields. However, for graph-structured data, which requires emphasis on to…
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
  we develop AUTOPROMPT, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. Using AUTOPROMPT, we show that masked language models (MLMs) have an inheren…
• Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
  “Consequently, diverging from the previously established works, this paper introduces a distinct problem-solving approach. It mainly contributes in three aspects: 1. Introduction of a novel graph st…
• Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
  Language models traditionally utilized for cross-domain generalization in natural language understanding and generation have recently demonstrated task-specific reasoning through inference-time scalin…
• CEO: Corpus-based Open-Domain Event Ontology Induction
  This paper presents CEO, a novel Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined event ontologies. Without direct supervision, CEO leverages distant supervi…
• Can Language Models Solve Graph Problems in Natural Language?
  Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures, such as planning in robotics, multi-hop question answering or knowledge probing, struct…
• Causal Claims in Economics
  We introduce a novel approach by constructing a knowledge graph for each paper in our dataset. In these graphs, nodes represent economic concepts classified using JEL codes, and edges represent relati…
• Characterizing Deep Research: A Benchmark and Formal Definition
  Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of deep research — a term also adopted by recent…
• ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling
  “Knowledge that can be incorporated into PLMs can be divided into implicit knowledge and explicit knowledge. Typical forms of implicit knowledge include word segmentation, part of speech, sentiment, a…
• CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
  We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (…
• Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation
  To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog poli…
• DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
  First, data-wise, most existing QA datasets usually feature relatively simple questions that do not reflect true “hard-to-find” cases. For example, questions in HotpotQA [Yang et al., 2018] can often …
• DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
  Large Language Models (LLMs) equipped with web search capabilities have demonstrated impressive potential for deep research tasks. However, current approaches predominantly rely on either manually eng…
• Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
  Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using spa…
• Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance
  Modern GODB have emerged as a solution for highly-connected data, and link oriented queries and algorithms [2]. In fact, they have been a valuable solution in software industry for decades. The implem…
• Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
  “Recent years have seen a surge of interest in developing chatbots with the facilitation of large-scale knowledge (Tang et al., 2022a). As a highly expressive data format, Knowledge Graphs (e.g. Conce…
• Evaluating Large Language Models in Exercises of UML Class Diagram Modeling
  The goal of this work is to evaluate the capability of LLM agents to correctly generate UML class diagrams in activities of Requirements Modeling in the field of Software Engineering. Our aim is to ev…
• Exploring Large Language Models for Knowledge Graph Completion
  “A KG is generally a multi-relational graph with entities as nodes and relations as edges. Each edge is depicted as a triplet (head entity, relation, tail entity) (abbreviated as (h, r, t)), signifyin…
• Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
  This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse re…
• Fine-tuning Pre-trained Language Models for Dialogical Argument Mining with Inference Anchoring Theory
  In this paper, we present our framework for DialAM-2024 Task A: Identification of Propositional Relations and Task B: Identification of Illocutionary Relations. The goal of Task A is to detect argumen…
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
  The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previous…
• From Louvain to Leiden: guaranteeing well-connected communities
  Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. W…
• From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
  Abstract. In dialogue generation, the naturalness of responses is crucial for effective human-machine interaction. Personalized response generation poses even greater challenges, as the responses must…
• Graph of Thoughts: Solving Elaborate Problems with Large Language Models
  “Chain-of-Thought (CoT) [70] is an approach for prompting, in which one includes the intermediate steps of reasoning within the prompt (intermediate “thoughts”), besides the task input/output. CoT was…
• Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
  Planning is a fundamental property of human intelligence. Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs. Can large languag…
• Grounding Multilingual Multimodal LLMs With Cultural Knowledge
  Multimodal Large Language Models excel in high-resource settings, but often misinterpret long-tail cultural entities and underperform in low-resource languages. To address this gap, we propose a data-…
• Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis
  The emergence of reasoning models and their integration into practical AI chat bots has led to breakthroughs in solving advanced math, deep search, and extractive question answering problems that requ…
• Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
  large language models can generate cognitive tasks, specifically category learning tasks, that match the statistics of real-world tasks, deriving rational agents adapted to these tasks using the frame…
• Informed Named Entity Recognition Decoding For Generative Language Models
  “Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition are still largely …
• Inspecting and Editing Knowledge Representations in Language Models
  [[Natural Language Inference]] Neural language models (LMs) represent facts about the world described by text. Sometimes these facts derive from training data (in most LMs, a representation of the …
• Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
  But how compelling are these AI-generated ideas, and how can we improve their quality? Here, we introduce SciMuse, which uses 58 million research papers and a large-language model to generate research…
• Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity
  “Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exp…
• JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering
  “An extensive research path is to elaborately design graph neural networks (GNNs) (Scarselli et al., 2008) to perform reasoning over explicit structural common sense knowledge from external knowledge …
• Knowledge Graph Prompting for Multi-Document Question Answering
  “The ’pre-train, prompt, predict’ paradigm of large language models (LLMs) has achieved remarkable success in open domain question answering (OD-QA). However, few works explore this paradigm in the sc…
• LLM Augmentations to support Analytical Reasoning over Multiple Documents
  Our key contributions are: 1) We conduct the first investigation of the feasibility of using LLMs in intelligence analysis where both evidencebased reasoning and analytical creativity is of utmost …
• Language Agents as Optimizable Graphs
  Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases. We unify these approaches …
• Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
  However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to …
• Large Language Models and Knowledge Graphs: Opportunities and Challenges
  “In conclusion, the recent advances on large language models (LLMs) mark an important inflection point for knowledge graph (KG) research. While important questions on the ability to combine their stre…
• Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
  Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. T…
• MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
  Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Search engines reshape the way of seeking information but often fail to align with complex human…
• Mitigating Hallucinations in Large Language Models via Causal Reasoning
  Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relationship between causal…
• Modeling Code: Is Text All You Need?
  Code LLMs have become extremely popular recently for modeling source code across a variety of tasks, such as generation, translation, and summarization. However, transformer-based models are limited i…
• Multi-Task End-to-End Training Improves Conversational Recommendation
  “The modern recommendation systems found in commercial applications are largely based on implicit preferences, such as a user’s history of web page clicks, item purchases, or media streams, with the r…
• NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoning
  We present a new AI task and baseline solution for Inter-Subjective Reasoning. We define inter-subjective information, to be a mixture of objective and subjective information possibly shared by differ…
• Neurosymbolic AI- Why, What, and How
  Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such…
• Neutralizing Bias in LLM Reasoning using Entailment Graphs
  However, recent works show that LLMs still suffer from hallucinations in NLI due to attestation bias, where LLMs overly rely on propositional memory to build shortcuts. To solve the issue, we design a…
• Octopus v4: Graph of language models
  This paper introduces a novel approach that employs functional tokens to integrate multiple open-source models, each optimized for particular tasks. Our newly developed Octopus v4 model leverages func…
• On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs
  we aim to further study the insight of the planning capability of LLMs by investigating the roles of LLMs in off-the-shelf planning frameworks. To do this, we investigate the effectiveness of embeddin…
• OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
  We study a conversational reasoning model that strategically traverses through a largescale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For…
• Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs
  Chain-of-Thought (CoT) prompting enhances mathematical reasoning in large language models (LLMs) by enabling detailed step-by-step solutions. However, due to the verbosity of LLMs, the resulting reaso…
• Proactive Human-Machine Conversation with Explicit Conversation Goals
  Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rathe…
• RAG-Gym: Systematic Optimization of Language Agents for Retrieval-Augmented Generation
  Retrieval-augmented generation (RAG) has shown great promise for knowledge-intensive tasks and recently advanced with agentic RAG, where language agents engage in multi-round interactions with externa…
• ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
  In this paper, we propose a new dataset, ReasonVQA, for the Visual Question Answering (VQA) task. Our dataset is automatically integrated with structured encyclopedic knowledge and constructed using a…
• RevCore: Review-augmented Conversational Recommendation
  CR remains challengeable because (i) typical dialogues are short and lack sufficient item information for user preference capturing (Chen et al., 2019; Zhou et al., 2020), and (ii) difficulties exist …
• Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
  Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables th…
• SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
  Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing …
• Schema-learning and rebinding as mechanisms of in-context learning and emergence
  “In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that underlie it are poor…
• Self-Organizing Graph Reasoning Evolves into a Critical State for Continuous Discovery Through Structural-Semantic Dynamics
  However, little is known about the mechanisms by which models, especially reasoning models, develop answers and whether general principles can be extracted. One particular class of reasoning models, a…
• SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
  Recently, the rapid development of Large Language Models (LLMs) has opened new frontiers in table processing (Li et al., 2023b) and reasoning (Cheng et al., 2022). However, spreadsheets pose unique ch…
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data
  In this paper, we aim to improve the reasoning ability of large language models (LLMs) over structured data in a unified way. Inspired by the studies on tool augmentation for LLMs, we develop an Itera…
• SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs
  Recent advancements have highlighted that Large Language Models (LLMs) are prone to hallucinations when solving complex reasoning problems, leading to erroneous results. To tackle this issue, research…
• Talk like a Graph: Encoding Graphs for Large Language Models
  Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance. Reasoning on graphs …
• Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph
  “Large language models (LLMs) have made significant strides in various tasks, yet they often struggle with complex reasoning and exhibit poor performance in scenarios where knowledge traceability, tim…
• Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties
  Recent large-scale reasoning models have achieved state-of-the-art performance on challenging mathematical benchmarks, yet the internal mechanisms underlying their success remain poorly understood. In…
• Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers
  Representing discourse as argument graphs facilitates robust analysis. Although computational frameworks for constructing graphs from monologues exist, there is a lack of frameworks for parsing dialog…
• UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
  However, graph learning has predominantly focused on single-graph models, tailored to specific tasks or datasets, lacking the ability to transfer learned knowledge to different domains. This limitatio…
• Unifying Large Language Models and Knowledge Graphs: A Roadmap
  “Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizabilit…
• Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation
  Graph-based retrieval-augmented generation (RAG) enables large language models (LLMs) to ground responses with structured external knowledge from up-to-date knowledge graphs (KGs) and reduce hallucina…
• What is a Discourse Graph?
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• Why Do Multi-agent LLM Systems Fail?
  [[Routers]] Despite growing enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains across popular benchmarks often remain minimal compared to single-agent frameworks. This gap highlig…
• You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures
  Large language models (LLMs) often suffer from hallucination, generating factually incorrect statements when handling questions beyond their knowledge and perception. Retrieval-augmented generation (R…