GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

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   GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

   Costas Mavromatis, George Karypis · 2024 · Computation and Language · AI summary available

   Abstract

   Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of answering natural questions grounding the reasoning to the information provided by the KG. Large Language Models (LLMs) are the state-of-the-art models for QA tasks due to their remarkable ability to understand natural language. On the other hand, Graph Neural Networks (GNNs) have been widely used for KGQA as they can handle the complex graph information stored in the KG. In this work, we introduce GNN-RAG, a novel method for combining language understanding abilities of LLMs with the reasoning abil…

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   LMExplainer: a Knowledge-Enhanced Explainer for Language Models

   Zichen Chen, Ambuj K Singh, Misha Sra · 2023 · Computation and Language · AI summary available

   Abstract

   Large language models (LMs) such as GPT-4 are very powerful and can process different kinds of natural language processing (NLP) tasks. However, it can be difficult to interpret the results due to the multi-layer nonlinear model structure and millions of parameters. Lack of understanding of how the model works can make the model unreliable and dangerous for everyday users in real-world scenarios. Most recent works exploit the weights of attention to provide explanations for model predictions. However, pure attention-based explanation is unable to support the growing complexity of the models, and cannot reason about their decision-making processes. Thus, we propose LMExplainer, a knowledge-en…

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   Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering

   Ojas Gramopadhye, Saeel Sandeep Nachane, Prateek Chanda et al. · 2024 · Computation and Language · AI summary available

   Abstract

   Large Language models (LLMs) have demonstrated significant potential in transforming healthcare by automating tasks such as clinical documentation, information retrieval, and decision support. In this aspect, carefully engineered prompts have emerged as a powerful tool for using LLMs for medical scenarios, e.g., patient clinical scenarios. In this paper, we propose a modified version of the MedQA-USMLE dataset, which is subjective, to mimic real-life clinical scenarios. We explore the Chain of Thought (CoT) reasoning based on subjective response generation for the modified MedQA-USMLE dataset with appropriate LM-driven forward reasoning for correct responses to the medical questions. Keeping…

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   FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering

   Zhengyi Zhao, Shubo Zhang, Zezhong Wang et al. · 2025 · Computation and Language · AI summary available

   Abstract

   Long-context question-answering (LCQA) systems have greatly benefited from the powerful reasoning capabilities of large language models (LLMs), which can be categorized into slow and quick reasoning modes. However, both modes have their limitations. Slow thinking generally leans to explore every possible reasoning path, which leads to heavy overthinking and wastes time. Quick thinking usually relies on pattern matching rather than truly understanding the query logic, which misses proper understanding. To address these issues, we propose FReM: Flexible Reasoning Mechanism, a method that adjusts reasoning depth according to the complexity of each question. Specifically, FReM leverages syntheti…

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   T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering

   Lei Wang, Yi Hu, Jiabang He et al. · 2023 · Computation and Language · AI summary available

   Abstract

   Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the redundant information involved or the essential information missed. To address these issues, we propose a nov…

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   Large Language Models on Graphs: A Comprehensive Survey

   Bowen Jin, Gang Liu, Chi Han et al. · 2023 · Computation and Language · AI summary available

   Abstract

   Large language models (LLMs), such as ChatGPT and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and newly found emergent capability (e.g., reasoning). While LLMs are mainly designed to process pure texts, there are many real-world scenarios where text data are associated with rich structure information in the form of graphs (e.g., academic networks, and e-commerce networks) or scenarios where graph data are paired with rich textual information (e.g., molecules with descriptions). Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalize…

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   Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models

   Linhao Luo, Zicheng Zhao, Chen Gong et al. · 2024 · Computation and Language · AI summary available

   Abstract

   Large language models (LLMs) have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address these issues, knowledge graphs (KGs) have been utilized to enhance LLM reasoning through their structured knowledge. However, existing KG-enhanced methods, either retrieval-based or agent-based, encounter difficulties in accurately retrieving knowledge and efficiently traversing KGs at scale. In this work, we introduce graph-constrained reasoning (GCR), a novel framework that bridges structured knowledge in KGs with unstructured reasoning in LLMs. To eliminate hallucinations, GCR ensures faithful KG-grounded reason…

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   Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs

   Travis Thompson, Seung-Hwan Lim, Paul Liu et al. · 2025 · Computation and Language · AI summary available

   Abstract

   Large Language Models (LLMs) have achieved impressive capabilities in language understanding and generation, yet they continue to underperform on knowledge-intensive reasoning tasks due to limited access to structured context and multi-hop information. Retrieval-Augmented Generation (RAG) partially mitigates this by grounding generation in retrieved context, but conventional RAG and GraphRAG methods often fail to capture relational structure across nodes in knowledge graphs. We introduce Inference-Scaled GraphRAG, a novel framework that enhances LLM-based graph reasoning by applying inference-time compute scaling. Our method combines sequential scaling with deep chain-of-thought graph traver…