Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods focus on semantic similarity between queries and candidate exemplars, while logical connections between reasoning steps can be beneficial to depict problem-solving process. |
| Approach: | They propose a method to retrieve exemplars with semantic and structural similarity using a graph kernel. |
| Outcome: | The proposed method is superior to state-of-the-art retrieval-based approaches on mathematics and logical reasoning tasks. |
Similar Papers
GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)
Copied to clipboard
Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui
| Challenge: | Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings. |
| Approach: | They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph . |
| Outcome: | The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge. |
Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing retrieval methods divide reference documents into passages, treating them in isolation. Existing methods only use contiguous passages or keywords. |
| Approach: | They propose a retrieval method that leverages graph neural networks to exploit relatedness between passages to enhance retrieval. |
| Outcome: | The proposed method improves retrieval by exploiting the relatedness between passages. |
STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for incontext learning often overlook structural alignment, leading to poor generalization and suboptimal performance. |
| Approach: | They propose a two-stage exemplar selection strategy that achieves a strong balance between efficiency, generalizability and performance. |
| Outcome: | The proposed method outperforms baselines on semantic parsing tasks on four benchmarks. |
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning (2025.findings-acl)
Copied to clipboard
Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Yang Li, Chen Luo, Shuiwang Ji, Qi He, Jiliang Tang
| Challenge: | Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks, however, they still face challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. |
| Approach: | They propose to construct explicit graphs from context and leverage them to enhance LLM reasoning performance on reasoning tasks. |
| Outcome: | Extensive experiments show that the proposed method improves both logical reasoning and multi-hop question answering tasks. |
GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to retrieval-augmented generation (RAG) rely on costly LLM calls to generate relation paths or traverse the KG. |
| Approach: | They propose a framework that uses lightweight Graph Neural Networks to enhance retrieval. |
| Outcome: | The proposed framework outperforms existing methods on multi-hop and multi-entity questions. |
Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. |
| Approach: | They propose a novel approach that leverages In-Context Learning to integrate graph data and task-specific information into large language models (LLMs) they employ a Graph Neural Network-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. |
| Outcome: | Experiments on three tasks and seven LLMs show that AskGNN performs better than existing methods. |
An End-to-End Submodular Framework for Data-Efficient In-Context Learning (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances in natural language tasks leverage the emergent In-Context Learning ability of pretrained Large Language Models (LLMs). |
| Approach: | They propose a framework for exemplar selection for in-context learning that uses a pool-based active learning approach to select Diverse and informative exemplars from the target tasks’ unlabeled pool. |
| Outcome: | The proposed framework outperforms existing methods for data annotation and similarity-based methods for test query-specific exemplar retrieval on 7 different NLP datasets and 5 LLMs of varying complexities. |
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning (2024.emnlp-main)
Copied to clipboard
Kiran Purohit, Venktesh V, Raghuram Devalla, Krishna Yerragorla, Sourangshu Bhattacharya, Avishek Anand
| Challenge: | Recent advances in large language models (LLMs) have enabled in-context learning (ICL) a critical challenge in ICL is the selection of optimal exemplars . |
| Approach: | They propose an algorithm for static exemplar subset selection for reasoning tasks . they propose a method that estimates parameters without incorporating confidence information . |
| Outcome: | The proposed method significantly reduces the number of LLM calls to 11% of those required by state-of-the-art methods and achieves a substantial performance improvement of 12.24%. |
Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)
Copied to clipboard
Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang
| Challenge: | Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency. |
| Approach: | They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them. |
| Outcome: | The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens. |
Empowering GraphRAG with Knowledge Filtering and Integration (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models suffer from knowledge gaps and hallucinations, resulting in incorrect or poor reasoning. |
| Approach: | They propose Graph retrieval-augmented generation (GraphRAG) which integrates structured knowledge from external graphs to enhance model's reasoning. |
| Outcome: | Experiments on knowledge graph QA tasks show that GraphRAG significantly improves reasoning performance across multiple backbone models. |