Papers with knowledge integration
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering (D19-60)
Copied to clipboard
| Challenge: | Recent approaches on non-extractive commonsense QA show increased performance . attention-based injection seems to be preferable for knowledge integration . |
| Approach: | They propose to use attention-based injection to integrate knowledge into commonsense QA models. |
| Outcome: | The proposed methods show that attention-based injection is preferable for knowledge integration, and that the degree of domain overlap plays a crucial role in determining model success. |
KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education? (2025.acl-short)
Copied to clipboard
| Challenge: | Existing knowledge discrepancies between textbooks and large language models can undermine RAG systems' performance. |
| Approach: | They propose to use a dataset to test RAG system robustness against knowledge discrepancies. |
| Outcome: | The proposed dataset shows that RAG systems suffer performance degradation when faced with knowledge discrepancies. |
KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)
Copied to clipboard
Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu
| Challenge: | Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) . |
| Approach: | They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). |
| Outcome: | The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. |
Verifiable Parameterization of Bayesian Networks from Scientific Literature: Unlocking Unstructured Empirical Evidence (2026.findings-acl)
Copied to clipboard
| Challenge: | Current methods to learn conditional probabilities from raw tabular data are limited due to privacy concerns or general lack of access to data. |
| Approach: | They propose to reconstruct local conditional probability tables solely from statistical summaries to parameterize Bayesian Networks. |
| Outcome: | The proposed methods outperform baseline methods while ensuring transparency and verifiability. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
Copied to clipboard
| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
Knowledge Enhanced Reflection Generation for Counseling Dialogues (2022.acl-long)
Copied to clipboard
| Challenge: | Using retrieval and generative methods, we generate responses using commonsense and domain knowledge. |
| Approach: | They propose a pipeline that collects domain knowledge through web mining and a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
| Outcome: | The proposed pipeline collects domain knowledge through web mining and incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
Copied to clipboard
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | End-to-end speech-to speech (S2S) dialogue systems face key challenges in incorporating external knowledge into their models. |
| Approach: | They propose a framework that directly retrieves relevant textual knowledge from speech queries. |
| Outcome: | The proposed framework improves the performance of end-to-end speech-tospeech dialogue systems while achieving higher retrieval efficiency. |
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Emotion Recognition in Conversation models neglect direct utterance-knowledge interaction and use emotion-indirect auxiliary tasks to augment semantic information. |
| Approach: | They propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning which leverages both commonsense knowledge and sentiment lexicon to augment semantic information. |
| Outcome: | The proposed model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset. |
CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games (2025.acl-long)
Copied to clipboard
| Challenge: | Metaphors are crucial for humans to express complex or subtle ideas by comparing one concept to another, often from a different domain. |
| Approach: | They propose a framework that enables LLMs to engage in metaphor processing by combining hypothesis-based metaphor reasoner and metaphor generator. |
| Outcome: | The proposed framework enhances agents' ability to interpret and apply metaphors in language games. |
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for enhancing response accuracy and accuracy struggle with open-domain QA tasks because they perform independent retrieval operations without maintaining a summarizing memory or using adaptive retrieval strategies. |
| Approach: | They propose a method that integrates non-parametric knowledge from external knowledge bases into models to enhance response accuracy while mitigating factual errors and hallucinations. |
| Outcome: | The proposed method improves on open-domain QA datasets and reduces noise and hallucinations due to redundant information and insufficient information integration. |
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining (2021.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge-based PLMs are based on linked-entity information, but they only use linked-enemy information as auxiliary information. |
| Approach: | They propose to integrate semantic knowledge from neighbours of linked-entity into a medical PLM that integrates heterogeneous-entities into the homogeneously neighbouring entity structure. |
| Outcome: | Experiments show that SMedBERT outperforms baselines in knowledge-intensive Chinese medical tasks. |
Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have exploited data augmentation or implemented specialised loss functions to alleviate the inconsistent behaviour issue, but they consume expensive training resources and can only handle a certain consistency type. |
| Approach: | They propose a method that allows PLMs to capture accurate meaning by learning precise interrelationships between concepts from word-definition pairs in a dictionary. |
| Outcome: | The proposed method can improve multiple types of consistency and integrate pre-trained knowledge with PLMs’ pre-training knowledge. |
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources. |
| Approach: | They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question. |
| Outcome: | The proposed framework outperforms SOTA methods on complex QA datasets. |
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning. |
| Approach: | They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
| Outcome: | The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
MedCPI: A Construct–Personalize–Integrate Framework for KG-enhanced Clinical Prediction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing KG-enhanced approaches to clinical prediction are limited . existing approaches to personalize and integrate knowledge are weakly controlled . |
| Approach: | They propose a framework to integrate medical knowledge graphs into EHRs to support KG-enhanced clinical prediction. |
| Outcome: | The proposed framework improves on MIMIC-III and MIMIC IV tasks. |
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning (2025.emnlp-main)
Copied to clipboard
Yu Wang, Shiwan Zhao, Zhihu Wang, Ming Fan, Xicheng Zhang, Yubo Zhang, Zhengfan Wang, Heyuan Huang, Ting Liu
| Challenge: | Existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between retrieved facts and task-specific reasoning. |
| Approach: | They introduce a module extension that integrates application-aware reasoning into the RAG pipeline. |
| Outcome: | Experiments show that RAG+ outperforms standard RAG variants and achieves gains of 3–5% in complex scenarios. |
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies either overlook temporal shifts or hardly capture rich shifting patterns of both semantic and knowledge. |
| Approach: | They develop a temporal adaptive learning framework that captures temporal shifts . they use medical ontology and other knowledge sources to integrate temporal adaptation . |
| Outcome: | The proposed framework improves classification tasks across multiple domains and domains with knowledge integration. |
ConvX: A Lightweight Converter to Bridge Indexed Dense Representations and Large Language Models for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing RAG pipelines suffer from critical efficiency limitations due to their complexity and complexity. |
| Approach: | They propose a compression-based RAG framework that directly leverages indexed dense representations produced by a retriever, substituting to long text contexts. |
| Outcome: | Empirical results show that the proposed model achieves competitive performances compared to the state-of-the-art model that uses a large ad-hoc context compressor while offering substantially improved inference efficiency. |