Gradually Excavating External Knowledge for Implicit Complex Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have gained attention for their human-comparable capabilities but they may not solve open-domain implicit questions due to out-of-date domain knowledge, one-shot generation and restricted comprehensiveness. |
| Approach: | They propose a gradual knowledge excavation framework for open-domain complex question answering using extrinsic knowledge and historical knowledge. |
| Outcome: | The proposed framework achieves 78.17% accuracy with less than 6% parameters of its competitors, setting new SOTA in the 10B LLM class. |
Similar Papers
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
Copied to clipboard
| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
FlexiQA: Leveraging LLM’s Evaluation Capabilities for Flexible Knowledge Selection in Open-domain Question Answering (2024.findings-eacl)
Copied to clipboard
| Challenge: | Current methods for open-domain question answering lacks the hallucination and relevance of acquired knowledge to the given question. |
| Approach: | They propose a new pipeline that utilizes the diverse evaluation capabilities of large language models to select knowledge effectively and flexibly. |
| Outcome: | The proposed pipeline combines the strengths of both paradigms and overcomes their shortcomings. |
LLMs on interactive feature collections with implicit dynamic decision strategy (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) struggle to efficiently narrow down the search space . external engineered systems may not fully utilize the inherent problem-solving capabilities of LLMs . |
| Approach: | They propose to implicitly guide Large Language Models to enhance their interactive feature collection abilities within a single prompt. |
| Outcome: | The proposed approach improves the performance of large language models in real-world scenarios. |
Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. |
| Approach: | They propose a framework that internalizes domain knowledge through internal-external knowledge self-selection and selective supervised fine-tuning. |
| Outcome: | The proposed framework outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)
Copied to clipboard
Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge. |
| Approach: | They propose a framework to train large language models to leverage both internal and external knowledge sources. |
| Outcome: | The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning. |
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
Copied to clipboard
Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
| Challenge: | Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question . |
| Approach: | They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance . |
| Outcome: | The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered . |
Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process. |
| Approach: | They propose a framework that employs learned rules to guide the generation of logical forms. |
| Outcome: | The proposed method achieves competitive results on standard KBQA datasets. |
How Much Knowledge Can You Pack Into the Parameters of a Language Model? (2020.emnlp-main)
Copied to clipboard
| Challenge: | In this paper, we show that large neural language models trained on unstructured text can attain competitive results on open-domain question answering benchmarks without access to external knowledge. |
| Approach: | They propose to fine-tune pre-trained neural language models to answer questions without external knowledge . they show that this approach scales with model size and performs competitively . |
| Outcome: | The proposed approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. |
Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
Copied to clipboard
| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
| Approach: | They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge. |
| Outcome: | The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. |