mlsanityData to 7 Oct 2026

FS-RAG: A Frame Semantics Based Approach for Improved Factual Accuracy in Large Language Models

arXiv 2406.16167 ↗First posted 23 Jun 2024cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

We present a novel extension to Retrieval Augmented Generation with the goal of mitigating factual inaccuracies in the output of large language models. Specifically, our method draws on the cognitive linguistic theory of frame semantics for the indexing and retrieval of factual information relevant to helping large language models answer queries. We conduct experiments to demonstrate the effectiveness of this method both in terms of retrieval effectiveness and in terms of the relevance of the frames and frame relations automatically generated. Our results show that this novel mechanism of Frame Semantic-based retrieval, designed to improve Retrieval Augmented Generation (FS-RAG), is effective and offers potential for providing data-driven insights into frame semantics theory. We provide open access to our program code and prompts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 18 references that are in the map. First posted on arXiv 23 Jun 2024.

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12 of 18 references.

References · 12

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  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
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  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
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  10. 2021Explaining Answers with Entailment Treescited by 148
  11. 2023Are Emergent Abilities in Large Language Models just In-Context Learning?cited by 109
  12. 2022Is a Question Decomposition Unit All We Need?cited by 37

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  2. 2026Structure Guided Retrieval-Augmented Generation for Factual Queries
  3. 2025TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs
  4. 2024Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
  5. 2024From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
  6. 2024Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
  7. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  8. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey