Retrieval Augmented Generation based Large Language Models for Causality Mining

• URL: http://arxiv.org/abs/2505.23944v1
• Date: Thu, 29 May 2025 18:51:00 GMT
• Title: Retrieval Augmented Generation based Large Language Models for Causality Mining
• Authors: Thushara Manjari Naduvilakandy, Hyeju Jang, Mohammad Al Hasan,
• Abstract summary: Causality detection and mining are important tasks in information retrieval.<n>We present several retrieval-augmented generation (RAG) based dynamic prompting schemes to enhance causality detection and extraction tasks.
• Score: 3.711112570761809
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causality detection and mining are important tasks in information retrieval due to their enormous use in information extraction, and knowledge graph construction. To solve these tasks, in existing literature there exist several solutions -- both unsupervised and supervised. However, the unsupervised methods suffer from poor performance and they often require significant human intervention for causal rule selection, leading to poor generalization across different domains. On the other hand, supervised methods suffer from the lack of large training datasets. Recently, large language models (LLMs) with effective prompt engineering are found to be effective to overcome the issue of unavailability of large training dataset. Yet, in existing literature, there does not exist comprehensive works on causality detection and mining using LLM prompting. In this paper, we present several retrieval-augmented generation (RAG) based dynamic prompting schemes to enhance LLM performance in causality detection and extraction tasks. Extensive experiments over three datasets and five LLMs validate the superiority of our proposed RAG-based dynamic prompting over other static prompting schemes.

Related papers

• DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning [4.817888539036794]
  DynaSearcher is an innovative search agent enhanced by dynamic knowledge graphs and multi-reward reinforcement learning (RL)<n>We employ a multi-reward RL framework for fine-grained control over training objectives such as retrieval accuracy, efficiency, and response quality.<n> Experimental results demonstrate that our approach achieves state-of-the-art answer accuracy on six multi-hop question answering datasets.
  arXiv  Detail & Related papers  (2025-07-23T09:58:31Z)
• mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation [5.647319807077936]
  Large Vision-Language Models (LVLMs) have made remarkable strides in multimodal tasks such as visual question answering, visual grounding, and complex reasoning.<n>Retrieval-Augmented Generation (RAG) offers a practical solution to mitigate these challenges by allowing the LVLMs to access large-scale knowledge databases via retrieval mechanisms.
  arXiv  Detail & Related papers  (2025-05-29T23:32:03Z)
• Does Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs [58.24692529185971]
  We introduce a comprehensive auditing framework for unlearning evaluation comprising three benchmark datasets, six unlearning algorithms, and five prompt-based auditing methods.<n>We evaluate the effectiveness and robustness of different unlearning strategies.
  arXiv  Detail & Related papers  (2025-05-29T09:19:07Z)
• Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation [19.48826538310603]
  We introduce LVLM to Policy (LVLM2P), a framework that distills knowledge from large vision-language models (LVLM) into more efficientReinforcement Learning agents.<n>Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent.<n>We show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms.
  arXiv  Detail & Related papers  (2025-05-16T13:15:54Z)
• Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning [61.99353167168545]
  We show that fine-tuning with LLM-generated data improves target task performance and reduces non-target task degradation.<n>This is the first work to provide an empirical explanation based on token perplexity reduction to mitigate catastrophic forgetting in LLMs after fine-tuning.
  arXiv  Detail & Related papers  (2025-01-24T08:18:56Z)
• AD-LLM: Benchmarking Large Language Models for Anomaly Detection [50.57641458208208]
  This paper introduces AD-LLM, the first benchmark that evaluates how large language models can help with anomaly detection.<n>We examine three key tasks: zero-shot detection, using LLMs' pre-trained knowledge to perform AD without tasks-specific training; data augmentation, generating synthetic data and category descriptions to improve AD models; and model selection, using LLMs to suggest unsupervised AD models.
  arXiv  Detail & Related papers  (2024-12-15T10:22:14Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.<n>We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.<n>Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• A Survey on Efficient Inference for Large Language Models [25.572035747669275]
  Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. The substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
  arXiv  Detail & Related papers  (2024-04-22T15:53:08Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.