Mitigating Prior Errors in Causal Structure Learning: Towards LLM driven Prior Knowledge

• URL: http://arxiv.org/abs/2306.07032v1
• Date: Mon, 12 Jun 2023 11:24:48 GMT
• Title: Mitigating Prior Errors in Causal Structure Learning: Towards LLM driven Prior Knowledge
• Authors: Lyuzhou Chen, Taiyu Ban, Xiangyu Wang, Derui Lyu, Huanhuan Chen
• Abstract summary: We aim to tackle erroneous prior causal statements from Large Language Models (LLM) As a pioneer attempt, we propose a BN learning strategy resilient to prior errors without need of human intervention. Specifically, we highlight its substantial ability to resist order-reversed errors while maintaining the majority of correct prior knowledge.
• Score: 17.634793921251777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal structure learning, a prominent technique for encoding cause and effect relationships among variables, through Bayesian Networks (BNs). Merely recovering causal structures from real-world observed data lacks precision, while the development of Large Language Models (LLM) is opening a new frontier of causality. LLM presents strong capability in discovering causal relationships between variables with the "text" inputs defining the investigated variables, leading to a potential new hierarchy and new ladder of causality. We aim an critical issue in the emerging topic of LLM based causal structure learning, to tackle erroneous prior causal statements from LLM, which is seldom considered in the current context of expert dominating prior resources. As a pioneer attempt, we propose a BN learning strategy resilient to prior errors without need of human intervention. Focusing on the edge-level prior, we classify the possible prior errors into three types: order-consistent, order-reversed, and irrelevant, and provide their theoretical impact on the Structural Hamming Distance (SHD) under the presumption of sufficient data. Intriguingly, we discover and prove that only the order-reversed error contributes to an increase in a unique acyclic closed structure, defined as a "quasi-circle". Leveraging this insight, a post-hoc strategy is employed to identify the order-reversed prior error by its impact on the increment of "quasi-circles". Through empirical evaluation on both real and synthetic datasets, we demonstrate our strategy's robustness against prior errors. Specifically, we highlight its substantial ability to resist order-reversed errors while maintaining the majority of correct prior knowledge.

Related papers

• LLM-initialized Differentiable Causal Discovery [0.0]
  Differentiable causal discovery (DCD) methods are effective in uncovering causal relationships from observational data. However, these approaches often suffer from limited interpretability and face challenges in incorporating domain-specific prior knowledge. We propose Large Language Models (LLMs)-based causal discovery approaches that provide useful priors but struggle with formal causal reasoning.
  arXiv  Detail & Related papers  (2024-10-28T15:43:31Z)
• Metacognitive Myopia in Large Language Models [0.0]
  Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally inherent stereotypes, cloud moral judgments, or amplify positive evaluations of majority groups. We propose metacognitive myopia as a cognitive-ecological framework that can account for a conglomerate of established and emerging LLM biases. Our theoretical framework posits that a lack of the two components of metacognition, monitoring and control, causes five symptoms of metacognitive myopia in LLMs.
  arXiv  Detail & Related papers  (2024-08-10T14:43:57Z)
• Language Model Cascades: Token-level uncertainty and beyond [65.38515344964647]
  Recent advances in language models (LMs) have led to significant improvements in quality on complex NLP tasks. Cascading offers a simple strategy to achieve more favorable cost-quality tradeoffs. We show that incorporating token-level uncertainty through learned post-hoc deferral rules can significantly outperform simple aggregation strategies.
  arXiv  Detail & Related papers  (2024-04-15T21:02:48Z)
• CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs [27.362012903540492]
  The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning. The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning.
  arXiv  Detail & Related papers  (2024-04-09T14:40:08Z)
• Cause and Effect: Can Large Language Models Truly Understand Causality? [1.2334534968968969]
  This research proposes a novel architecture called Context Aware Reasoning Enhancement with Counterfactual Analysis(CARE CA) framework. The proposed framework incorporates an explicit causal detection module with ConceptNet and counterfactual statements, as well as implicit causal detection through Large Language Models. The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
  arXiv  Detail & Related papers  (2024-02-28T08:02:14Z)
• Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic [51.967603572656266]
  We introduce a consistent and theoretically grounded approach to annotating decompositional entailment. We find that our new dataset, RDTE, has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality.
  arXiv  Detail & Related papers  (2024-02-22T18:55:17Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• DeepEdit: Knowledge Editing as Decoding with Constraints [118.78008395850888]
  How to edit the knowledge in multi-step reasoning has become the major challenge in the knowledge editing (KE) of large language models (LLMs) We propose a new KE framework: DEEPEDIT, which enhances LLMs's ability to generate coherent reasoning chains with new knowledge through depth-first search. In addition to DEEPEDIT, we propose two new KE benchmarks: MQUAKE-2002 and MQUAKE-HARD, which provide more precise and challenging assessments of KE approaches.
  arXiv  Detail & Related papers  (2024-01-19T03:48:27Z)
• From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data [19.264745484010106]
  Large Language Models (LLMs) exhibit exceptional abilities for causal analysis between concepts in numerous societally impactful domains. Recent research on LLM performance in various causal discovery and inference tasks has given rise to a new ladder in the classical three-stage framework of causality. We propose a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning.
  arXiv  Detail & Related papers  (2023-06-29T12:48:00Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• CASTLE: Regularization via Auxiliary Causal Graph Discovery [89.74800176981842]
  We introduce Causal Structure Learning (CASTLE) regularization and propose to regularize a neural network by jointly learning the causal relationships between variables. CASTLE efficiently reconstructs only the features in the causal DAG that have a causal neighbor, whereas reconstruction-based regularizers suboptimally reconstruct all input features.
  arXiv  Detail & Related papers  (2020-09-28T09:49:38Z)

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.