Bridging Causal Discovery and Large Language Models: A Comprehensive
Survey of Integrative Approaches and Future Directions
- URL: http://arxiv.org/abs/2402.11068v1
- Date: Fri, 16 Feb 2024 20:48:53 GMT
- Title: Bridging Causal Discovery and Large Language Models: A Comprehensive
Survey of Integrative Approaches and Future Directions
- Authors: Guangya Wan, Yuqi Wu, Mengxuan Hu, Zhixuan Chu, Sheng Li
- Abstract summary: Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence.
This paper presents a comprehensive survey of the integration of LLMs, such as GPT4, into CD tasks.
- Score: 10.226735765284852
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Causal discovery (CD) and Large Language Models (LLMs) represent two emerging
fields of study with significant implications for artificial intelligence.
Despite their distinct origins, CD focuses on uncovering cause-effect
relationships from data, and LLMs on processing and generating humanlike text,
the convergence of these domains offers novel insights and methodologies for
understanding complex systems. This paper presents a comprehensive survey of
the integration of LLMs, such as GPT4, into CD tasks. We systematically review
and compare existing approaches that leverage LLMs for various CD tasks and
highlight their innovative use of metadata and natural language to infer causal
structures. Our analysis reveals the strengths and potential of LLMs in both
enhancing traditional CD methods and as an imperfect expert, alongside the
challenges and limitations inherent in current practices. Furthermore, we
identify gaps in the literature and propose future research directions aimed at
harnessing the full potential of LLMs in causality research. To our knowledge,
this is the first survey to offer a unified and detailed examination of the
synergy between LLMs and CD, setting the stage for future advancements in the
field.
Related papers
- A Survey on Large Language Models with some Insights on their Capabilities and Limitations [0.3222802562733786]
Large Language Models (LLMs) exhibit remarkable performance across various language-related tasks.
LLMs have demonstrated emergent abilities extending beyond their core functions.
This paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities.
arXiv Detail & Related papers (2025-01-03T21:04:49Z) - Causality for Large Language Models [37.10970529459278]
Large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks.
Recent research highlights that LLMs function as causal parrots, capable of reciting causal knowledge without truly understanding or applying it.
This survey aims to explore how causality can enhance LLMs at every stage of their lifecycle.
arXiv Detail & Related papers (2024-10-20T07:22:23Z) - From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems.
The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness.
This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
arXiv Detail & Related papers (2024-09-19T02:51:54Z) - LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges.
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model.
This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
arXiv Detail & Related papers (2024-02-26T07:33:05Z) - Discovery of the Hidden World with Large Language Models [95.58823685009727]
This paper presents Causal representatiOn AssistanT (COAT) that introduces large language models (LLMs) to bridge the gap.
LLMs are trained on massive observations of the world and have demonstrated great capability in extracting key information from unstructured data.
COAT also adopts CDs to find causal relations among the identified variables as well as to provide feedback to LLMs to iteratively refine the proposed factors.
arXiv Detail & Related papers (2024-02-06T12:18:54Z) - Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored.
We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
arXiv Detail & Related papers (2023-12-26T07:24:46Z) - Survey on Factuality in Large Language Models: Knowledge, Retrieval and
Domain-Specificity [61.54815512469125]
This survey addresses the crucial issue of factuality in Large Language Models (LLMs)
As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
arXiv Detail & Related papers (2023-10-11T14:18:03Z) - Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well.
Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries.
We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
arXiv Detail & Related papers (2023-07-20T16:46:10Z) - 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) - Causal Reasoning and Large Language Models: Opening a New Frontier for Causality [29.433401785920065]
Large language models (LLMs) can generate causal arguments with high probability.
LLMs may be used by human domain experts to save effort in setting up a causal analysis.
arXiv Detail & Related papers (2023-04-28T19:00:43Z)
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.