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Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning

arXiv:2603.10377v1 Announce Type: new Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Causal Concept Graphs (CCG): a directed acyclic graph over sparse, interpretable latent features, where edges capture learned causal dependencies between concepts. We combine task-conditioned sparse autoencoders for concept discovery with DAGMA-style differentiable structure learning for graph recovery and introduce the Causal Fidelity Score (CFS) to evaluate whether graph-guided interventions induce larger downstream effects than random ones. On ARC-Challenge, StrategyQA, and LogiQA with GPT-2 Medium, across five seeds ($n{=}15$ paired runs), CCG achieves $\CFS=5.654\pm0.625$, outperforming ROME-style tracing ($3.382\pm0.233$), SAE-only ranking ($2.479\pm0.196$), and a random baseline ($1.032\pm0.034$), with $p<0.0001$ after Bonferroni correction. Learned graphs are sparse (5-6\% edge density), domai

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Md Muntaqim Meherab, Noor Islam S. Mohammad, Faiza Feroz · · 1 min read · 8 views
#cs.LG #cs.AI #stat.ME

arXiv:2603.10377v1 Announce Type: new Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Causal Concept Graphs (CCG): a directed acyclic graph over sparse, interpretable latent features, where edges capture learned causal dependencies between concepts. We combine task-conditioned sparse autoencoders for concept discovery with DAGMA-style differentiable structure learning for graph recovery and introduce the Causal Fidelity Score (CFS) to evaluate whether graph-guided interventions induce larger downstream effects than random ones. On ARC-Challenge, StrategyQA, and LogiQA with GPT-2 Medium, across five seeds ($n{=}15$ paired runs), CCG achieves $\CFS=5.654\pm0.625$, outperforming ROME-style tracing ($3.382\pm0.233$), SAE-only ranking ($2.479\pm0.196$), and a random baseline ($1.032\pm0.034$), with $p<0.0001$ after Bonferroni correction. Learned graphs are sparse (5-6\% edge density), domain-specific, and stable across seeds.

Executive Summary

This article proposes Causal Concept Graphs (CCG), a novel approach to visualize and analyze the causal relationships within the latent space of Large Language Models (LLMs). By combining sparse autoencoders with differentiable structure learning, CCG aims to provide a stepwise reasoning mechanism that outperforms existing methods. The authors report promising results on three benchmark datasets, with CCG achieving a significantly higher Causal Fidelity Score (CFS) than baseline methods. The learned graphs are found to be sparse, domain-specific, and stable across seeds. This research contributes to the growing field of explainable AI and has potential applications in various areas, including natural language processing, decision-making, and cognitive modeling.

Key Points

  • ▸ Causal Concept Graphs (CCG) is a novel approach to visualize and analyze causal relationships within LLM latent space
  • ▸ CCG combines sparse autoencoders with differentiable structure learning for stepwise reasoning
  • ▸ Promising results on three benchmark datasets: ARC-Challenge, StrategyQA, and LogiQA with GPT-2 Medium

Merits

Strength in Explainability

CCG provides a visual representation of causal relationships within LLMs, enhancing explainability and interpretability of complex models

Improved Reasoning Mechanism

CCG's stepwise reasoning mechanism outperforms existing methods, demonstrating its potential in various applications

Interpretable Latent Features

CCG's sparse, interpretable latent features facilitate understanding of complex models and enable more informed decision-making

Demerits

Limited Generalizability

CCG's performance may be dataset-specific and may not generalize to other domains or tasks

Computational Complexity

CCG's differentiable structure learning requires significant computational resources, potentially limiting its scalability

Lack of Theoretical Guarantees

CCG's performance is empirically evaluated, but theoretical guarantees for its effectiveness are lacking

Expert Commentary

The proposed Causal Concept Graphs (CCG) approach demonstrates a novel and innovative way to analyze and visualize causal relationships within LLMs. The results presented in this article are promising, and the method shows potential for improving explainability and interpretability in complex models. However, the limitations of CCG, such as limited generalizability and computational complexity, need to be addressed. Furthermore, the lack of theoretical guarantees for CCG's effectiveness necessitates further research. Nevertheless, this article contributes significantly to the growing field of Explainable AI and has far-reaching implications for various applications.

Recommendations

  • ✓ Future research should focus on addressing the limitations of CCG and exploring its generalizability to other domains and tasks
  • ✓ Developing theoretical guarantees for CCG's effectiveness would further solidify its position as a valuable approach in Explainable AI

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