Teaching Transformers Causal Reasoning through Axiomatic Training

• URL: http://arxiv.org/abs/2407.07612v2
• Date: Tue, 15 Apr 2025 08:43:28 GMT
• Title: Teaching Transformers Causal Reasoning through Axiomatic Training
• Abstract summary: We study to what extent a system can learn causal reasoning from symbolic demonstrations of causal axioms.<n>A key question is whether the system would learn to generalize from the axiom demonstrations to more complex scenarios.<n>To handle diverse text inputs, the same method is extended to finetune language models.
• Score: 33.77073807002369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since active interventions are costly, we study to what extent a system can learn causal reasoning from symbolic demonstrations of causal axioms. Specifically, we present an axiomatic training method where the system learns from multiple demonstrations of a causal axiom (or rule), rather than incorporating the axiom as an inductive bias or inferring it from data values. A key question is whether the system would learn to generalize from the axiom demonstrations to more complex scenarios. Our results, based on applying axiomatic training to learn the transitivity axiom and d-separation rule, indicate that such generalization is possible. To avoid data contamination issues, we start with a 67 million parameter transformer model and train it from scratch. On both tasks, we find that a model trained on linear causal chains (along with some noisy variations) can generalize well to complex graphs, including longer causal chains, causal chains with reversed order, and graphs with branching.To handle diverse text inputs, the same method is extended to finetune language models. Finetuning Llama-3.1 8B model on our axiomatic data leads to significant gains on causal benchmarks such as Corr2Cause and CLEAR, in some cases providing state-of-the-art performance surpassing GPT-4.

Related papers

• Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models [27.142703756752997]
  We introduce MathIF, a benchmark for evaluating instruction-following in mathematical reasoning tasks.<n>Our empirical analysis reveals a consistent tension between scaling up reasoning capacity and maintaining controllability.<n>We show that even simple interventions can partially recover obedience, though at the cost of reasoning performance.
  arXiv  Detail & Related papers  (2025-05-20T18:18:01Z)
• Do Larger Language Models Imply Better Reasoning? A Pretraining Scaling Law for Reasoning [89.17086632436363]
  We introduce a synthetic multihop reasoning environment designed to replicate the structure and distribution of real-world large-scale knowledge graphs. Our reasoning task involves completing missing edges in the graph, which requires advanced multi-hop reasoning and mimics real-world reasoning scenarios. To predict the optimal model size for a specific knowledge graph, we find an empirical scaling that linearly maps the knowledge graph search entropy to the optimal model size.
  arXiv  Detail & Related papers  (2025-04-04T17:57:22Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation [110.71955853831707]
  We view LMs as deriving new conclusions by aggregating indirect reasoning paths seen at pre-training time. We formalize the reasoning paths as random walk paths on the knowledge/reasoning graphs. Experiments and analysis on multiple KG and CoT datasets reveal the effect of training on random walk paths.
  arXiv  Detail & Related papers  (2024-02-05T18:25:51Z)
• Generalization on the Unseen, Logic Reasoning and Degree Curriculum [25.7378861650474]
  This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting. We study how different network architectures trained by (S)GD perform under GOTU. More specifically, this means an interpolator of the training data that has minimal Fourier mass on the higher degree basis elements.
  arXiv  Detail & Related papers  (2023-01-30T17:44:05Z)
• CLEAR: Generative Counterfactual Explanations on Graphs [60.30009215290265]
  We study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed. We propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models.
  arXiv  Detail & Related papers  (2022-10-16T04:35:32Z)
• Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought [10.524051272257614]
  Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts. We present a new synthetic question-answering dataset called PrOntoQA, where each example is generated as a synthetic world model. This allows us to parse the generated chain-of-thought into symbolic proofs for formal analysis.
  arXiv  Detail & Related papers  (2022-10-03T21:34:32Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)
• Do-calculus enables causal reasoning with latent variable models [2.294014185517203]
  Latent variable models (LVMs) are probabilistic models where some of the variables are hidden during training. We show that causal reasoning can enhance a broad class of LVM long established in the probabilistic modeling community.
  arXiv  Detail & Related papers  (2021-02-12T17:12:53Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge [96.92252296244233]
  Large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control. We show that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements. Our work paves a path towards open-domain systems that constantly improve by interacting with users who can instantly correct a model by adding simple natural language statements.
  arXiv  Detail & Related papers  (2020-06-11T17:02:20Z)

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.