Causal Graph based Event Reasoning using Semantic Relation Experts (2506.06910v1)

Abstract: Understanding how events in a scenario causally connect with each other is important for effectively modeling and reasoning about events. But event reasoning remains a difficult challenge, and despite recent advances, LLMs still struggle to accurately identify causal connections between events. This struggle leads to poor performance on deeper reasoning tasks like event forecasting and timeline understanding. To address this challenge, we investigate the generation of causal event graphs (e.g., A enables B) as a parallel mechanism to help LLMs explicitly represent causality during inference. This paper evaluates both how to generate correct graphs as well as how graphs can assist reasoning. We propose a collaborative approach to causal graph generation where we use LLMs to simulate experts that focus on specific semantic relations. The experts engage in multiple rounds of discussions which are then consolidated by a final expert. Then, to demonstrate the utility of causal graphs, we use them on multiple downstream applications, and also introduce a new explainable event prediction task that requires a causal chain of events in the explanation. These explanations are more informative and coherent than baseline generations. Finally, our overall approach not finetuned on any downstream task, achieves competitive results with state-of-the-art models on both forecasting and next event prediction tasks.

• The paper introduces a collaborative framework of semantic relation experts to construct causal graphs that model event dependencies with high accuracy.
• It leverages LLM-based agents—temporal, discourse, precondition, and commonsense experts—to outperform baseline methods on causal reasoning benchmarks.
• The approach improves explainable event likelihood prediction and forecasting by generating interpretable causal chains that aid decision-making.

Semantic Relation Experts for Causal Graph-Based Event Reasoning

The paper entitled "Semantic Relation Experts for Causal Graph-Based Event Reasoning" addresses a key challenge in NLP: the identification and modeling of causal connections between events, which are crucial for effective event reasoning. Recent advancements in LLMs have improved event reasoning capabilities but still face significant obstacles in accurately pinpointing causal relationships between real-life events, particularly within the news domain. This paper proposes an innovative approach to enhance causal reasoning through the generation of causal event graphs using a set of collaborative agents focused on specific semantic relations.

Methodology

The proposed framework involves utilizing LLMs to simulate various expert agents, each concentrating on a different semantic aspect relevant to causality:

1. Temporal Expert: Focuses on the chronological order of events since a prerequisite for causality is that the cause precedes the effect.
2. Discourse Expert: Examines shared entities within events as potential indicators of causal links.
3. Precondition Expert: Considers whether one event is a necessary precondition for another, evaluating scenarios where removing one event could negate the occurrence of another.
4. Commonsense Expert: Aims to identify implicit knowledge or unstated information that could mediate causal relationships between events.

These semantic relation experts engage in multiple rounds of discussions to refine their causal assessments, and a final expert acts as a judge to consolidate these findings into a coherent causal graph. The intrinsic evaluation results reveal that this collaborative approach significantly enhances the accuracy of causal graph generation compared to direct or pairwise LLM-based methods without collaboration.

Results and Implications

The paper presents a comprehensive intrinsic evaluation using the Causal Reasoning Assessment Benchmark (CRAB) dataset, demonstrating that the collaborative approach with semantic relation experts achieves higher balanced accuracy (BAcc) and Macro F1 scores compared to baseline methods. This implementation provides more accurate causal graphs due to the diverse perspectives each agent contributes and the iterative communication process that refines these connections.

The extrinsic evaluation focuses on applying these causal graphs to practical tasks in event reasoning, such as explainable event likelihood prediction, event forecasting, and next event prediction. Introducing a novel Explainable Event Likelihood Prediction (EELP) task, the paper demonstrates how causal graphs can generate more meaningful explanations of event likelihood, outperforming baseline systems in causality, coherence, and informativeness metrics.

Moreover, the causal graph-based approach achieves competitive results with state-of-the-art models on forecasting tasks, emphasizing its utility not just in prediction but in providing inherent explanations. This method allows for the generation of causal chains that elucidate predicted outcomes, enhancing both transparency and interpretability in decision-making processes involving real-world events.

Future Directions

The research posits promising avenues for employing causal graphs in further AI-developments. These include refining the collaborative method to reduce computational costs associated with multiple expert communications, exploring the integration of additional semantic relation experts, and enhancing causal inference techniques to accommodate more nuanced event representations. Additionally, expanding the dataset to incorporate diverse real-world scenarios beyond the news domain could strengthen the model's applicability across various fields in NLP and artificial intelligence.

In conclusion, the framework introduced in this paper marks a significant stride in causal reasoning, moving beyond correlative event modeling to incorporate nuanced event dependencies that improve understanding and prediction capabilities. By leveraging the expertise of specialized agents within a collaborative framework, this research paves the way for more robust and interpretable event reasoning systems in AI.