Papers Commonsense Causal Reasoning

CounterBench: A Benchmark for Counterfactuals Reasoning in Large Language Models

Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this paper, we evaluate the performance of large language models (LLMs) in co…

COLD: Causal reasOning in cLosed Daily activities

Large Language Models (LLMs) have shown state-of-the-art performance in a variety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of LLMs, causal reasoning has become a relia…

CLadder: Assessing Causal Reasoning in Language Models

The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason about causality. Much of the existing w…

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, explo…

Knowledge-Augmented Language Models for Cause-Effect Relation Classification

Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream tasks. In this work, we investigate the au…

Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification

Commonsense knowledge can be leveraged for identifying causal relations in text. In this work, we convert triples in ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, to natural language text and continu…

HeadlineCause: A Dataset of News Headlines for Detecting Causalities

Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work,…

Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models

Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this p…

BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian

In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-spe…

BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian

In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-spe…

Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation

Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to train a large pretrained language model on…

Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning

SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning