Abductive Commonsense Reasoning (1908.05739v2)

Abstract: Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language (Hobbs et al., 1988), there has been relatively little research in support of abductive natural language inference and generation. We present the first study that investigates the viability of language-based abductive reasoning. We introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations. Based on this dataset, we conceptualize two new tasks -- (i) Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and (ii) Abductive NLG: a conditional generation task for explaining given observations in natural language. On Abductive NLI, the best model achieves 68.9% accuracy, well below human performance of 91.4%. On Abductive NLG, the current best language generators struggle even more, as they lack reasoning capabilities that are trivial for humans. Our analysis leads to new insights into the types of reasoning that deep pre-trained LLMs fail to perform--despite their strong performance on the related but more narrowly defined task of entailment NLI--pointing to interesting avenues for future research.

Analysis of Abductive Commonsense Reasoning in LLMs

The presented paper details research that explores the potential for language-based abductive reasoning, specifically within the scope of NLP. This paper addresses a significant gap in existing NLP research by focusing on abductive reasoning—a method of inference aimed at identifying the most plausible explanation for a given set of observations, a process central to human commonsense understanding and narrative interpretation.

Key Contributions

The research introduces two new tasks designed to assess systems on abductive reasoning: Abductive Natural Language Inference (ANLI) and Abductive Natural Language Generation (ANLG). For the ANLI task, models choose between multiple hypotheses to identify which explanation best fits the provided narrative context. In contrast, the ANLG task challenges models to generate plausible explanations suited for given observations.

Central to this work is a novel dataset, consisting of approximately 20,000 narrative contexts and 200,000 hypotheses. This dataset provides a benchmark for evaluating AI's ability to perform abductive reasoning in written narratives, where traditional deductive or inductive reasoning may fall short.

Experimental Findings

Several experiments were conducted using state-of-the-art models such as BERT and GPT. Results indicate that while these models achieve moderate success, with the best performance resulting in 68.9% accuracy on ANLI tasks and considerably lower scores on ANLG tasks, there is still a marked gap compared to human-level performance, which averages 91.4% accuracy on similar tasks.

The models’ struggles in ANLG tasks, achieving only about 45% human acceptance in generations, highlight the difficulty of replicating human commonsense reasoning in AI. This reveals a deficiency in the models' capability to generate coherent and commonsensical hypotheses akin to human-written narratives.

Implications and Future Directions

The research holds substantial implications for both practical applications and theoretical advancements in AI. Practically, enhanced abductive reasoning could improve AI's ability to engage in more human-like narrative interpretations, benefiting applications in storytelling AI, autonomous agents, and interactive user interfaces. Theoretically, this paper suggests future avenues in improving AI reasoning through more robust models capable of intricate cognitive functions like abduction.

For future research, focusing on integrating a richer form of commonsense reasoning into model architectures could prove beneficial. Enhancements may involve incorporating external commonsense knowledge bases or developing novel machine learning solutions that allow models to better infer intricate associations that mimic human abductive reasoning processes.

Conclusion

In conclusion, this paper makes a significant step towards addressing the shortcomings of current LLMs in performing abductive reasoning. By focusing on a less-explored area within AI and NLP research, this paper lays foundational work for the development of more cognitively advanced AI systems. The tasks and datasets introduced will likely serve as important benchmarks for future studies aiming to bridge the gap between human and machine reasoning capabilities.