- The paper defines natural language reasoning by merging philosophical insights with practical inference methods in NLP.
- It categorizes reasoning into deductive and defeasible types while analyzing chain-of-thought prompting in large language models.
- It highlights empirical challenges like spurious correlations and advocates advanced techniques to enhance reasoning generalization.
Natural Language Reasoning: A Survey
This survey paper titled "Natural Language Reasoning, A Survey" provides an extensive review of natural language reasoning (NLR) within the field of NLP. The paper delineates a systematic framework for understanding NLR, thereby offering a distinct conceptual clarity aimed at guiding both academic discourse and practical applications. The authors, Fei Yu, Hongbo Zhang, Prayag Tiwari, and Benyou Wang from The Chinese University of Hong Kong and Halmstad University, meticulously dissect the domain into core thematic areas highlighting conceptual definitions, task requirements, reasoning taxonomies, prevailing methodologies, and emerging directions for future research.
Conceptual Framework
A primary contribution of this work is its effort to define NLR in NLP contexts, merging philosophical perspectives with practical considerations. The authors argue that NLR extends beyond simple language understanding, necessitating the integration of diverse knowledge sources to enable inference and decision-making. To this end, reasoning is categorized into deductive and defeasible (non-deductive), each with distinct epistemological goals and methodological implications. Deductive reasoning deals with strict logical inference, whereas defeasible reasoning is concerned with probable conclusions subject to new evidence.
Taxonomy and Methodologies
The paper proposes a taxonomy distinguishing between end-to-end reasoning, forward reasoning, and backward reasoning. End-to-end reasoning models, albeit efficient, present limitations in interpretability and lack structured reasoning paths. Forward reasoning, particularly chain-of-thought (CoT) prompting, is noted for its impact in LLMs like GPT-3 and GPT-4, showcasing the emergent ability to enhance multi-step reasoning through intermediate logic steps before reaching conclusions. Meanwhile, backward reasoning through techniques like question decomposition offers efficient goal-directed reasoning, significantly benefiting complex tasks requiring top-down problem-solving.
Empirical Advances and Challenges
Empirical insights reveal the potential of PLMs to grasp reasoning tasks across multiple domains by finetuning on task-specific data. However, a key challenge remains in the prevalence of spurious correlations and dataset artifacts which impede the generalization of reasoning skills. Despite this, the emergence of LLM capabilities to perform zero-shot reasoning and generate coherent logic chains indicates a positive trajectory.
Implications and Future Directions
The research underscores the evolving capabilities of AI systems in reasoning, with several implications. Practically, it urges further exploration into algorithmic advancements for better reasoning under uncertainty and more complex inference scenarios. Furthermore, the pursuit of more interpretable AI systems necessitates advances in generating faithful reasoning paths. Theoretically, it poses questions about the inherent cognitive processes that PLMs emulate or enhance, paving the way for a deeper understanding of machine intelligence.
To address current limitations, the authors emphasize the importance of advancing defeasible reasoning capabilities and fostering multi-modality research. Furthermore, they advocate for a strategic emphasis on creating scalable datasets and leveraging novel prompting techniques to capture more nuanced reasoning processes.
In conclusion, this survey paper serves as a comprehensive guide for researchers in the NLP domain, providing foundational knowledge and strategic insights into the ongoing and future developments in natural language reasoning. The integration of philosophical reasoning concepts into practical NLP applications sets a robust framework for the ongoing expansion of AI capabilities in discerning and operationalizing natural language logic.