Let's CONFER: A Dataset for Evaluating Natural Language Inference Models on CONditional InFERence and Presupposition

Abstract: Natural Language Inference (NLI) is the task of determining whether a sentence pair represents entailment, contradiction, or a neutral relationship. While NLI models perform well on many inference tasks, their ability to handle fine-grained pragmatic inferences, particularly presupposition in conditionals, remains underexplored. In this study, we introduce CONFER, a novel dataset designed to evaluate how NLI models process inference in conditional sentences. We assess the performance of four NLI models, including two pre-trained models, to examine their generalization to conditional reasoning. Additionally, we evaluate LLMs, including GPT-4o, LLaMA, Gemma, and DeepSeek-R1, in zero-shot and few-shot prompting settings to analyze their ability to infer presuppositions with and without prior context. Our findings indicate that NLI models struggle with presuppositional reasoning in conditionals, and fine-tuning on existing NLI datasets does not necessarily improve their performance.

• The paper presents the CONFER dataset, a novel resource designed to evaluate presuppositional reasoning in complex conditional statements.
• It employs a two-tier experimental approach comparing standard NLI models and LLMs, demonstrating significant performance gaps in processing conditionals.
• Results indicate that fine-tuning on the CONFER dataset substantially improves model accuracy, highlighting the need for specialized training on pragmatic reasoning.

Analyzing NLI Models' Capacity for Conditional Inference Using the CONFER Dataset

This paper introduces the CONFER dataset, a significant contribution to addressing a notable gap in the study of Natural Language Inference (NLI) models with respect to their ability to handle presuppositional reasoning within conditional statements. While NLI models have shown proficiency in a range of inference tasks, the nuanced area of presuppositions, particularly within conditionals, has been largely underexplored. This research provides a foundational step in dissecting and evaluating the shortcomings and potentials of NLI models, including LLMs, in this complex aspect of language understanding.

Main Contributions and Methodology

Primarily, the paper presents the CONFER dataset, which is specifically tailored for evaluating presuppositional reasoning in conditional sentences—an essential but challenging domain of pragmatic inference. The dataset comprises 18,000 sentence pairs crafted to test NLI models' understanding of presuppositions that arise in complex conditional structures. It includes five distinct types of conditional sentences that explore various logical relationships between antecedents and consequences, thereby offering a comprehensive testing ground for NLI models.

The research utilizes a two-tier experimental approach. Firstly, it examines the performance of established NLI models, including RoBERTa and DeBERTa, highlighting their limitations in processing presuppositional inferences, especially when conditioned by fine-tuning on standard datasets. Secondly, the study evaluates leading LLMs in zero-shot and few-shot prompts, investigating their capacity to generalize and handle presuppositional reasoning without prior context.

Numerical Findings and Analysis

Before model fine-tuning, the results indicate that precision in handling entailments in the CONFER dataset is notably less robust, especially when compared to models tested on more traditional datasets like IMPPRES and NOPE. However, the models deliver improved results for the Neutral and Contradiction categories within the new dataset, indicating the complexity conditional structures introduce to inferential tasks. When models are fine-tuned with the CONFER dataset, a marked increase in performance across most metrics is observed, suggesting that specific training on targeted datasets can ameliorate understanding of more abstract inferential challenges.

The results from LLM testing in different prompting scenarios show that even state-of-the-art models like GPT-4o experience difficulty in processing complex conditional reasoning tasks, irrespective of their general reasoning capabilities. Zero-shot and few-shot evaluations reveal limitations, particularly in handling logically independent structures and making accurate presuppositional inferences, thus reinforcing the notion that current LLMs require further advancements to excel in pragmatic reasoning tasks.

Implications and Future Directions

The implications of this research extend both practically and theoretically. On a practical level, the study underscores the necessity for more diverse and specifically constructed datasets like CONFER, which probe deeper into the subtleties of language and logic than current datasets allow. Such datasets are crucial for pushing the boundaries of what NLI and LLMs models can achieve in terms of understanding and disambiguating nuanced language use.

Theoretically, this study points towards the need for models that can elegantly navigate between semantic and pragmatic dimensions of language. Future research avenues could involve the development of more sophisticated architectures or training paradigms that integrate pragmatic reasoning directly into the learning objectives of NLI models.

In summary, this study offers a critical tool and insights necessary for advancing the ability of NLI models and LLMs to handle presuppositional reasoning in conditionals — a step towards more profound, context-aware AI language systems. The CONFER dataset not only serves as a vital resource for evaluating current models but also guides future research towards addressing the intricate interplay between semantics and pragmatics in computational linguistics.