Kyle Richardson

DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models

2022

In this paper, we present and implement a multidimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst -a T5 model We create a synthetic corpus for deep argument …

Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking

2021 · arXiv (Cornell University)

While neural language models often perform surprisingly well on natural language understanding (NLU) tasks, their strengths and limitations remain poorly understood. Controlled synthetic tasks are thus an increasingly important resource for diagnosing model behavior. In …

Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts

2022 · arXiv (Cornell University)

Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems. However, despite the many datasets …

Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs

2022 · arXiv (Cornell University)

Can we teach natural language understanding models to track their beliefs through intermediate points in text? We propose a representation learning framework called breakpoint modeling that allows for learning of this type. Given any text …

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

2025 · arXiv (Cornell University)

We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance on logic …

Probing Natural Language Inference Models through Semantic Fragments

2020

Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential contexts)? While such phenomena …

OCNLI: Original Chinese Natural Language Inference

2020

Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a …