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Found 5 papers, 3 papers with code
Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts
We propose a novel framework for generating causal graphs from narrative texts, bridging high-level causality and detailed event-specific relationships.
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models
Smaller-scale Vision-Langauge Models (VLMs) often claim to perform on par with larger models in general-domain visual grounding and question-answering benchmarks while offering advantages in computational efficiency and storage.
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications.
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models.
Reasoning Like Program Executors
Reasoning over natural language is a long-standing goal for the research community.
Ranked #2 on
Question Answering
on DROP Test
(using extra training data)
