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Found 10 papers, 5 papers with code
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
Further analysis of perception questions reveals that MLLMs struggle to comprehend the visual features (near-random performance) and even count the panels in the puzzle ( <45%), hindering their ability for abstract reasoning.
Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
However, with the increasing adoption of Large Language Models (LLMs) as writing assistants in everyday writing, a critical question emerges: are authors' linguistic patterns still predictive of their personal traits when LLMs are involved in the writing process?
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models
While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation models, namely multi-modal large language models (MLLMs).
Robust Text Classification: Analyzing Prototype-Based Networks
Downstream applications often require text classification models to be accurate, robust, and interpretable.
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms.
ARN: Analogical Reasoning on Narratives
However, while cognitive theories of analogy often focus on narratives and study the distinction between surface, relational, and system similarities, existing work in natural language processing has a narrower focus as far as relational analogies between word pairs.
Contextualizing Argument Quality Assessment with Relevant Knowledge
Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech.
Case-Based Reasoning with Language Models for Classification of Logical Fallacies
The ease and speed of spreading misinformation and propaganda on the Web motivate the need to develop trustworthy technology for detecting fallacies in natural language arguments.
Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed.
Multimodal and Explainable Internet Meme Classification
In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult.
