The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models

BengaliFig has been introduced as a new challenge set aimed at evaluating figurative and culturally grounded reasoning in Bengali, a language that is considered low-resource. The dataset comprises 435 unique riddles from Bengali traditions, annotated across five dimensions to assess reasoning types and cultural depth, and is designed for use with large language models (LLMs).

A recent study on the geometry of decision-making in Large Language Models (LLMs) reveals insights into their internal processes, particularly in multiple-choice question answering (MCQA) tasks. The research analyzed 28 transformer models, uncovering a consistent pattern in the intrinsic dimension of hidden representations across different layers, indicating how LLMs project linguistic inputs onto low-dimensional manifolds.

TrafficLens has been introduced as a specialized algorithm designed to enhance the analysis of multi-camera traffic video feeds, addressing the challenges posed by the vast amounts of data generated in urban environments. This innovation aims to improve traffic management, law enforcement, and pedestrian safety by efficiently converting video data into actionable insights.

A recent study evaluates large language models (LLMs) by examining their alignment with human preferences, focusing on their generation and evaluation capabilities. The research reveals a strong correlation between LLMs' ability to generate responses and their effectiveness as evaluators, proposing a new benchmarking paradigm for assessing alignment without direct human input.

A recent study has explored the potential of Large Language Models (LLMs) to assist in restructuring hierarchical knowledge to optimize hyperbolic embeddings. This research highlights the importance of a high branching factor and single inheritance in creating effective hyperbolic representations, which are crucial for applications in machine learning that rely on hierarchical data structures.

The introduction of AssurAI marks a significant advancement in the evaluation of generative AI within the Korean socio-cultural context. This new multimodal dataset, comprising 11,480 instances across various media types, aims to address the limitations of existing safety datasets that are predominantly English-centric and text-focused. The dataset includes a taxonomy of 35 distinct AI risk factors tailored to the Korean environment.

Recent advancements in large language models (LLMs) have significantly improved their reasoning capabilities across various domains, including arithmetic and commonsense reasoning. However, extending these abilities to multimodal contexts, where visual and textual inputs must be integrated, remains a challenge. This paper provides an overview of the complexities involved in multimodal reasoning and the methodologies needed to evaluate reasoning accuracy and coherence.

Recent research investigates whether in-context learning (ICL) can alter pre-trained label semantics in large language models (LLMs). The study reveals that while ICL can refine existing semantics, it cannot successfully flip label meanings, as demonstrated through various classification tasks with both natural and inverted demonstrations.