3 citations · 3 across the 17 of their papers we have counts for
12 papers · 1 filter
Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Ej Zhou, Caiqi Zhang, Tiancheng Hu +4
Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large Language Models (LLMs). However, t…
ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance
Hannah Sterz, Fabian David Schmidt, Goran Glavaš +1
As they become increasingly multilingual, Large Language Models (LLMs) exhibit more language confusion, i.e., they tend to generate answers in a language different from the languag…
Lost in Embeddings: Information Loss in Vision-Language Models
Wenyan Li, Raphael Tang, Chengzu Li +3
Vision--language models (VLMs) often process visual inputs through a pretrained vision encoder, followed by a projection into the language model's embedding space via a connector c…
11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis
Chengzu Li, Wenshan Wu, Huanyu Zhang +6
For human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large la…
Quantifying Language Disparities in Multilingual Large Language Models
Songbo Hu, Ivan Vulić, Anna Korhonen
Results reported in large-scale multilingual evaluations are often fragmented and confounded by factors such as target languages, differences in experimental setups, and model choi…
RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding
Jiaang Li, Yifei Yuan, Wenyan Li +8
As vision-language models (VLMs) become increasingly integrated into daily life, the need for accurate visual culture understanding is becoming critical. Yet, these models frequent…
Visual Planning: Let's Think Only with Images
Yi Xu, Chengzu Li, Han Zhou +4
Recent advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have substantially enhanced machine reasoning across diverse tasks. However, these model…
Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
Huanyu Zhang, Chengzu Li, Wenshan Wu +8
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks. However, recent studies have exposed critical limitations in the…
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
Han Zhou, Xingchen Wan, Ruoxi Sun +5
Large language models, employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with prompts tha…
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
Chengzu Li, Wenshan Wu, Huanyu Zhang +5
Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it s…
Language Fusion for Parameter-Efficient Cross-lingual Transfer
Philipp Borchert, Ivan Vulić, Marie-Francine Moens +1
Limited availability of multilingual text corpora for training language models often leads to poor performance on downstream tasks due to undertrained representation spaces for lan…
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding
Fabian David Schmidt, Ivan Vulić, Goran Glavaš +1
Spoken language understanding (SLU) is indispensable for half of all living languages that lack a formal writing system. Unlike for high-resource languages, for these languages, we…