5 citations · 12 across the 14 of their papers we have counts for
6 papers · 1 filter
The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities
Zhaofeng Wu, Xinyan Velocity Yu, Dani Yogatama +2
Modern language models can process inputs across diverse languages and modalities. We hypothesize that models acquire this capability through learning a shared representation space…
Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics
Isabelle Lee, Joshua Lum, Ziyi Liu +1
While interpretability research has shed light on some internal algorithms utilized by transformer-based LLMs, reasoning in natural language, with its deep contextuality and ambigu…
LocateBench: Evaluating the Locating Ability of Vision Language Models
Ting-Rui Chiang, Joshua Robinson, Xinyan Velocity Yu +1
The ability to locate an object in an image according to natural language instructions is crucial for many real-world applications. In this work we propose LocateBench, a high-qual…
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
Deqing Fu, Ruohao Guo, Ghazal Khalighinejad +5
Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs. But do their capabilities change depending on the…
Pelican Soup Framework: A Theoretical Framework for Language Model Capabilities
Ting-Rui Chiang, Dani Yogatama
In this work, we propose a simple theoretical framework, Pelican Soup, aiming to better understand how pretraining allows LLMs to (1) generalize to unseen instructions and (2) perf…
DeLLMa: Decision Making Under Uncertainty with Large Language Models
Ollie Liu, Deqing Fu, Dani Yogatama +1
The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face chall…