565 citations · 730 across the 94 of their papers we have counts for
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TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives
Maitreya Patel, Abhiram Kusumba, Sheng Cheng +4
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the train…
VL-GLUE: A Suite of Fundamental yet Challenging Visuo-Linguistic Reasoning Tasks
Shailaja Keyur Sampat, Mutsumi Nakamura, Shankar Kailas +4
Deriving inference from heterogeneous inputs (such as images, text, and audio) is an important skill for humans to perform day-to-day tasks. A similar ability is desirable for the…
ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions
Shailaja Keyur Sampat, Yezhou Yang, Chitta Baral
Humans observe various actions being performed by other humans (physically or in videos/images) and can draw a wide range of inferences about it beyond what they can visually perce…
Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
Shailaja Keyur Sampat, Maitreya Patel, Yezhou Yang +1
An ability to learn about new objects from a small amount of visual data and produce convincing linguistic justification about the presence/absence of certain concepts (that collec…
ToW: Thoughts of Words Improve Reasoning in Large Language Models
Zhikun Xu, Ming Shen, Jacob Dineen +6
We introduce thoughts of words (ToW), a novel training-time data-augmentation method for next-word prediction. ToW views next-word prediction as a core reasoning task and injects f…
Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark
Himanshu Gupta, Shreyas Verma, Ujjwala Anantheswaran +4
Multi-modal Large Language Models (MLLMs) exhibit impressive problem-solving abilities in various domains, but their visual comprehension and abstract reasoning skills remain under…
REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models
Agneet Chatterjee, Yiran Luo, Tejas Gokhale +2
Text-to-Image (T2I) and multimodal large language models (MLLMs) have been adopted in solutions for several computer vision and multimodal learning tasks. However, it has been foun…
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
Nemika Tyagi, Mihir Parmar, Mohith Kulkarni +5
Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate the reasoning capability of a model which can then guide us to impro…
Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation
Neeraj Varshney, Satyam Raj, Venkatesh Mishra +4
Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitatio…
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models
Nisarg Patel, Mohith Kulkarni, Mihir Parmar +4
As Large Language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like…
Cutting Through the Noise: Boosting LLM Performance on Math Word Problems
Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria +3
Large Language Models (LLMs) excel at various tasks, including solving math word problems (MWPs), but struggle with real-world problems containing irrelevant information. To addres…
Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies
Aswin RRV, Nemika Tyagi, Md Nayem Uddin +2
This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not…
Grounding Stylistic Domain Generalization with Quantitative Domain Shift Measures and Synthetic Scene Images
Yiran Luo, Joshua Feinglass, Tejas Gokhale +3
Domain Generalization (DG) is a challenging task in machine learning that requires a coherent ability to comprehend shifts across various domains through extraction of domain-invar…
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation
Agneet Chatterjee, Tejas Gokhale, Chitta Baral +1
Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results, the impact of the langu…
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
Mihir Parmar, Nisarg Patel, Neeraj Varshney +5
Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really "reason" over the n…
Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks
Amir Saeidi, Shivanshu Verma, Md Nayem Uddin +1
This study evaluates Direct Preference Optimization (DPO) and its variants for aligning Large Language Models (LLMs) with human preferences, testing three configurations: (1) with…
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo +8
One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in…
Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts
Michael Saxon, Yiran Luo, Sharon Levy +3
Benchmarks of the multilingual capabilities of text-to-image (T2I) models compare generated images prompted in a test language to an expected image distribution over a concept set.…
When "Competency" in Reasoning Opens the Door to Vulnerability: Jailbreaking LLMs via Novel Complex Ciphers
Divij Handa, Zehua Zhang, Amir Saeidi +4
Recent advancements in Large Language Model (LLM) safety have primarily focused on mitigating attacks crafted in natural language or common ciphers (e.g. Base64), which are likely…
-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space
Maitreya Patel, Sangmin Jung, Chitta Baral +1
Despite the recent advances in personalized text-to-image (P-T2I) generative models, it remains challenging to perform finetuning-free multi-subject-driven T2I in a resource-effici…