5 papers
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
So Hasegawa, Shailaja Keyur Sampat, Lei Liu +1
Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fact retrieval from small tables…
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…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…