4 citations · 5 across the 6 of their papers we have counts for
10 papers
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
DSBC : Data Science task Benchmarking with Context engineering
Ram Mohan Rao Kadiyala, Siddhant Gupta, Jebish Purbey +4
Recent advances in large language models (LLMs) have significantly impacted data science workflows, giving rise to specialized data science agents designed to automate analytical t…
Uncovering Cultural Representation Disparities in Vision-Language Models
Ram Mohan Rao Kadiyala, Siddhant Gupta, Jebish Purbey +4
Vision-Language Models (VLMs) have demonstrated impressive capabilities across a range of tasks, yet concerns about their potential biases exist. This work investigates the extent…
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
Israfel Salazar, Manuel Fernández Burda, Shayekh Bin Islam +42
The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While mu…
Robust and Fine-Grained Detection of AI Generated Texts
Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen +11
An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existence day by day. Existing systems often…
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Siddhant Gupta +6
Large Language Models (LLMs) have shown remarkable capabilities, but their development has primarily focused on English and other high-resource languages, leaving many languages un…