6 papers
CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension
Abhilash Nandy, Rahul Seetharaman, Aman Bansal +5
Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor remains challenging because…
Scaling Laws for Cross-Encoder Reranking
Rahul Seetharaman, Aman Bansal, Hamed Zamani +1
Scaling laws are well studied for language models and first-stage retrieval, but not for reranking. We present the first systematic study of scaling laws for cross-encoder reranker…
What Tracks Hard-Negative Transfer? A Study of Lexical and Semantic Signals
Aarush Sinha, Rahul Seetharaman, Aman Bansal
Which properties of hard-negative supervision track dense-retriever transfer before training? We compare lexical noncoverage, frozenencoder similarity statistics, hardness proxies,…
From Native Memes to Global Moderation: Cross-Cultural Evaluation of Vision-Language Models for Hateful Meme Detection
Mo Wang, Kaixuan Ren, Pratik Jalan +5
Cultural context profoundly shapes how people interpret online content, yet vision-language models (VLMs) remain predominantly trained through Western or English-centric lenses. Th…
Leveraging Large Language Models for Predictive Analysis of Human Misery
Bishanka Seal, Rahul Seetharaman, Aman Bansal +1
This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores from natural language descriptions of real-world scenarios. The task is…
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking
Rahul Seetharaman, Kaustubh D. Dhole, Aman Bansal
Large Language Models (LLMs) have demonstrated significant strides across various information retrieval tasks, particularly as rerankers, owing to their strong generalization and k…