Update README.md

README.md

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 ---
 license: mit
+modalities:
+- image
+- text
+task_categories:
+- image-classification
+- text-classification
+language:
+- en
+tags:
+- memes
+- hate-speech
+- toxicity
+- content-moderation
+- multimodal
+- image-text
+- vision-language
+- social-media
+- nlp
+- computer-vision
+- functional-testing
+- fbhm
+- lsv
+- evaluation
+- classification
+pretty_name: FBHM
 ---
+
+# FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection
+
+Accepted at **EMNLP 2026 Main** 🎉
+
+Paramananda Bhaskar*, Naquee Rizwan*, Daksh Jogchand, Saurabh Kumar Pandey, Animesh Mukherjee
+
+(*) denotes equal contribution
+
+<p align="center">
+  <img src="teaser.pdf" alt="FBHM Dataset" width="400">
+</p>
+
+<p align="center">
+Left: suite of 5,000 FBHM memes spread across 25 functionalities. Each tile presents the functionality number, its description and the corresponding number of memes in that functionality. Right: examples of constructing ten memes for ten target communities using one base image.
+</p>
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-B31B1B)](https://arxiv.org/abs/2605.31349v1)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/hate-alert/fbhm)
+
+</div>
+
+------------------------------------------
+## Abstract
+Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational-confounding rhetorical hate mechanisms with target community features and preventing causal evaluation of model vulnerabilities. To address this, we introduce FBHM, a systematically curated benchmark of **F**unctionality **B**ased **H**ateful **M**emes constructed along two orthogonal axes: 25 distinct rhetorical functionalities and 10 target communities (5,000 memes total). Benchmarking state-of-the-art VLMs reveals a severe generalization gap: models highly accurate on standard datasets catastrophically drop to near-random performance on FBHM, proving they exploit dataset-specific heuristics rather than robust multimodal reasoning. To efficiently close this gap, we propose LSV (**l**earnable **s**teering **v**ectors), an ultra-low data regime strategy that applies a causal intervention objective on as few as 500 steering samples (50 unique base memes), boosting FBHM performance by ~30 Macro-F1 points while outperforming in-context learning and PEFT without degrading source-domain performance.
+
+------------------------------------------
+## Please cite our paper
+
+~~~bibtex
+@misc{bhaskar2026fbhmfunctionalbenchmarkingsteering,
+      title={FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection}, 
+      author={Paramananda Bhaskar and Naquee Rizwan and Daksh Jogchand and Saurabh Kumar Pandey and Animesh Mukherjee},
+      year={2026},
+      eprint={2605.31349},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2605.31349}, 
+}
+~~~
+
+------------------------------------------
+## Contact
+For any questions or issues, please contact: pbhaskar@kgpian.iitkgp.ac.in, nrizwan@kgpian.iitkgp.ac.in