Farhan Ahmed — accepted AI-conference papers

2026

GneissWeb: Preparing High Quality Data for LLMs at Scale

Hajar Emami Gohari, Swanand Ravindra Kadhe, Yousaf Shah, Constantin M Adam, Abdulhamid Adebayo, Praneet Adusumilli +26

ICLR 2026poster

Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM's ability to generalize on a wide range of downstream tasks. In this paper, we introduce **GneissWeb**, a large dataset of aro…

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2026

When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets

Aladin Djuhera, Farhan Ahmed, Swanand Ravindra Kadhe, Syed Zawad, Heiko Ludwig, Holger Boche

ICLR 2026poster

Aligning large language models (LLMs) is a central objective of post-training, often achieved through reward modeling and reinforcement learning methods. Among these, direct preference optimization (DPO) has emerged as a widely adopted technique that fine-tunes LLMs on preferred completions over les…

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2025

Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance

Aladin Djuhera, Swanand Ravindra Kadhe, Syed Zawad, Farhan Ahmed, Heiko Ludwig, Holger Boche

NeurIPS 2025spotlight

Recent work on large language models (LLMs) has increasingly focused on post-training and alignment with datasets curated to enhance instruction following, world knowledge, and specialized skills. However, most post-training datasets used in leading open- and closed-source LLMs remain inaccessible t…

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