Papers by Aashiq Muhamed
Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders (2026.acl-long)
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Xiangchen Song, Aashiq Muhamed, Yujia Zheng, Lingjing Kong, Zeyu Tang, Mona T. Diab, Virginia Smith, Kun Zhang
| Challenge: | Sparse Autoencoders (SAEs) are a tool in mechanistic interpretability (MI) but the aspiration to identify a canonical set of features is challenged by the observed inconsistency of learned SAE features across different training runs. |
| Approach: | They propose to use the Pairwise Dictionary Mean Correlation Coefficient to quantify SAE feature consistency as an evaluation axis alongside reconstruction and sparsity. |
| Outcome: | The proposed measure is based on the pairwise dictionary mean correlation coefficient (PW-MCC) on LLM activations. |
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs’ Cultural Processing of Figurative Language (2026.eacl-long)
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| Challenge: | Using figurative language as a proxy for cultural nuance and local knowledge, large language models struggle with connotative meaning. |
| Approach: | They evaluate large language models' ability to process culturally grounded language . they use figurative language as a proxy for cultural nuance and local knowledge . |
| Outcome: | The proposed models can understand and use figurative expressions that encode local knowledge and social nuance. |
Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models (2025.findings-naacl)
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| Challenge: | Sparse Autoencoders (SAEs) are a promising tool for disentangling FM representations, but they struggle to capture rare, yet crucial concepts in the data. |
| Approach: | They propose a technique to train Sparse Autoencoders to illuminate elusive dark matter features by focusing on specific subdomains. |
| Outcome: | The proposed method achieves 12.5% better classification accuracy than general-purpose SAEs when applied to remove spurious gender information. |
GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients (2024.emnlp-main)
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| Challenge: | Existing projection-based methods that project gradients into a lower-dimensional subspace can introduce computational and memory overheads. |
| Approach: | They propose a novel approach that leverages sparse projections to transform gradients into structured sparser updates. |
| Outcome: | The proposed approach significantly reduces memory usage for optimizer states and minimizes memory footprint, computation, and communication costs, leading to substantial throughput improvements. |
CoRAG: Collaborative Retrieval-Augmented Generation (2025.naacl-short)
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| Challenge: | Existing research on Retrieval-Augmented Generation models has focused on centralized settings where a single entity controls both the model and the datastore. |
| Approach: | They propose a framework for RAG where clients jointly train a shared model using a collaborative passage store. |
| Outcome: | The proposed framework outperforms parametric learning methods and locally trained models in low-resource scenarios. |
RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models (2026.eacl-long)
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| Challenge: | Language models fail to selectively refuse to answer based on flawed context, study finds . current benchmarks fail to evaluate complex capabilities like selective refusal . |
| Approach: | They propose a framework that generates diagnostic test cases through controlled linguistic perturbation. |
| Outcome: | The proposed framework employs 176 perturbation strategies across six categories of uncertainty and three intensity levels. |
ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models (2023.acl-short)
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Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, Yiran Chen
| Challenge: | Knowledge distillation (KD) is an effective compression technique to derive a smaller student model from a larger teacher model by transferring the knowledge embedded in the teacher's network. |
| Approach: | They propose a framework and loss function that preserves the semantic similarities of teacher and student training examples to enable the student to retrieve from the knowledge base effectively. |
| Outcome: | The proposed framework preserves the semantic similarities of teacher and student training examples to achieve state-of-the-art performance on the GLUE benchmark. |