Papers by Massoud Pedram
Activation Steering for Chain-of-Thought Compression (2026.findings-acl)
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| Challenge: | Large language models produce intermediate explanations, commonly referred to as chains of thought (CoTs), but the generated rationales are typically verbose, consuming many additional tokens, and thus degrading throughput and increasing inference energy consumption. |
| Approach: | They propose to generate concise reasoning traces by directly adjusting internal representations via activation steering. |
| Outcome: | The proposed method reduces generated token length by 69.4% across five reasoning benchmarks while maintaining accuracy. |
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation (2024.findings-emnlp)
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| Challenge: | Low-rank adaptation (LoRA) fine-tunes large language models due to its significant reduction in trainable parameters, but its backward updates require storing high-dimensional intermediate activations and optimizer states, requiring high peak GPU memory. |
| Approach: | They propose a low-dimensional adaptation approach to fine-tune large language models which freezes a first projection matrix while introducing a lower-dimensional trainable square matrix. |
| Outcome: | The proposed approach reduces trainable parameters and peak GPU memory footprint while preserving low-dimensional trainable square matrix. |