Papers by Erfan Baghaei Potraghloo
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. |