LoRA for Gender-Inclusive Rewriting and Activation Steering for Counter-Narrative Generation
Gender-inclusive language generation seeks to transform biased text into inclusive alternatives while preserving semantic meaning and contextual coherence. This paper presents the IHLC system for the LT-EDI 2026 Shared Task, addressing both gender-inclusive rewriting and counter-narrative generation. For gender-inclusive rewriting, we employ parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning, achieving an official score of 80.00%. Our primary contribution is a compute-efficient inference-time representation engineering approach for counter-narrative generation. We derive a principal steering direction from contrastive hidden-state activations using principal component analysis (PC...
| Authors | Akhil Rajeev P, Manoj Balaji J |
|---|---|
| Primary category | cs.CL |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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