Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs
A key challenge in Arabic NLP is the scarcity of dialectal data relative to Modern Standard Arabic (MSA), causing LLMs to overproduce MSA and struggle with dialectally accurate generation. From an interpretability perspective, this raises a fundamental question: where and how are dialectal features encoded within model internals, and can these representations be leveraged to improve dialect generation without fine-tuning? This study investigates two complementary inference-time approaches that s
Record details
Published: 3 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Internal Pluralism and the Limits of Pairwise Comparisons
arXiv · 2 July 2026
Harnessing Textual Refusal Directions for Multimodal Safety
arXiv · 30 June 2026
User identity conditions moral wrongness ratings in non-reasoning large language models
arXiv · 8 July 2026
DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
arXiv · 8 July 2026
RAG-GNN: retrieval-augmented graph neural networks for protein interaction network embeddings
Frontiers in Artificial Intelligence · 10 July 2026
ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
arXiv · 10 July 2026
How to cite this record
ethics.ai (3 July 2026), “Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs,” evidence record 1553, https://ethics.ai/record/1553 (originally published by HuggingFace Daily Papers).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.