title: Steering Large Language Models for Machine Translation Personalization
summary: High-quality machine translation systems based on large language models
  (LLMs) have simplified the production of personalized translations reflecting
  specific stylistic constraints. However, these systems still struggle in
  settings where stylistic requirements are less explicit and might be harder to
  convey via prompting. We explore various strategies for personalizing
  LLM-generated translations in low-resource settings, focusing on the
  challenging literary translation domain. We explore prompting strategies and
  inference-time interventions for steering model generations towards a
  personalized style, and propose a contrastive framework exploiting latent
  concepts extracted from sparse autoencoders to identify salient personalization
  properties. Our results show that steering achieves strong personalization
  while preserving translation quality. We further examine the impact of steering
  on LLM representations, finding model layers with a relevant impact for
  personalization are impacted similarly by multi-shot prompting and our steering
  method, suggesting similar mechanism at play.
paper_authors._id: 6830410d37efd0e958fcaf9c
paper_authors.name: Daniel Scalena
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:14:28.746Z
paper_authors._id: 633e9ae8630ed415eb38073c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/633e9ae8630ed415eb38073c/FtSb_ybm08qBR1liISKQ0.jpeg
paper_authors.fullname: Daniel Scalena
paper_authors.type: user
paper_authors.user: DanielSc4
paper_authors._id: 6830410d37efd0e958fcaf9d
paper_authors.name: Gabriele Sarti
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-23T09:35:27.088Z
paper_authors._id: 5e7749883d77a72421292d07
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5e7749883d77a72421292d07/M4AmBReZk_otxCIG3o0bL.jpeg
paper_authors.fullname: Gabriele Sarti
paper_authors.type: user
paper_authors.user: gsarti
paper_authors._id: 6830410d37efd0e958fcaf9e
paper_authors.name: Arianna Bisazza
paper_authors._id: 6830410d37efd0e958fcaf9f
paper_authors.name: Elisabetta Fersini
paper_authors._id: 6830410d37efd0e958fcafa0
paper_authors.name: Malvina Nissim
paper_title: Steering Large Language Models for Machine Translation Personalization
paper_summary: High-quality machine translation systems based on large language models
  (LLMs) have simplified the production of personalized translations reflecting
  specific stylistic constraints. However, these systems still struggle in
  settings where stylistic requirements are less explicit and might be harder to
  convey via prompting. We explore various strategies for personalizing
  LLM-generated translations in low-resource settings, focusing on the
  challenging literary translation domain. We explore prompting strategies and
  inference-time interventions for steering model generations towards a
  personalized style, and propose a contrastive framework exploiting latent
  concepts extracted from sparse autoencoders to identify salient personalization
  properties. Our results show that steering achieves strong personalization
  while preserving translation quality. We further examine the impact of steering
  on LLM representations, finding model layers with a relevant impact for
  personalization are impacted similarly by multi-shot prompting and our steering
  method, suggesting similar mechanism at play.
paper_ai_summary: Strategies including prompting and contrastive frameworks using latent concepts from sparse autoencoders effectively personalize LLM translations in low-resource settings while maintaining quality.
paper_ai_keywords: large language models LLMs literary translation prompting strategies inference-time interventions steering contrastive framework latent concepts sparse autoencoders personalization properties translation quality multi-shot prompting