Steering Large Language Models for Machine Translation Personalization
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.
6830410d37efd0e958fcaf9c
Daniel Scalena
claimed_verified
2025-05-26T08:14:28.746Z
633e9ae8630ed415eb38073c
https://cdn-avatars.huggingface.co/v1/production/uploads/633e9ae8630ed415eb38073c/FtSb_ybm08qBR1liISKQ0.jpeg
Daniel Scalena
user
DanielSc4
6830410d37efd0e958fcaf9d
Gabriele Sarti
extracted_confirmed
2025-05-23T09:35:27.088Z
5e7749883d77a72421292d07
https://cdn-avatars.huggingface.co/v1/production/uploads/5e7749883d77a72421292d07/M4AmBReZk_otxCIG3o0bL.jpeg
Gabriele Sarti
user
gsarti
6830410d37efd0e958fcaf9e
Arianna Bisazza
6830410d37efd0e958fcaf9f
Elisabetta Fersini
6830410d37efd0e958fcafa0
Malvina Nissim
Steering Large Language Models for Machine Translation Personalization
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.
Strategies including prompting and contrastive frameworks using latent concepts from sparse autoencoders effectively personalize LLM translations in low-resource settings while maintaining quality.
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