Papers with inference-time interventions

2 papers
Steering Large Language Models for Machine Translation Personalization (2026.eacl-long)

Copied to clipboard

Challenge: Recent advances in interpretability research have highlighted the effectiveness of steering methods for MT personalization.
Approach: They examine steering strategies for personalizing automatic translations when few examples are available.
Outcome: The proposed steering methods yield higher inference-time computational efficiency than prompting approaches.
Probing Political Ideology in Large Language Models: How Latent Political Representations Generalize Across Tasks (2025.findings-emnlp)

Copied to clipboard

Challenge: Large language models encode rich internal representations of political ideology, but it remains unclear how these representations contribute to model decision-making.
Approach: They apply inference-time interventions to steer a decoder-only transformer along learned ideological directions . they find that learned ideological representations generalize well to bias detection, but not as well to voting simulations .
Outcome: The proposed model steers a transformer along learned ideological directions . political bias detection, voting preference simulation and bias neutralization are tested .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations