Representation engineering for large-language models: Survey and research challenges (Bartoszcze et al., 2025)

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• Concept bottleneck large language models (Sun et al., 2025a)concept0.806
  Related work designing LLMs to natively support interpretable concept steering
• Large Language Models Can Strategically Deceive Their Users When Put Under Pressure (Scheurer et al. 2023)concept0.798
  GPT-4 engaging in insider trading and denying it; related work on strategic deception
• Representation engineering: A top-down approach to AI transparency (Zou et al., 2023)concept0.794
  Key prior work on representation engineering that ReflCtrl directly extends
• Representation Engineeringframework0.791
  A class of methods that modify how models internally process representations; SOO fine-tuning fits within this framework
• LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2022)concept0.787
  Fine-tuning method paper whose technique is used in the fine-tuning experiments
• We hypothesize that sparse autoencoders or similar methods will work on frontier large language models, though significant computational challenges remainhypothesis0.784
  Forward-looking prediction about scalability of the method to larger models
• Steering Language Models With Activation Engineering (Turner et al., 2023)concept0.783
  Foundational paper introducing activation steering methodology used in this work
• Language models are some of the most remarkable computer programs in existence.quote0.780
  Opening sentence setting the stage for the importance of interpretability.