No Matter Who You Are: Addressivity and Sycophancy in Large Language Models
To test this, we use Sparse Autoencoders to extract addressivity directions from pre-trained and post-trained models and examine their relationship with sycophancy.
ProofPaper ↗
Key points
- Sycophancy, where an LLM becomes overly agreeable, deferential, or flattering toward the user, has been identified as a major failure mode.
- Post-training transforms the model's output from text completion into utterances directed toward an addressee, thereby giving the output addressivity.
- Steering models with addressivity directions can amplify or suppress sycophancy, and this effect is observed only in post-trained models.
- Finally, through a controlled post-training experiment, we find that models are sycophantic not toward a specific addressee but toward any recognized addressee, suggesting that the presence of an addressee, rather than the addressee's identity, is what elicits sycophancy.
Sources (1)
- [1]No Matter Who You Are: Addressivity and Sycophancy in Large Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:31 AM
To test this, we use Sparse Autoencoders to extract addressivity directions from pre-trained and post-trained models and examine their relationship with sycophancy.
Sycophancy, where an LLM becomes overly agreeable, deferential, or flattering toward the user, has been identified as a major failure mode.
Extractive summary: sentences quoted from the sources.
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