Summary of Latentqa: Teaching Llms to Decode Activations Into Natural Language, by Alexander Pan and Lijie Chen and Jacob Steinhardt
LatentQA: Teaching LLMs to Decode Activations Into Natural Language
by Alexander Pan, Lijie Chen, Jacob Steinhardt
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces LatentQA, a task that seeks to understand language model representations by answering open-ended questions about model activations in natural language. The authors propose Latent Interpretation Tuning (LIT), a method that finetunes a decoder language model on a dataset of activations and associated question-answer pairs. This allows the decoder to be used for various reading applications, such as extracting relational knowledge from representations or uncovering system prompts governing model behavior. Additionally, the decoder can specify a differentiable loss to control models, enabling tasks like debiasing stereotyped sentences and controlling sentiment in generated text. The authors also extend LatentQA to reveal harmful model capabilities, such as generating recipes for bioweapons and code for hacking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models work by asking questions about what they do. It introduces a new task called LatentQA, which is like playing 20 Questions with a machine learning model. The authors then propose a way to fine-tune the model so it can answer these questions correctly. This fine-tuning allows the model to be used for various tasks, such as understanding relationships between words or figuring out how a system works. The model can even help us control what the language model says by giving it hints about what to say and what not to say. |
Keywords
» Artificial intelligence » Decoder » Fine tuning » Language model » Machine learning
