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Sprint projectNov 25, 2024

BBLLM

Joey SKAF, Mickaël Boillaud, Thaïs Distinguin · Team BBLLM team

Submitted to Reprogramming AI Models Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

This project focuses on enhancing feature interpretability in large language models (LLMs) by visualizing relationships between latent features. Using an interactive graph-based representation, the tool connects co-activated features for specific prompts, enabling intuitive exploration of feature clusters. Deployed as a web application for Llama-3-70B and Llama-3-8B, it provides insights into the organization of latent features and their roles in decision-making processes.

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Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?

How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?

How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation

  1. This project develops a visualisation tool for language model SAE latents. Visualisation is an important and underexplored area in interpretability, so it's cool to see this. The visualisation is a graph, where features are connected to one another if they co-occur sufficiently frequently.

    The tool is interesting but I'd really like to see some example of the kind of application it might be used in, or an interesting insight (even something very minor) that the authors obtained from using the tool.

  2. This work presents a way to visualise SAE latents that frequently activate together. With some additional time, it'd be cool to see some insights gained from this kind of technique! It'd be especially cool if there was some insight that wasn't easily uncovered via something like UMAP or PCA over the dictionary vectors.

  3. Very interesting visualisation tool! It would have been great to see a bit more of lit.review and see there is specific valua added where other existing techniques fall short. The fact that is ready for local deployment definately deserves extra points.

Cite this project

@misc{skaf2024bbllm,
  title = {{BBLLM}},
  author = {Joey SKAF and Mickaël Boillaud and Thaïs Distinguin},
  year = {2024},
  month = nov,
  note = {Submitted to Reprogramming AI Models Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/bbllm}},
  url = {https://apartresearch.com/sprints/projects/bbllm}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026