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Sprint projectNov 25, 2024

Clear Thought and Clear Speech: Reducing Grammatical Scope Ambiguity

Zmavli Caimle · Team Girzu

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

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Report: Clear Thought and Clear Speech: Reducing Grammatical Scope Ambiguity

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With language models starting to be used in fields such as law, unambiguity in wording is an important desideratum in model outputs. I therefore try to find features in Llama-3.1-70B-Instruct that correspond to grammatical scope ambiguity using Goodfire's contrastive feature search tool, and try to steer the model away from ambiguous outputs using Goodfire's feature nudging tool.

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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. The author successfully uses steering to decrease "grammatical scope ambiguity."

    With more time, I'd love to see the author work on quantifying the effect and comparing this approach to baselines like prompting.

    Good work!

  2. This is an interesting project - it's a little surprising to me that features can have nuanced stylistic effects, even if only marginally. The autointerp labels we generated for these features could definitely be better, so kudos to the authors for finding features that have these effects using the contrast tool.

    I appreciate the grounded nature of this work: qualitative observations can be the foundation of good science. More examples would be good, and if this was developed further I'd want to see some kind of quantitative evaluation.

  3. This is a really interesting application of feature steering. It's great to see more subtle applications of feature steering being explored - with some additional development this work could be a really interesting data point on the limits (or lack thereof) of current feature steering methods

Cite this project

@misc{caimle2024clear,
  title = {{Clear Thought and Clear Speech: Reducing Grammatical Scope Ambiguity}},
  author = {Zmavli Caimle},
  year = {2024},
  month = nov,
  note = {Submitted to Reprogramming AI Models Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/clear-thought-and-clear-speech-reducing-grammatical-scope-ambiguity}},
  url = {https://apartresearch.com/sprints/projects/clear-thought-and-clear-speech-reducing-grammatical-scope-ambiguity}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026