Nikhil Prakash

selected publications

1. 

   Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking

   Prakash, Nikhil, Shaham, Tamar Rott, Haklay, Tal, Belinkov, Yonatan, and Bau, David

   In International Conference on Learning Representations (ICLR) 2024

   Abs arXiv Bib Website

   Fine-tuning on generalized tasks such as instruction following, code generation, and mathematics has been shown to enhance language models’ performance on a range of tasks. Nevertheless, explanations of how such fine-tuning influences the internal computations in these models remain elusive. We study how fine-tuning affects the internal mechanisms implemented in language models. As a case study, we explore the property of entity tracking, a crucial facet of language comprehension, where models fine-tuned on mathematics have substantial performance gains. We identify a mechanism that enables entity tracking and show that (i) both the original model and its fine-tuned version implement entity tracking with the same circuit. In fact, the entity tracking circuit of the fine-tuned version performs better than the full original model. (ii) The circuits of all the models implement roughly the same functionality, that is entity tracking is performed by tracking the position of the correct entity in both the original model and its fine-tuned version. (iii) Performance boost in the fine-tuned model is primarily attributed to its improved ability to handle positional information. To uncover these findings, we employ two methods: DCM, which automatically detects model components responsible for specific semantics, and CMAP, a new approach for patching activations across models to reveal improved mechanisms. Our findings suggest that fine-tuning enhances, rather than fundamentally alters, the mechanistic operation of the model.

   @inproceedings{prakash2024finetuning,
     title={Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking},
     author={Prakash, Nikhil and Shaham, Tamar Rott and Haklay, Tal and Belinkov, Yonatan and Bau, David},
     booktitle={Proceedings of the 2024 International Conference on Learning Representations},
     note={arXiv:2402.14811},
     year={2024}
   }

2. 

   Discovering Variable Binding Circuitry with Desiderata

   Davies, Xander, Nadeau, Max, Prakash, Nikhil, Shaham, Tamar Rott, and Bau, David

   In Challenges in Deployable Generative AI Workshop, International Conference on Machine Learning (ICML) 2023

   Abs arXiv Bib Website

   Recent work has shown that computation in language models may be human-understandable, with successful efforts to localize and intervene on both single-unit features and input-output circuits. Here, we introduce an approach which extends causal mediation experiments to automatically identify model components responsible for performing a specific subtask by solely specifying a set of \textitdesiderata, or causal attributes of the model components executing that subtask. As a proof of concept, we apply our method to automatically discover shared \textitvariable binding circuitry in LLaMA-13B, which retrieves variable values for multiple arithmetic tasks. Our method successfully localizes variable binding to only 9 attention heads (of the 1.6k) and one MLP in the final token’s residual stream.

   @article{davies2023discovering,
     title={Discovering Variable Binding Circuitry with Desiderata},
     author={Davies, Xander and Max Nadeau and Nikhil Prakash and Tamar Rott Shaham and David Bau},
     journal={arXiv preprint arXiv:2307.03637},
     year={2023}
   }