Papers by Gregory Polyakov
Interpretability Analysis of Arithmetic In-Context Learning in Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) solve arithmetic with only a few in-context examples, yet the computations that connect those examples to the answer remain opaque. |
| Approach: | They propose to use in-context examples to illustrate how large language models process ICEs to isolate partial-sum representations in three-operand tasks and investigate their influence on final logits. |
| Outcome: | The proposed model performs better than previous models on three-operand tasks. |