Papers by Deeksha Koul
Robust In-Context Selection via Online Learned Position-Corrected Attention (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to fix this limitation can be classified into two ways: (1) Methods that use the LLM to generate the selection either via logits of item identifiers, or explicit rank permutations often requiring multiple LLM calls or fine-tuning. |
| Approach: | They propose a method that harnesses attention patterns available from a single forward call on the Large Language Model (LLM) the method learns the logic for item selection using a few in-context examples and a simple online position-debiasing mechanism to correct attention distortion. |
| Outcome: | The proposed method improves selection performance over direct generation and prior attention-based methods while remaining robust to prompt variations and item ordering. |