Papers with generalization gap

2 papers
Resonance RoPE: Improving Context Length Generalization of Large Language Models (2024.findings-acl)

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Challenge: Recent advances in Large Language Models (LLMs) have demonstrated their potential across a wide spectrum of natural language processing tasks.
Approach: They propose a novel approach to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions.
Outcome: The proposed approach improves performance without additional online computational costs on train-short-test-long scenarios.
Major Entity Identification: A Generalizable Alternative to Coreference Resolution (2024.emnlp-main)

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Challenge: Prior work identified annotation differences as one of the main reasons for the limited generalization gap in coreference resolution models.
Approach: They propose an alternative referential task where the target entities are assumed to be specified in the input and the task is limited to the frequent entities.
Outcome: The proposed model generalizes well across domains on multiple datasets with supervised models and LLM-based few-shot prompting.

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