Slerp

Model merging as training-free strategy for conversational information retrieval

A new arXiv preprint proposes using model merging (Model Soup and Slerp) as a training-free alternative to fine-tuning for conversational information retrieval, avoiding catastrophic forgetting of ad-hoc retrieval capabilities. Experiments on standard ad-hoc and conversational retrieval benchmarks show up to 15% higher NDCG@3 under zero-shot conditions. The approach enables a single retrieval model to operate across both ad-hoc and conversational settings without retraining.