Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Merging
arXiv:2607.08540v1 Announce Type: cross Abstract: Conversational information retrieval is challenging since it requires the consideration of the conversation history which potentially gives rise to topic shifts and coreference resolution across previous turns. To address these challenges, previous work mainly rely on traditional fine-tuning of ad-hoc retrievers on conversational datasets or extrapolates their generalizability through multi-tasking. However, this mainstream approach is costly - since it requires model re-training - and exhibits catastrophic forgetting, where the model loses its
The proliferation of conversational AI systems necessitates more efficient and adaptable information retrieval, pushing research into methods that reduce training costs and combat catastrophic forgetting.
Improving ad-hoc search for conversational AI without costly re-training addresses a significant barrier to scaling and deploying sophisticated AI agents, directly impacting their real-world utility and adoption.
This research proposes 'model merging' as a more efficient alternative to traditional fine-tuning for conversational AI, potentially lowering development costs and accelerating iterative improvements in agentic systems.
- · AI developers
- · Conversational AI platforms
- · SaaS providers leveraging AI
- · Companies reliant on brute-force re-training
- · Inefficient AI development methodologies
More sophisticated and context-aware conversational AI agents become feasible with reduced resource overhead.
Accelerated development cycles for AI agents could lead to faster market adoption across various industries.
The reduced cost of creating adaptable agents may lower the barrier to entry for AI development, fostering greater innovation and competition.
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Read at arXiv cs.CL