Papers with shared base model

2 papers
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks (2025.findings-acl)

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Challenge: Recent advances in "Chain of Models" approach increase resource demands as each model must be deployed separately.
Approach: They propose a prompt-tuning method that enables models to share hidden states . they modify input and attention masks during training to eliminate redundant forward passes .
Outcome: Empirical results show that FTHSS matches the performance of traditional model chains while improving inference efficiency.
Split-Merge: Scalable and Memory-Efficient Merging of Expert LLMs (2025.emnlp-main)

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Challenge: a zero-shot merging framework for large language models consolidates specialized domain experts into a single model without any further training.
Approach: They propose a zero-shot merging framework that consolidates specialized domain experts into a single model without further training.
Outcome: Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of the proposed framework.

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