Papers with expert model
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? (2026.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable progress in reasoning, but are resource-intensive and require large curated datasets. |
| Approach: | They propose a framework that leverages the behavioral divergence between a stronger expert model and a weaker amateur model. |
| Outcome: | The proposed framework improves accuracy by up to 28.1% while reducing time consumption by 90% and tuning token usage by 99%. |
Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are prohibitive to use under resource constraints due to their high latency and high latex. |
| Approach: | They propose to use a contextual bandit to help choose a model based on a context to improve performance. |
| Outcome: | The proposed model can be used to improve performance on multiple domains even without prior knowledge of the model. |
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs (2024.findings-acl)
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| Challenge: | Large language models have demonstrated considerable capabilities across various tasks . however, they often fall short of the performance achieved by domain-specific state-of-the-art models . |
| Approach: | They propose a tuning-free method to augment domain-specific abilities of Large language models . they leverage insights from the response preference of expert models to augment LLMs . |
| Outcome: | The proposed method outperforms the expert model on 4 ScienceWorld tasks. |
SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents (2024.acl-long)
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Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi, Maarten Sap, Yonatan Bisk, Graham Neubig, Hao Zhu
| Challenge: | Existing studies on building language agents have not addressed this social learning gap. |
| Approach: | They propose an interactive learning method that improves the social intelligence of language agents by using behavior cloning and self-reinforcement based training on filtered social interaction data. |
| Outcome: | The proposed method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent) without the loss of more generic abilities, such as the ability to answer knowledge-based questions. |
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications (2025.acl-long)
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| Challenge: | Existing approaches to source planning fail to achieve this due to misalignment between the model’s expectation of the sources and their actual content. |
| Approach: | They propose a method to optimise large-scale medical knowledge models by combining multiple medical knowledge sources into one query. |
| Outcome: | The proposed method significantly improves multi-source planning performance while training a smaller model to learn source alignment. |
AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)
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Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li
| Challenge: | Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks. |
| Approach: | They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness. |
| Outcome: | The proposed method improves LLMs’ safety over all baselines. |
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)
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| Challenge: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains. |
| Approach: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models. |
| Outcome: | MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks . |