Papers by Shuxiang Cao
ThinkSLM: Towards Reasoning in Small Language Models (2025.emnlp-main)
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| Challenge: | Reasoning has long been viewed as an emergent property of large language models (LLMs), appearing at or above a certain scale (100B parameters). |
| Approach: | They propose a benchmark to evaluate the reasoning abilities of small language models (SLMs) using quantization, pruning, and distillation. |
| Outcome: | The proposed benchmark systematically evaluates reasoning abilities of small language models trained from scratch or derived from LLMs using quantization, pruning, and distillation. |
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |
CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction (2025.findings-acl)
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| Challenge: | Large language models struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE) |
| Approach: | They propose a multi-agent framework that enhances zero-shot IE through multi-task collaboration. |
| Outcome: | CROSSAGENTIE outperforms state-of-the-art models in structured prediction . the framework significantly reduces inference cost while preserving accuracy . |