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cs.CL2026
Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders
Guangzhi Xiong, Zhenghao He, Bohan Liu +2
Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs) by grounding outputs in retrieved evidence, but faithfulness failures, where generation…
cs.CL2026★ 1 cited
Reasoning Beyond Chain-of-Thought: A Latent Computational Mode in Large Language Models
Zhenghao He, Guangzhi Xiong, Bohan Liu +2
Chain-of-Thought (CoT) prompting has improved the reasoning performance of large language models (LLMs), but it remains unclear why it works and whether it is the unique mechanism…
cs.CL2024
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
Sanchit Sinha, Yuguang Yue, Victor Soto +3
Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts…