1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.AI2026★ 1 cited
Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals
Linda Zeng, Rithwik Gupta, Divij Motwani +2
Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent r…
cs.LG2025
Beyond Introspection: Reinforcing Thinking via Externalist Behavioral Feedback
Diji Yang, Linda Zeng, Kezhen Chen +1
While inference-time thinking allows Large Language Models (LLMs) to address complex problems, the extended thinking process can be unreliable or inconsistent because of the model'…
cs.AI2025
Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing
Diji Yang, Linda Zeng, Jinmeng Rao +1
Retrieval Augmented Generation (RAG) has shown strong capability in enhancing language models' knowledge and reducing AI generative hallucinations, driving its widespread use. Howe…