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20242026
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cs.CL2026

Metacognition in LLMs: Foundations, Progress, and Opportunities

Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu +3

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become…

cs.CL2026

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs

Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2

Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in ke…

cs.CL2026

SciMDR: Advancing Scientific Multimodal Document Reasoning

Ziyu Chen, Yilun Zhao, Chengye Wang +3

Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this…

cs.CL2025

Investigating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-hop Queries

Gabrielle Kaili-May Liu, Bryan Li, Arman Cohan +2

Real-world use cases often present RAG systems with complex queries for which relevant information is missing from the corpus or is incomplete. In these settings, RAG systems must…

cs.CL2025

MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs

Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu +3

A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-relian…

cs.CL2024

MDCure: A Scalable Pipeline for Multi-Document Instruction-Following

Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu +2

Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved…