most citedBeyond Quantification: Navigating Uncertainty in Professional AI Systems

6 citations · 6 across the 2 of their papers we have counts for

collaborators

5 papers

cs.HC20256 cited

Beyond Quantification: Navigating Uncertainty in Professional AI Systems

Sylvie Delacroix, Diana Robinson, Umang Bhatt +12

The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant res…

cs.CL2025

Hatevolution: What Static Benchmarks Don't Tell Us

Chiara Di Bonaventura, Barbara McGillivray, Yulan He +1

Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact…

cs.CL2025

NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning

Wei Liu, Siya Qi, Xinyu Wang +3

Recent advances such as DeepSeek R1-Zero highlight the effectiveness of incentive training, a reinforcement learning paradigm that computes rewards solely based on the final answer…

cs.CL2025

Evaluating LLMs' Assessment of Mixed-Context Hallucination Through the Lens of Summarization

Siya Qi, Rui Cao, Yulan He +1

With the rapid development of large language models (LLMs), LLM-as-a-judge has emerged as a widely adopted approach for text quality evaluation, including hallucination evaluation.…

cs.CL2025

EnigmaToM: Improve LLMs' Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States

Hainiu Xu, Siya Qi, Jiazheng Li +4

Theory-of-Mind (ToM), the ability to infer others' perceptions and mental states, is fundamental to human interaction but remains challenging for Large Language Models (LLMs). Whil…