4 citations · 7 across the 10 of their papers we have counts for
10 papers
HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices
Wei Liu, Siya Qi, Linhai Zhang +2
Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM ag…
Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain
Wei Liu, Siya Qi, Yali Du +1
Large language models (LLMs) make it plausible to build systems that improve through self-evolving loops, but many existing proposals are better understood as self-play and often p…
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Siya Qi, Yudong Chen, Runcong Zhao +6
Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…
Beyond Perplexity: Let the Reader Select Retrieval Summaries via Spectrum Projection Score
Zhanghao Hu, Qinglin Zhu, Siya Qi +3
Large Language Models (LLMs) have shown improved generation performance through retrieval-augmented generation (RAG) following the retriever-reader paradigm, which supplements mode…
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…
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.…
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…
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases
Rena Gao, Xuetong Wu, Tatsuki Kuribayashi +6
This study evaluates Large Language Models' (LLMs) ability to simulate non-native-like English use observed in human second language (L2) learners interfered with by their native f…
A Survey of Automatic Hallucination Evaluation on Natural Language Generation
Siya Qi, Lin Gui, Yulan He +1
The rapid advancement of Large Language Models (LLMs) has brought a pressing challenge: how to reliably assess hallucinations to guarantee model trustworthiness. Although Automatic…
Subjective Bias in Abstractive Summarization
Lei Li, Wei Liu, Marina Litvak +4
Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document. However, many modern large-scale abstractive summ…