25 citations · 54 across the 28 of their papers we have counts for
7 papers · 1 filter
Persona-Aware Alignment Framework for Personalized Dialogue Generation
Guanrong Li, Xinyu Liu, Zhen Wu +1
Personalized dialogue generation aims to leverage persona profiles and dialogue history to generate persona-relevant and consistent responses. Mainstream models typically rely on t…
TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them
Yidong Wang, Yunze Song, Tingyuan Zhu +11
The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundam…
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios
Fei Zhao, Chengqiang Lu, Yufan Shen +9
While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset…
LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation
Guanrong Li, Kuo Tian, Jinnan Qi +4
Conversational Recommender Systems (CRS) powered by Large Language Models (LLMs) enable users to articulate explicit and dynamic preferences, overcoming the limitations of fixed te…
Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines
Guanrong Li, Haolin Yang, Xinyu Liu +3
In the era of generative AI, recommender systems are moving from precise prediction to trustworthy generation. Large language models (LLMs) support this shift by inferring user int…
PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues
Fangxu Yu, Lai Jiang, Shenyi Huang +2
The ability to understand and predict the mental states of oneself and others, known as the Theory of Mind (ToM), is crucial for effective social scenarios. Although recent studies…
Rethinking Relation Extraction: Beyond Shortcuts to Generalization with a Debiased Benchmark
Liang He, Yougang Chu, Zhen Wu +3
Benchmarks are crucial for evaluating machine learning algorithm performance, facilitating comparison and identifying superior solutions. However, biases within datasets can lead m…