6 papers · 1 filter
ZenGen: Social Mind for LLMs
ZenGen Team, Zing Team, Ao Xiang +57
As large language models move from isolated task solving toward long-term service in human environments, they require social intelligence: the ability to infer mental states, track…
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory
Yihang Wang, Xu Huang, Bowen Tian +6
Generative LLM have achieved remarkable success in various industrial applications, owing to their promising In-Context Learning capabilities. However, the issue of long context in…
Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method
Weichao Zhang, Ruqing Zhang, Jiafeng Guo +3
As the scale of training corpora for large language models (LLMs) grows, model developers become increasingly reluctant to disclose details on their data. This lack of transparency…
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework
Lu Chen, Ruqing Zhang, Jiafeng Guo +2
Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. However, existing studies on RAG seldom addres…
A Claim Decomposition Benchmark for Long-form Answer Verification
Zhihao Zhang, Yixing Fan, Ruqing Zhang +1
The advancement of LLMs has significantly boosted the performance of complex long-form question answering tasks. However, one prominent issue of LLMs is the generated "hallucinatio…
On the Capacity of Citation Generation by Large Language Models
Haosheng Qian, Yixing Fan, Ruqing Zhang +1
Retrieval-augmented generation (RAG) appears as a promising method to alleviate the "hallucination" problem in large language models (LLMs), since it can incorporate external trace…