Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Zhang, Ruqing"'
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 "hallucination" responses that are not factual. Consequently, attribution for each
Externí odkaz:
http://arxiv.org/abs/2410.12558
Retrieval-augmented generation (RAG) appears as a promising method to alleviate the "hallucination" problem in large language models (LLMs), since it can incorporate external traceable resources for response generation. The essence of RAG in combatin
Externí odkaz:
http://arxiv.org/abs/2410.11217
Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for queries. While this method offers benefits, current approaches are limit
Externí odkaz:
http://arxiv.org/abs/2409.18409
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 address the issue of predictive uncertainty, i.e., how likely it is that a R
Externí odkaz:
http://arxiv.org/abs/2409.16146
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 poses challenges to scientific evaluation and ethical deployment. Rec
Externí odkaz:
http://arxiv.org/abs/2409.14781
Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query. Recent studies have highlighted the potential of a strong generative retrieval model, trained with carefully crafted pr
Externí odkaz:
http://arxiv.org/abs/2407.11504
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ensuring their reliability in practice, has also garnered significant att
Externí odkaz:
http://arxiv.org/abs/2407.06992
Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only deliver strong performance on average but also have the ability to hand
Externí odkaz:
http://arxiv.org/abs/2406.08891
Autor:
Huang, Feini, Jiang, Shijie, Li, Lu, Zhang, Yongkun, Zhang, Ye, Zhang, Ruqing, Li, Qingliang, Li, Danxi, Shangguan, Wei, Dai, Yongjiu
In recent years, artificial intelligence (AI) rapidly accelerated its influence and is expected to promote the development of Earth system science (ESS) if properly harnessed. In application of AI to ESS, a significant hurdle lies in the interpretabi
Externí odkaz:
http://arxiv.org/abs/2406.11882
Adversarial ranking attacks have gained increasing attention due to their success in probing vulnerabilities, and, hence, enhancing the robustness, of neural ranking models. Conventional attack methods employ perturbations at a single granularity, e.
Externí odkaz:
http://arxiv.org/abs/2404.01574