5 papers
Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
Wanying Ren, Xin Song, Futing Wang +2
Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted significant attention. However…
MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan
Xin Song, Zhilin Guan, Ruidong Han +12
Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and str…
Benchmarking and Rethinking Knowledge Editing for Large Language Models
Guoxiu He, Xin Song, Futing Wang +1
Knowledge editing aims to update the embedded knowledge within Large Language Models (LLMs). However, existing approaches, whether through parameter modification or external memory…
Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoning
Guoxiu He, Xin Song, Aixin Sun
As real-world knowledge evolves, the information embedded within large language models (LLMs) can become outdated, inadequate, or erroneous. Model editing has emerged as a prominen…
Interweaving Memories of a Siamese Large Language Model
Xin Song, Zhikai Xue, Guoxiu He +2
Parameter-efficient fine-tuning (PEFT) methods optimize large language models (LLMs) by modifying or introducing a small number of parameters to enhance alignment with downstream t…