7 papers · 1 filter
Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
Mingda Li, Rundong Lv, Xinyu Li +2
Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate. Existing state-of-the-art UQ approaches for…
FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging
Zijian Li, Xiaocheng Feng, Huixin Liu +3
With the development of large language models, fine-tuning has emerged as an effective method to enhance performance in specific scenarios by injecting domain-specific knowledge. I…
One for All: Update Parameterized Knowledge Across Multiple Models
Weitao Ma, Xiyuan Du, Xiaocheng Feng +8
Large language models (LLMs) encode vast world knowledge but struggle to stay up-to-date, often leading to errors and hallucinations. Knowledge editing offers an efficient alternat…
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
Yichong Huang, Baohang Li, Xiaocheng Feng +4
Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this…
Relay Decoding: Concatenating Large Language Models for Machine Translation
Chengpeng Fu, Xiaocheng Feng, Yichong Huang +5
Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handlin…
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Liang Zhao, Xiachong Feng, Xiaocheng Feng +6
Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including L…