108 citations · 118 across the 8 of their papers we have counts for
8 papers
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective
Yijie Chen, Yijin Liu, Fandong Meng +3
Code generation aims to understand the problem description and generate corresponding code snippets, where existing works generally decompose such complex tasks into intermediate s…
Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy
Yijin Liu, Fandong Meng, Jie Zhou
Recently, dynamic computation methods have shown notable acceleration for Large Language Models (LLMs) by skipping several layers of computations through elaborate heuristics or ad…
Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
Yijie Chen, Yijin Liu, Fandong Meng +3
Large Language Models (LLMs) present strong general capabilities, and a current compelling challenge is stimulating their specialized capabilities, such as machine translation, thr…
Instruction Position Matters in Sequence Generation with Large Language Models
Yijin Liu, Xianfeng Zeng, Fandong Meng +1
Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. The fine-tuning…
Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluation
Xianfeng Zeng, Yijin Liu, Fandong Meng +1
N-gram matching-based evaluation metrics, such as BLEU and chrF, are widely utilized across a range of natural language generation (NLG) tasks. However, recent studies have reveale…
BranchNorm: Robustly Scaling Extremely Deep Transformers
Yijin Liu, Xianfeng Zeng, Fandong Meng +1
Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000 layers) and reveals the promising potential of deep scaling. To stabilize the training of deep models, DeepNo…