20 citations · 45 across the 12 of their papers we have counts for
13 papers
IndustryLLM: Failure-Driven LLM Training for Industrial Procurement
Liang Ding, Zhiang Xu, Yuyang Sheng +13
Industrial procurement requires language models to bridge informal buyer jargon, sparse marketplace attributes, and authoritative engineering standards under strict safety toleranc…
BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning
Keshu Fu, Keqin Peng, Jun Bai +6
Large language models often improve task performance by generating long reasoning traces, but the resulting computation is frequently wasted on redundant verification and revision.…
Diagnosing and Mitigating Thinking Collapse in On-Policy Self-Distillation
Keqin Peng, Chen Li, Yuanxin Ouyang +2
On-Policy Self-Distillation (OPSD) has emerged as a crucial paradigm for enhancing and aligning Large Language Models (LLMs). However, in complex reasoning tasks, OPSD paradoxicall…
Think Dense, Not Long: Dynamic Decoupled Conditional Advantage for Efficient Reasoning
Keqin Peng, Yuanxin Ouyang, Xuebo Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) can elicit strong multi-step reasoning, yet it often encourages overly verbose traces. Moreover, naive length penalties in gro…
Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt
Keqin Peng, Liang Ding, Yuanxin Ouyang +2
Reasoning Large Language Models (RLLMs) have demonstrated impressive performance on complex tasks, largely due to the adoption of Long Chain-of-Thought (Long CoT) reasoning. Howeve…
Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding
Keqin Peng, Liang Ding, Yuanxin Ouyang +3
Large language models (LLMs) excel at a range of tasks through in-context learning (ICL), where only a few task examples guide their predictions. However, prior research highlights…
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction
Ziyang Xu, Keqin Peng, Liang Ding +2
Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labe…
Revisiting Demonstration Selection Strategies in In-Context Learning
Keqin Peng, Liang Ding, Yancheng Yuan +4
Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL), where a few examples are used to describe a task to…
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer
Boan Liu, Liang Ding, Li Shen +4
The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without signific…
Towards Making the Most of ChatGPT for Machine Translation
Keqin Peng, Liang Ding, Qihuang Zhong +5
ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource lan…
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
Qihuang Zhong, Liang Ding, Keqin Peng +5
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of…
Vega-MT: The JD Explore Academy Translation System for WMT22
Changtong Zan, Keqin Peng, Liang Ding +9
We describe the JD Explore Academy's submission of the WMT 2022 shared general translation task. We participated in all high-resource tracks and one medium-resource track, includin…
Improving Neural Machine Translation by Denoising Training
Liang Ding, Keqin Peng, Dacheng Tao
We present a simple and effective pretraining strategy {D}en{o}ising {T}raining DoT for neural machine translation. Specifically, we update the model parameters with source- and ta…