3 citations · 7 across the 13 of their papers we have counts for
13 papers
Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning
Xinyan Guan, Jiali Zeng, Chunlei Xin +5
Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivatio…
Offline Exploration-Aware Fine-Tuning for Long-Chain Mathematical Reasoning
Yongyu Mu, Jiali Zeng, Fandong Meng +2
Through encouraging self-exploration, reinforcement learning from verifiable rewards (RLVR) has significantly advanced the mathematical reasoning capabilities of large language mod…
GRAM-R: Self-Training Generative Foundation Reward Models for Reward Reasoning
Chenglong Wang, Yongyu Mu, Hang Zhou +10
Significant progress in reward modeling over recent years has been driven by a paradigm shift from task-specific designs towards generalist reward models. Despite this trend, devel…
RewardAnything: Generalizable Principle-Following Reward Models
Zhuohao Yu, Jiali Zeng, Weizheng Gu +7
Reward Models, essential for guiding Large Language Model optimization, are typically trained on fixed preference datasets, resulting in rigid alignment to single, implicit prefere…
Dissecting Long-Chain-of-Thought Reasoning Models: An Empirical Study
Yongyu Mu, Jiali Zeng, Bei Li +5
Despite recent progress in training long-chain-of-thought reasoning models via scaling reinforcement learning (RL), its underlying training dynamics remain poorly understood, and s…
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning
Ziqing Qiao, Yongheng Deng, Jiali Zeng +7
Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs, increasing computational over…
LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information
Bowen Ping, Jiali Zeng, Fandong Meng +3
Long-form generation is crucial for academic writing papers and repo-level code generation. Despite this, current models, including GPT-4o, still exhibit unsatisfactory performance…
DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
Xinyan Guan, Jiali Zeng, Fandong Meng +6
Large Language Models (LLMs) have shown remarkable reasoning capabilities, while their practical applications are limited by severe factual hallucinations due to limitations in the…
DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory
Yutong Wang, Jiali Zeng, Xuebo Liu +4
Large language models (LLMs) have achieved reasonable quality improvements in machine translation (MT). However, most current research on MT-LLMs still faces significant challenges…
LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
Yongjing Yin, Jiali Zeng, Yafu Li +2
The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from…
TasTe: Teaching Large Language Models to Translate through Self-Reflection
Yutong Wang, Jiali Zeng, Xuebo Liu +3
Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. Techniques like instruction tuning have effectively enhanced the pr…
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective
Chenze Shao, Fandong Meng, Jiali Zeng +1
Neural Machine Translation (NMT) has made remarkable progress over the past years. However, under-translation and over-translation remain two challenging problems in state-of-the-a…
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Xinwei Long, Jiali Zeng, Fandong Meng +4
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of…