2 citations · 4 across the 13 of their papers we have counts for
13 papers
A Target-Centric Survey of Quantization-Aware Training
Jiamin Song, Mengjie Zhao, Zijing Wang +6
The rapid development of LLMs incurs prohibitive memory footprints and intensive computational demands. Quantization-Aware Training (QAT) techniques have emerged as a promising sol…
ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning
Yongkang Liu, Zijing Wang, Mengjie Zhao +7
This work presents \textsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activated working set. \textsc{Chunk…
SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
Yongkang Liu, Xing Li, Mengjie Zhao +7
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation…
High-Rank Structured Modulation for Parameter-Efficient Fine-Tuning
Yongkang Liu, Xing Li, Mengjie Zhao +7
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation…
Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction
Lei Hei, Tingjing Liao, Yingxin Pei +4
Relation extraction (RE) aims to identify semantic relations between entities in unstructured text. Although recent work extends traditional RE to multimodal scenarios, most approa…
LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning
Jiaqi Wang, Binquan Ji, Haibo Luo +7
Complex Reasoning in Large Language Models can be dynamically optimized using Test-Time Scaling (TTS) to mitigate Overthinking. Methods such as Coconut, SoftCoT and its variant are…
ConvergeWriter: Data-Driven Bottom-Up Article Construction
Binquan Ji, Jiaqi Wang, Ruiting Li +6
Large Language Models (LLMs) have shown remarkable prowess in text generation, yet producing long-form, factual documents grounded in extensive external knowledge bases remains a s…
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards
Cheng Liu, Yifei Lu, Fanghua Ye +5
Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering…
Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering
Binquan Ji, Haibo Luo, Yifei Lu +6
Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds…
CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision
Yifei Lu, Fanghua Ye, Jian Li +6
Tool invocation significantly enhances the capabilities of Large Language Models (LLMs), yet challenges persist, particularly in complex task scenarios. Current methods, such as in…
Multimodal Relational Triple Extraction with Query-based Entity Object Transformer
Lei Hei, Ning An, Tingjing Liao +3
Multimodal Relation Extraction is crucial for constructing flexible and realistic knowledge graphs. Recent studies focus on extracting the relation type with entity pairs present i…
RTF: Region-based Table Filling Method for Relational Triple Extraction
Ning An, Lei Hei, Yong Jiang +4
Relational triple extraction is crucial work for the automatic construction of knowledge graphs. Existing methods only construct shallow representations from a token or token pair-…
TechGPT-2.0: A large language model project to solve the task of knowledge graph construction
Jiaqi Wang, Yuying Chang, Zhong Li +6
Large language models have exhibited robust performance across diverse natural language processing tasks. This report introduces TechGPT-2.0, a project designed to enhance the capa…