activity
20232025
most citedSparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers

3 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

cs.IR20251 cited

TongSearch-QR: Reinforced Query Reasoning for Retrieval

Xubo Qin, Jun Bai, Jiaqi Li +2

Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or comple…

cs.AI2025

ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection

Jiaqi Li, Xinyi Dong, Yang Liu +6

We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively gener…

cs.CL20243 cited

Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers

Chao Lou, Zixia Jia, Zilong Zheng +1

Accommodating long sequences efficiently in autoregressive Transformers, especially within an extended context window, poses significant challenges due to the quadratic computation…

cs.CL2024

LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments

Zixia Jia, Mengmeng Wang, Baichen Tong +2

Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs. However, it remain…

cs.CL2024

Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels

Zixia Jia, Junpeng Li, Shichuan Zhang +2

Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like d…

cs.CL20231 cited

Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models

Junpeng Li, Zixia Jia, Zilong Zheng

Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and gene…