works on

From the 1 of 5 linked papers with an AI index.

activity
20242026
most citedLIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.SE2026

UniCode: Augmenting Evaluation for Code Reasoning

Xinyue Zheng, Haowei Lin, Shaofei Cai +3

The paper presents UniCode, a generative evaluation framework that augments seed coding problems and automatically generates tests to more rigorously assess large language models'…

cs.CL20261 cited

LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning

Yansheng Mao, Yufei Xu, Jiaqi Li +5

Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-cont…

cs.CL2026

When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations

Kailin Jiang, Yuntao Du, Yukai Ding +7

Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradatio…

cs.CL2025

In-Context Editing: Learning Knowledge from Self-Induced Distributions

Siyuan Qi, Bangcheng Yang, Kailin Jiang +5

In scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded gen…

cs.CL2024

LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning

Yansheng Mao, Jiaqi Li, Fanxu Meng +3

Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context…