Cover Image

LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ali, Iqra, Tseriotou, Talia, Akhter, Mahmud Elahi, Zhou, Yuxiang, Liakata, Maria
Format:	Preprint
Published:	2026
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models
by: Tan, Xingwei, et al.
Published: (2026)

TempoFormer: A Transformer for Temporally-aware Representations in Change Detection
by: Tseriotou, Talia, et al.
Published: (2024)

Investigating LLM Capabilities on Long Context Comprehension for Medical Question Answering
by: AlMannaa, Feras, et al.
Published: (2025)

Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
by: Tseriotou, Talia, et al.
Published: (2023)

Temporal reasoning for timeline summarisation in social media
by: Song, Jiayu, et al.
Published: (2024)

Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models
by: Cao, Mingzi, et al.
Published: (2026)

Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision
by: Tan, Xingwei, et al.
Published: (2025)

The Alignment Bottleneck in Decomposition-Based Claim Verification
by: Akhter, Mahmud Elahi, et al.
Published: (2026)

Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification
by: Dougrez-Lewis, John, et al.
Published: (2024)

Phased Instruction Fine-Tuning for Large Language Models
by: Pang, Wei, et al.
Published: (2024)

Dynamics of Instruction Fine-Tuning for Chinese Large Language Models
by: Song, Chiyu, et al.
Published: (2023)

LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
by: Wang, Yibin, et al.
Published: (2024)

Modeling Subjectivity in Cognitive Appraisal with Language Models
by: Zhou, Yuxiang, et al.
Published: (2025)

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
by: Zou, Muqi, et al.
Published: (2025)

Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning
by: Prottasha, Nusrat Jahan, et al.
Published: (2024)

Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
by: Heuillet, Maxime, et al.
Published: (2025)

Dual Instruction Tuning with Large Language Models for Mathematical Reasoning
by: Zhou, Yongwei, et al.
Published: (2024)

ReFT: Reasoning with Reinforced Fine-Tuning
by: Luong, Trung Quoc, et al.
Published: (2024)

GiFT: Gibbs Fine-Tuning for Code Generation
by: Li, Haochen, et al.
Published: (2025)

Automated Data Enrichment using Confidence-Aware Fine-Grained Debate among Open-Source LLMs for Mental Health and Online Safety
by: Mao, Junyu, et al.
Published: (2025)

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
by: Ruan, Zhiwen, et al.
Published: (2026)

Enhancing Complex Instruction Following for Large Language Models with Mixture-of-Contexts Fine-tuning
by: Lu, Yuheng, et al.
Published: (2025)

Improving Large Language Models with Concept-Aware Fine-Tuning
by: Chen, Michael K., et al.
Published: (2025)

Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation
by: Gkoumas, Dimitris, et al.
Published: (2024)

Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
by: Hayati, Shirley Anugrah, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models
by: Huang, Yulin, et al.
Published: (2025)

Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
by: Zhang, Xinlu, et al.
Published: (2024)

Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages
by: Toukmaji, Christopher, et al.
Published: (2025)

You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
by: He, Wenchong, et al.
Published: (2025)

Instruction Fine-Tuning: Does Prompt Loss Matter?
by: Huerta-Enochian, Mathew, et al.
Published: (2024)

Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification
by: Sivakaran, Thanushon, et al.
Published: (2025)

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
by: Ding, Fei, et al.
Published: (2025)

Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
by: Mai, Long, et al.
Published: (2024)

Fine-tuning Large Language Models with Sequential Instructions
by: Hu, Hanxu, et al.
Published: (2024)

Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science
by: Wang, Taihang, et al.
Published: (2024)

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
by: Luo, Junyu, et al.
Published: (2024)

Memorization in Fine-Tuned Large Language Models
by: Savine, Danil
Published: (2025)

A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
by: Peng, Cheng, et al.
Published: (2025)

Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
by: Jin, Hongwei, et al.
Published: (2024)

HFT: Half Fine-Tuning for Large Language Models
by: Hui, Tingfeng, et al.
Published: (2024)