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Shengyi Liao

2 accepted papers

2026

SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization

ICLR 2026poster

Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primarily due to insufficient long-context alignment caused by data quality issues, training inefficiencies, and the lack of w…

Cited by 0SourcecodeScholar
2024

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

EMNLP 2024main

Long-context modeling capabilities of Large Language Models (LLMs) have garnered widespread attention, leading to the emergence of LLMs with ultra-context windows. Meanwhile, benchmarks for evaluating long-context language models are gradually catching up. However, existing benchmarks employ irrelev…