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cs.CL2024
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization
Xinghua Zhang, Haiyang Yu, Cheng Fu +2
In the realm of large language models (LLMs), the ability of models to accurately follow instructions is paramount as more agents and applications leverage LLMs for construction, w…
cs.CL2024★ 1 cited
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
Minzheng Wang, Longze Chen, Cheng Fu +11
Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows. Meanwhile, benchmarks fo…