Go to ACL ARR 2025 February homepage$\textit{L-CiteEval}$: A Suite for Evaluating Fidelity of Long-context Models
Long-context models~(LCMs) have witnessed remarkable advancements in recent years, facilitating real-world tasks like long-document QA. The success of LCMs is founded on the hypothesis that the model demonstrates strong $\textbf{fidelity}$, enabling it to respond based on the provided long context rather than relying solely on the intrinsic knowledge acquired during pre-training. Yet, in this paper, we find that open-sourced LCMs are not as faithful as expected. We introduce $\textit{L-CiteEval}$, an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks. It covers 11 tasks with context lengths ranging from 8K to 48K and a corresponding automatic evaluation pipeline. Evaluation of 11 cutting-edge closed-source and open-source LCMs indicates that, while there are minor differences in their generation, open-source models significantly lag behind closed-source counterparts in terms of fidelity. Furthermore, we analyze the benefits of citation generation for LCMs from both the perspective of explicit model output and the internal attention mechanism.