Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack

• URL: http://arxiv.org/abs/2407.16695v1
• Date: Tue, 23 Jul 2024 17:57:41 GMT
• Title: Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack
• Authors: Xiaoyue Xu, Qinyuan Ye, Xiang Ren,
• Abstract summary: We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn from a sequence of language tasks through in-context learning (ICL) We introduce Task Haystack, an evaluation suite dedicated to assessing and diagnosing how long-context LMs utilize contexts in Lifelong ICL.
• Score: 33.178008350124315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn from a sequence of language tasks through in-context learning (ICL). We further introduce Task Haystack, an evaluation suite dedicated to assessing and diagnosing how long-context LMs utilizes contexts in Lifelong ICL. When given a task instruction and test inputs, long-context LMs are expected to leverage the relevant demonstrations in the Lifelong ICL prompt, avoid distraction and interference from other tasks, and achieve test accuracies that are not significantly worse than the Single-task ICL baseline. Task Haystack draws inspiration from the widely-adopted "needle-in-a-haystack" (NIAH) evaluation, but presents new and unique challenges. It demands that models (1) utilize the contexts with deeper understanding, rather than resorting to simple copying and pasting; (2) navigate through long streams of evolving topics and tasks, which closely approximates the complexities of real-world usage of long-context LMs. Additionally, Task Haystack inherits the controllability aspect of NIAH, providing model developers with tools and visualizations to identify model vulnerabilities effectively. We benchmark 12 long-context LMs using Task Haystack. We find that state-of-the-art closed models such as GPT-4o still struggle in this setting, failing 15% of the cases on average, while all open-weight models we evaluate further lack behind by a large margin, failing up to 61% of the cases. In our controlled analysis, we identify factors such as distraction and recency bias as contributors to these failure cases. Further, we observe declines in performance when task instructions are paraphrased at test time or when ICL demonstrations are repeated excessively, raising concerns about the robustness, instruction understanding, and true context utilization of current long-context LMs.

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