  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.
