Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Omer Goldman, Alon Jacovi, Aviv Slobodkin, Aviya Maimon, Ido Dagan, Reut Tsarfaty
2024-07-02
Summary
This paper talks about the challenges of evaluating language models that can handle long contexts. It argues that simply measuring how much text a model can process isn't enough, as different tasks require different skills and levels of difficulty.
What's the problem?
As language models have improved, researchers have started to focus on how well these models can manage longer pieces of text. However, many tasks that fall under the category of 'long-context' are grouped together without considering their unique challenges. For example, tasks like summarizing a book or finding specific information in a large document can be very different in terms of difficulty. This lack of distinction can lead to confusion and ineffective evaluations.
What's the solution?
To address this issue, the authors propose a new way to categorize long-context tasks based on two main factors: 'Diffusion,' which measures how difficult it is to find the necessary information in the text, and 'Scope,' which looks at how much important information needs to be found. By creating this taxonomy, the authors aim to provide clearer definitions and descriptions of what makes long-context tasks challenging. They also call for better-designed tasks and benchmarks that reflect these differences.
Why it matters?
This research is important because it helps clarify what we mean by 'long-context' in natural language processing (NLP). By understanding the specific challenges associated with different types of long-context tasks, researchers can develop more effective models and evaluation methods. This could lead to significant advancements in how AI systems understand and process large amounts of information, improving applications like document summarization, question answering, and more.
Abstract
Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. However, many disparate use-cases are grouped together under the umbrella term of "long-context", defined simply by the total length of the model's input, including - for example - Needle-in-a-Haystack tasks, book summarization, and information aggregation. Given their varied difficulty, in this position paper we argue that conflating different tasks by their context length is unproductive. As a community, we require a more precise vocabulary to understand what makes long-context tasks similar or different. We propose to unpack the taxonomy of long-context based on the properties that make them more difficult with longer contexts. We propose two orthogonal axes of difficulty: (I) Diffusion: How hard is it to find the necessary information in the context? (II) Scope: How much necessary information is there to find? We survey the literature on long-context, provide justification for this taxonomy as an informative descriptor, and situate the literature with respect to it. We conclude that the most difficult and interesting settings, whose necessary information is very long and highly diffused within the input, is severely under-explored. By using a descriptive vocabulary and discussing the relevant properties of difficulty in long-context, we can implement more informed research in this area. We call for a careful design of tasks and benchmarks with distinctly long context, taking into account the characteristics that make it qualitatively different from shorter context.