Summary of Is It Really Long Context If All You Need Is Retrieval? Towards Genuinely Difficult Long Context Nlp, by Omer Goldman et al.
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
by Omer Goldman, Alon Jacovi, Aviv Slobodkin, Aviya Maimon, Ido Dagan, Reut Tsarfaty
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper argues that grouping various long-context applications together under a single term is unproductive, as they have distinct properties making them more or less challenging. To address this, the authors introduce a taxonomy based on two orthogonal axes: Diffusion (how hard it is to find necessary information) and Scope (the amount of necessary information). The paper surveys existing literature on long-context tasks, justifying its descriptive vocabulary, and highlights the most under-explored settings with highly diffused and lengthy necessary information. By promoting informed research design, this work encourages careful task and benchmark creation for distinctly long contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how researchers are trying to use special computer models that can understand longer pieces of text. They think that grouping these tasks together is not helpful because they are very different. The authors suggest a way to group them based on two things: how hard it is to find the important information and how much information there is to find. They look at what other researchers have done in this area, explain why their method makes sense, and say that we should create new tasks and tests that are specifically designed for longer pieces of text. |
Keywords
» Artificial intelligence » Diffusion
