Pith. sign in

REVIEW 4 cited by

Are Long-LLMs A Necessity For Long-Context Tasks?

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.15318 v1 pith:OPV7MFU3 submitted 2024-05-24 cs.CL cs.AI

Hongjin Qian , Zheng Liu , Peitian Zhang , Kelong Mao , Yujia Zhou , Xu Chen , Zhicheng Dou This is my paper · ORCID
classification cs.CLcs.AI
keywords long-contexttaskscontextframeworklc-boostlong-llmsnecessityshort-llm
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we argue that the long-LLMs are not a necessity to solve long-context tasks, as common long-context tasks are short-context solvable, i.e. they can be solved by purely working with oracle short-contexts within the long-context tasks' inputs. On top of this argument, we propose a framework called LC-Boost (Long-Context Bootstrapper), which enables a short-LLM to address the long-context tasks in a bootstrapping manner. In our framework, the short-LLM prompts itself to reason for two critical decisions: 1) how to access to the appropriate part of context within the input, 2) how to make effective use of the accessed context. By adaptively accessing and utilizing the context based on the presented tasks, LC-Boost can serve as a general framework to handle diversified long-context processing problems. We comprehensively evaluate different types of tasks from popular long-context benchmarks, where LC-Boost is able to achieve a substantially improved performance with a much smaller consumption of resource.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PRISM: Efficient Long-Range Reasoning With Short-Context LLMs

    cs.AI 2024-12 conditional novelty 6.0 of 10

    PRISM enables short-context LLMs to outperform incremental and hierarchical merging baselines on long-range reasoning with 4 to 50 times shorter contexts and up to 54% lower cost via structured memory, programmatic re...

  2. CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs

    cs.NI 2024-11 conditional novelty 6.0 of 10

    CAIP is a context-aware iterative prompting framework that improves LLM-based router misconfiguration detection by mining neighboring, similar, and referenced configuration lines.

  3. Boosting Long-Context Management via Query-Guided Activation Refilling

    cs.CL 2024-12 conditional novelty 4.0 of 10

    ACRE uses a bi-layer KV cache with query-guided refilling to answer long-context questions beyond a model's native window, reporting gains over RAG and compression baselines.

  4. Practical Considerations for Agentic LLM Systems

    cs.AI 2024-12 conditional novelty 3.0 of 10

    This paper is a practical survey that organizes research on LLM-based agents into design considerations for planning, memory, tools, and control flow.

Pith tools