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New DecompressionLM framework extracts concept graphs from LLMs without pre-defined queries

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DecompressionLM, a new framework designed to extract concept graphs from language models without relying on pre-defined queries. This method addresses limitations in existing knowledge probing techniques, such as cross-sequence coupling and competitive decoding, which can suppress less common concepts. By employing Van der Corput low-discrepancy sequences and arithmetic decoding, DecompressionLM allows for deterministic and parallel generation of concept graphs. The framework also revealed significant differences in concept coverage between activation-aware quantization (AWQ-4bit) and uniform quantization (GPTQ-Int4), with the former showing substantial expansion and the latter a notable collapse. AI

IMPACT This framework could improve the evaluation of compressed language models by providing a more comprehensive understanding of their encoded knowledge.

RANK_REASON The cluster contains a research paper detailing a new method for concept graph extraction from language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New DecompressionLM framework extracts concept graphs from LLMs without pre-defined queries

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The cluster contains a research paper detailing a new method for concept graph extraction from language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhaochen Hong, Jiaxuan You ·

    DecompressionLM: Deterministic, Diagnostic, and Zero-Shot Concept Graph Extraction from Language Models

    arXiv:2602.00377v3 Announce Type: replace Abstract: Existing knowledge probing methods rely on pre-defined queries, limiting extraction to known concepts. We introduce DecompressionLM, a stateless framework for zero-shot concept graph extraction that discovers what language model…

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