Task context suppresses factual correction in LLMs at the response-selection stage even when the model has encoded the error, and two training-free interventions raise correction rates substantially.
BERT rediscovers the classical NLP pipeline
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
EdgeRazor uses structural mixed-precision quantization, layer-adaptive feature distillation, and entropy-aware KL divergence to achieve 1.88-bit LLMs that outperform prior 2-bit and 3-bit baselines with 4-10x lower training budget.
citing papers explorer
-
Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs
Task context suppresses factual correction in LLMs at the response-selection stage even when the model has encoded the error, and two training-free interventions raise correction rates substantially.
-
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
EdgeRazor uses structural mixed-precision quantization, layer-adaptive feature distillation, and entropy-aware KL divergence to achieve 1.88-bit LLMs that outperform prior 2-bit and 3-bit baselines with 4-10x lower training budget.