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arXiv Machine Learning By Shayan Shahrabi-Farahani (Shahid Beheshti University, Tehran, Iran), Dara Rahmati (Shahid Beheshti University, Tehran, Iran)

Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs

Read the original on arXiv Machine Learning →

The study investigates how post‑training quantization (PTQ) affects proactive interference (PI) in large language models. Using bitsandbytes, the authors compare FP16, INT8, and INT4/NF4 precision across three instruction‑tuned models and find that INT4 quantization markedly degrades accuracy under high interference, with INT8 also incurring a smaller penalty in two of the three models. The degradation is linked to increased same‑key intrusion errors and originates in the quantized transformer backbone rather than the output layer.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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