arXiv Machine Learning
5d ago

Less Loss, Fixed Weights: Logit Corrections for Forgetting

The paper investigates whether a language model’s forgetting—worsening performance on standard web text after fine‑tuning or further pretraining—can be mitigated by simple, fixed adjustments to its final token predictions. By fitting a vocabulary‑wide bias (a token‑specific bonus or penalty) and optionally a temperature scaling, the authors evaluate recovery across 14 base‑to‑derived model pairs ranging from 1B to 7B parameters. Results show modest gains: up to 10% recovery with bias alone and up to 36% when combined with temperature, though models further pretrained on math lose the most and recover little; even in a controlled simulation with random damage, the bias‑plus‑temperature fix recovers 29%.

By Hanyu Yang
Hugging Face Trending Papers
Jun 28

The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis

Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually treat these choices as fixed implementation details. This leaves a practical uncertainty: does a model that is safe at FP16 and greedy decoding remain safe after it is quantized and sampled stochastically, or do the two deployment knobs amplify one another?