4 papers · 1 filter
Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals
Yamato Narita, Issei Sato
Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains difficult because activation…
Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
Shota Fujikawa, Issei Sato
Hallucination detection has become increasingly important for improving the reliability of large language models (LLMs). Recently, hybrid approaches such as HaMI, which combine sem…
Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
Akiyoshi Tomihari, Issei Sato
Recent analyses question whether reinforcement learning (RL) is responsible for strong reasoning in large language models (LLMs). At the same time, distillation and inference-time…
Understanding Scattered Forest Search: A Version-Space Perspective on Multi-Turn Program Correction
Yuto Tanaka, Issei Sato
In multi-turn program correction, the state-of-the-art method Scattered Forest Search (SFS) has been proposed, employing Monte Carlo Tree Search (MCTS) with carefully crafted initi…