1 citations · 1 across the 6 of their papers we have counts for
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Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models
Qi Cao, Andrew Gambardella, Takeshi Kojima +2
Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, the truthfulness of their outputs is not guaranteed, and their tendency toward…
Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
Ru Wang, Wei Huang, Qi Cao +3
Test-time reinforcement learning (TTRL) offers a label-free paradigm for adapting models using only synthetic signals at inference, but its success hinges on constructing reliable…
Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?
Fumiya Uchiyama, Takeshi Kojima, Andrew Gambardella +3
Recent large language models (LLMs) have demonstrated remarkable generalization abilities in mathematics and logical reasoning tasks. Prior research indicates that LLMs pre-trained…
Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning
Shota Takashiro, Takeshi Kojima, Andrew Gambardella +3
As large language models (LLMs) are applied across diverse domains, the ability to selectively unlearn specific information is becoming increasingly essential. For instance, LLMs a…
Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text
Qi Cao, Takeshi Kojima, Yutaka Matsuo +1
While Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear. In this study, we present novel experimental…