69 citations · 70 across the 8 of their papers we have counts for
5 papers · 1 filter
Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models
Qi Cao, Takeshi Kojima, Andrew Gambardella +3
Large language models (LLMs) demonstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while being factually incorrect. This…
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…
Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar
Andrew Gambardella, Takeshi Kojima, Yusuke Iwasawa +1
Typical methods for evaluating the performance of language models evaluate their ability to answer questions accurately. These evaluation metrics are acceptable for determining the…
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…