6 citations · 14 across the 17 of their papers we have counts for
6 papers · 2 filters
Can LLMs Detect Their Own Hallucinations?
Sora Kadotani, Kosuke Nishida, Kyosuke Nishida
Large language models (LLMs) can generate fluent responses, but sometimes hallucinate facts. In this paper, we investigate whether LLMs can detect their own hallucinations. We form…
Lossless Vocabulary Reduction for Auto-Regressive Language Models
Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida +4
Tokenization -- the process of decomposing a given text into a sequence of subwords called tokens -- is one of the key components in the development of language models. Particularl…
Let's Put Ourselves in Sally's Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models
Kazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida +4
Recent studies have shown that Theory of Mind (ToM) in large language models (LLMs) has not reached human-level performance yet. Since fine-tuning LLMs on ToM datasets often degrad…
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents
Ryota Tanaka, Taichi Iki, Taku Hasegawa +3
We aim to develop a retrieval-augmented generation (RAG) framework that answers questions over a corpus of visually-rich documents presented in mixed modalities (e.g., charts, tabl…
Wavelet-based Positional Representation for Long Context
Yui Oka, Taku Hasegawa, Kyosuke Nishida +1
In the realm of large-scale language models, a significant challenge arises when extrapolating sequences beyond the maximum allowable length. This is because the model's position e…
ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of Mind
Kazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida +5
Existing Theory of Mind (ToM) benchmarks diverge from real-world scenarios in three aspects: 1) they assess a limited range of mental states such as beliefs, 2) false beliefs are n…