6 papers
AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking
Xilin Jiang, Qiaolin Wang, Junkai Wu +30
Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…
KDCM: Reducing Hallucination in LLMs through Explicit Reasoning Structures
Jinbo Hao, Kai Yang, Qingzhen Su +2
To mitigate hallucinations in large language models (LLMs), we propose a framework that focuses on errors induced by prompts. Our method extends a chain-style knowledge distillatio…
Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning
Jinbo Hao, Kai Yang, Qingzhen Su +3
To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation cha…
From Sequence to Structure: Uncovering Substructure Reasoning in Transformers
Xinnan Dai, Kai Yang, Jay Revolinsky +4
Recent studies suggest that large language models (LLMs) possess the capability to solve graph reasoning tasks. Notably, even when graph structures are embedded within textual desc…
Contextures: Representations from Contexts
Runtian Zhai, Kai Yang, Che-Ping Tsai +3
Despite the empirical success of foundation models, we do not have a systematic characterization of the representations that these models learn. In this paper, we establish the con…
Spectral Journey: How Transformers Predict the Shortest Path
Andrew Cohen, Andrey Gromov, Kaiyu Yang +1
Decoder-only transformers lead to a step-change in capability of large language models. However, opinions are mixed as to whether they are really planning or reasoning. A path to m…