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Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection
Subaru Kimura, Ryota Tanaka, Shumpei Miyawaki +2
We explore visual prompt injection (VPI) that maliciously exploits the ability of large vision-language models (LVLMs) to follow instructions drawn onto the input image. We propose…
Detecting Response Generation Not Requiring Factual Judgment
Ryohei Kamei, Daiki Shiono, Reina Akama +1
With the remarkable development of large language models (LLMs), ensuring the factuality of output has become a challenge. However, having all the contents of the response with giv…
A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems
Shiki Sato, Reina Akama, Jun Suzuki +1
Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response dat…
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Mengyu Ye, Tatsuki Kuribayashi, Jun Suzuki +2
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style rea…
Chat Translation Error Detection for Assisting Cross-lingual Communications
Yunmeng Li, Jun Suzuki, Makoto Morishita +4
In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate crosslingual communications due to the limitations of…
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
Shiki Sato, Reina Akama, Hiroki Ouchi +3
Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such…