G roun D ial: Human-norm Grounded Safe Dialog Response Generation (2024)

Abstract: Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses agreeing to offensive user input or including toxic content. Previous research aimed to alleviate the toxicity by fine-tuning LLM with manually annotated safe dialogue histories. However, the dependency on additional tuning requires substantial costs. To remove the dependency, we propose GrounDial, where response safety is achieved by grounding responses to commonsense social rules without requiring fine-tuning. A hybrid approach of in-context learning and human-norm-guided decoding of GrounDial enables the response to be quantitatively and qualitatively safer even without additional data or tuning.

## introduction recent llm-based dialog systems generate responses with near-human naturalness. however, there have been reported a number of cases where the agent fails to generate safe responses. they often excuse problematic user input or contain offensive expressions @xcite . this potentially exposes users to misleading moral values or causes offense, threatening the versatility of ai-based dialog systems. previous attempts for safe response generation have been dedicated to making use of exemplary safe dialogues annotated by humans, by fine-tuning @xcite @xcite or training auxiliary safety detector @xcite . however, the fine-tuning-based approaches have two key limitations: cost and generalizability. firstly, they incur additional costs for collecting safe dialogs and training a large-scale lm with numerous parameters. this weakens efficiency since off-the-shelf llms cannot be employed directly. secondly, there is no guarantee that regarding the model's ability to generalize to novel problematic inputs from the growing diversity within the user base. it is crucial to robustly and efficiently generate safe responses in such diverse scenarios. on the other hand, how do humans do? humans learn not only through experiences but also through education. in other words, humans learn common sense social rules or norms explicitly from parents, teachers, books, etc, and ground their behavior to those rules. there have been few early attempts to incorporate the human norms, namely rules-of-thumb (rot), into dialog system @xcite . they successfully improved the response safety by fine-tuning llm to generate rot simultaneously with response, but they did not tackle the dependency on fine-tuning. to the best of our knowledge, there has been no attempt to directly integrate rots into response without the need for additional fine-tuning. in this paper, we propose a novel safe response generation framework, groundial, which achieves the response safety by grounding response to appropriate rot. the response is grounded to rot through two steps: in-context learning (icl) and human-norm-guided decoding (hgd). we demonstrate the quantitative and qualitative effectiveness of groundial with blenderbot @xcite where both response safety and rot relevance are improved without additional training. ## conclusion in this paper, we proposed groundial that grounds responses to social rules through icl and hgd, without additional fine-tuning. experimental results showed the effectiveness of groundial which steers blenderbot to generate safer and more grounded responses.