Analyzing Gaps in Grounding Acts Between Humans and Large Language Models

Core Concepts

Large language models generate text with significantly fewer grounding acts compared to humans, indicating a fundamental gap in how they establish common ground.

Abstract

The article examines the discrepancies between how humans and large language models (LLMs) use grounding acts in dialogue. Grounding acts, such as clarification, acknowledgment, and follow-up questions, are crucial for building shared understanding between conversation participants. The authors first curate a set of grounding acts based on prior research in linguistics and dialogue analysis. They then use these acts to analyze conversations across three domains - emotional support, education, and persuasion - where grounding is critical. The authors find that compared to humans, LLM generations contain significantly fewer grounding acts. For example, LLMs use 64.3% fewer follow-up questions and 83.4% fewer acknowledgment acts than humans. Furthermore, the agreement between human and LLM grounding acts, as measured by Cohen's kappa, is poor to fair across all evaluated models. To understand the roots of this "grounding gap", the authors investigate the role of supervised fine-tuning (SFT) and preference optimization (PO) in LLM training. They find that while SFT alone does not improve grounding agreement, PO actually degrades it. The authors hypothesize that current preference datasets may signal that asking questions is dispreferred, leading to LLMs that presume common ground instead of actively constructing it. The authors discuss the risks of LLMs not generating grounding acts in critical domains like social skill training, and suggest that contextualizing preferences across domains and training reward models on multi-turn interactions may help address the grounding gap.

Stats

LLMs generate 64.3% fewer follow-up questions than humans. LLMs use 83.4% fewer acknowledgment acts than humans. Across 3 grounding acts x 3 datasets, only 3/9 have Cohen's kappa agreement significantly greater than zero.

Quotes

"Failing to construct common ground in human-human conversation can be at best misleading and at worst harmful." "We find that—compared to humans—LLMs generate language with less conversational grounding, instead generating text that appears to simply presume common ground." "We observe negative correlation between DPO train steps and Cohen κ agreement on grounding acts, with Pearson R averaging R = −0.79, and p < 0.05 for all acts."

Key Insights Distilled From

Grounding Gaps in Language Model Generations

by Omar... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2311.09144.pdf

Deeper Inquiries

How can we design training datasets and algorithms that incentivize LLMs to actively construct common ground, rather than presuming it?

To incentivize LLMs to actively construct common ground, training datasets and algorithms need to focus on incorporating grounding acts as a fundamental part of the interaction. Here are some strategies to achieve this: Dataset Augmentation: Introduce diverse examples where grounding acts are essential for effective communication. Include scenarios where clarification, acknowledgment, and follow-up questions are crucial for understanding and collaboration. Explicit Grounding Prompts: Design prompts that explicitly instruct LLMs to use grounding acts in their responses. By providing specific instructions on when and how to employ these acts, models can learn the importance of establishing common ground. Reward Mechanisms: Implement reinforcement learning techniques that reward the use of grounding acts. Models should receive positive reinforcement when they successfully employ clarification, acknowledgment, and follow-up questions in their interactions. Multi-turn Dialogue Training: Train LLMs on multi-turn dialogues where the continuity of conversation relies on effective grounding. By exposing models to extended interactions, they can learn the value of maintaining common ground throughout a conversation. Fine-tuning on Real Conversations: Incorporate real-world conversations where human-human grounding is evident. By fine-tuning models on authentic dialogues that showcase effective grounding strategies, LLMs can learn to emulate human-like behavior.

What are the potential negative consequences of LLMs failing to ground in high-stakes domains like mental health support or education, and how can we mitigate these risks?

The failure of LLMs to ground in high-stakes domains like mental health support or education can have severe consequences: Misunderstandings: Without proper grounding, LLMs may misinterpret or misrepresent critical information shared by individuals seeking support. This can lead to incorrect responses and exacerbate the issues faced by the individuals. Lack of Empathy: Effective grounding is essential for demonstrating empathy and understanding in sensitive conversations. If LLMs fail to acknowledge emotions or provide appropriate responses, it can result in a lack of empathy and support for the individuals in need. Risk of Harm: In mental health support, the failure to ground can result in inappropriate responses to distressing situations, potentially causing harm to the individuals seeking help. In education, misunderstandings due to lack of grounding can lead to ineffective teaching and learning experiences. To mitigate these risks, it is crucial to: Prioritize Training on Grounding Acts: Ensure that LLMs are extensively trained on the use of grounding acts in various contexts, especially in high-stakes domains where clear communication is paramount. Human Oversight and Intervention: Incorporate human oversight in critical interactions to review LLM responses and intervene when necessary to correct misunderstandings or provide additional support. Continuous Evaluation and Improvement: Regularly evaluate LLM performance in grounding and adjust training algorithms based on feedback to enhance their ability to construct common ground effectively.

Given the importance of grounding in human communication, how might insights from this work inform the development of more natural and collaborative human-AI interaction beyond just language models?

Insights from this work can significantly impact the development of more natural and collaborative human-AI interaction beyond just language models by: Designing Interactive Systems: Incorporating grounding acts into the design of interactive AI systems can enhance the overall user experience and make interactions more engaging and effective. Enhancing User Understanding: By training AI systems to actively construct common ground, users can feel more understood and supported in their interactions with technology. Improving Task Completion: Effective grounding can lead to better task completion rates as AI systems can better understand user needs and provide more relevant and accurate responses. Building Trust and Rapport: Grounding acts play a crucial role in building trust and rapport between humans and AI systems. By incorporating these acts, AI can establish a stronger connection with users. Personalizing Interactions: Understanding the nuances of grounding can help AI systems personalize interactions based on individual preferences and communication styles, leading to more tailored and effective conversations. By leveraging the insights from this work, developers can create AI systems that not only communicate effectively but also engage in collaborative and empathetic interactions with users, ultimately enhancing the overall human-AI interaction experience.