# Towards Harnessing Large Language Models for Comprehension of Conversational Grounding

arXiv: https://arxiv.org/abs/2406.01749 · HTML: https://mlsanity.com/paper/2406.01749/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Maintaining Common Ground in Dynamic Environments](https://mlsanity.com/paper/2105.14207/) (2021)
- [Investigating Conversational Search Behavior For Domain Exploration](https://mlsanity.com/paper/2301.04098/) (2023)

## Built on by (most cited first, 3 of 3)

- [Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs](https://mlsanity.com/paper/2408.01088/) (2024)
- [Is Length Really A Liability? An Evaluation of Multi-turn LLM Conversations using BoolQ](https://mlsanity.com/paper/2601.16508/) (2026)
- [Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents](https://mlsanity.com/paper/2610.18684/) (2026)

## Abstract (arXiv)

Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeking conversations to investigate the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks and discuss ongoing research efforts to enhance large language model-based conversational grounding comprehension through pipeline architectures and knowledge bases. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations.