# Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM

arXiv: https://arxiv.org/abs/2404.02402 · HTML: https://mlsanity.com/paper/2404.02402/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension](https://mlsanity.com/paper/1902.00164/) (2019)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [Survey on Evaluation Methods for Dialogue Systems](https://mlsanity.com/paper/1905.04071/) (2019)
- [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://mlsanity.com/paper/2004.04494/) (2020)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [A Sequence-to-Sequence Approach to Dialogue State Tracking](https://mlsanity.com/paper/2011.09553/) (2020)
- [Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents](https://mlsanity.com/paper/2310.09343/) (2023)
- [Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents](https://mlsanity.com/paper/2308.01542/) (2023)
- [DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation](https://mlsanity.com/paper/2204.13031/) (2022)
- [Context-dependent Instruction Tuning for Dialogue Response Generation](https://mlsanity.com/paper/2311.07006/) (2023)
- [Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs](https://mlsanity.com/paper/2311.09976/) (2023)

## Built on by (most cited first, 2 of 2)

- [Does Self-Attention Need Separate Weights in Transformers?](https://mlsanity.com/paper/2412.00359/) (2024)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)

## Abstract (arXiv)

Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of Token Trails in improving conversational understanding and response generation, achieving state-of-the-art performance. Our results highlight the significance of contextual modeling in conversational AI and underscore the promising potential of Token Trails to advance the field, paving the way for more sophisticated and contextually aware chatbot interactions.