This is the official GitHub repository for our survey paper "Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models".
This repo investigates LLMs' tendency to exhibit acquiescence bias in sequential QA interactions. Includes evaluation methods, datasets, benchmarks, and experiment code to assess and mitigate vulnerabilities in conversational consistency and robustness, offering a reproducible framework for future research.
Reproduce alpaca
Pytorch Implementation of "Sequential Dialogue Context Modeling for Spoken Language Understanding( https://arxiv.org/abs/1705.03455 )"
Entity extraction using BERT + CRF for single-tun / multi-turn setting in dialogues
[EMNLP 2025 main] C3 Benchmark: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations
collect the open dialog corpus and some useful data processing utils.
EMNLP 2019: Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
A smart AI assistant for clinics using LangGraph, LangChain, and OpenAI GPT. It handles patient queries, schedules appointments, detects emergencies, and updates medical records through multi-turn, memory-aware conversations.
MTalk-Bench: Evaluating Speech-to-Speech Models in Multi-Turn Dialogues via Arena-style and Rubrics Protocols
The codes of our paper When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
[NeurIPS 2025 Spotlight] InterMT: Multi-Turn Interleaved Preference Alignment with Human Feedback
MultiTurnResponseSelection
🧠 LLMs don’t just process text — they read the room. Meaning emerges through context — shaped by tone, trust & trajectory. Most benchmarks flatten that. This one maps it.
A smart AI assistant for clinics using LangGraph, LangChain, and OpenAI GPT. It handles patient queries, schedules appointments, detects emergencies, and updates medical records through multi-turn, memory-aware conversations.
The dataset used to fine-tune the GPT-2 model used in Anees for the multi-turn dialogue generation.
PLD: Runtime Phase Model for Stable Multi-Turn LLM Systems
Tutor Agent is an AI-powered educational assistant that leverages a multi-agent along with multi turn architecture to provide specialized tutoring in mathematics and physics (with extensibility for more domains). It uses Google's Gemini API for LLM-powered reasoning, and routes student queries to the most appropriate specialist agent, which can use
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