mlsanityData to 7 Oct 2026

Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs

arXiv 2402.11633 ↗First posted 18 Feb 2024cs.CLcited by 18

On the map: Synthetic data and self-improvement for LLMs → Code and instruction data synthesis

Abstract (arXiv)

Identifying user intents in information-seeking dialogs is crucial for a system to meet user's information needs. Intent prediction (IP) is challenging and demands sufficient dialogs with human-labeled intents for training. However, manually annotating intents is resource-intensive. While large language models (LLMs) have been shown to be effective in generating synthetic data, there is no study on using LLMs to generate intent-aware information-seeking dialogs. In this paper, we focus on leveraging LLMs for zero-shot generation of large-scale, open-domain, and intent-aware information-seeking dialogs. We propose SOLID, which has novel self-seeding and multi-intent self-instructing schemes. The former improves the generation quality by using the LLM's own knowledge scope to initiate dialog generation; the latter prompts the LLM to generate utterances sequentially, and mitigates the need for manual prompt design by asking the LLM to autonomously adapt its prompt instruction when generating complex multi-intent utterances. Furthermore, we propose SOLID-RL, which is further trained to generate a dialog in one step on the data generated by SOLID. We propose a length-based quality estimation mechanism to assign varying weights to SOLID-generated dialogs based on their quality during the training process of SOLID-RL. We use SOLID and SOLID-RL to generate more than 300k intent-aware dialogs, surpassing the size of existing datasets. Experiments show that IP methods trained on dialogs generated by SOLID and SOLID-RL achieve better IP quality than ones trained on human-generated dialogs.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Placed in Code and instruction data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 32 of its 70 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

32 of 70 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking3 Feb
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructions20 Dec

Other references · 30

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  4. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  5. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  6. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  7. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 825
  8. 2022Large Language Models Can Self-Improvecited by 656
  9. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Modelscited by 552
  10. 2023Zephyr: Direct Distillation of LM Alignmentcited by 498
  11. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
  12. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
18 more
  1. 2023OpenChat: Advancing Open-source Language Models with Mixed-Quality Datacited by 250
  2. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 151
  3. 2018Building a Conversational Agent Overnight with Dialogue Self-Playcited by 70
  4. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
  5. 2023Evaluating the Zero-shot Robustness of Instruction-tuned Language Modelscited by 63
  6. 2022Data Augmentation for Intent Classification with Off-the-shelf Large Language Modelscited by 49
  7. 2022Controllable Dialogue Simulation with In-Context Learningcited by 42
  8. 2018Analyzing and Characterizing User Intent in Information-seeking Conversationscited by 36
  9. 2023Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue Systemcited by 32
  10. 2022INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactionscited by 27
  11. 2019Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Datasetcited by 26
  12. 2020Guiding attention in Sequence-to-sequence models for Dialogue Act predictioncited by 26
  13. 2022User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systemscited by 20
  14. 2019User Intent Prediction in Information-seeking Conversationscited by 19
  15. 2022What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluationcited by 18
  16. 2022Generating Information-Seeking Conversations from Unlabeled Documentscited by 17
  17. 2020IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systemscited by 16
  18. 2023Intent-calibrated Self-training for Answer Selection in Open-domain Dialoguescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 4; 2026: 4.

18 papers.

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  1. 2024Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agentscited by 210
  2. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
  3. 2025Agentic Large Language Models, a surveycited by 105
  4. 2024A Survey on Self-Evolution of Large Language Modelscited by 86
  5. 2024A Survey on Recent Advances in Conversational Data Generationcited by 23
  6. 2024Ranked List Truncation for Large Language Model-based Re-Rankingcited by 20
  7. 2024Query Performance Prediction using Relevance Judgments Generated by Large Language Modelscited by 18
  8. 2024Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agentscited by 9
  9. 2025Query Decomposition for RAG: Balancing Exploration-Exploitationcited by 5
  10. 2026Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agentscited by 5
  11. 2025A Comprehensive Taxonomy of Negation for NLP and Neural Retrieverscited by 4
  12. 2024CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systemscited by 3
6 more
  1. 2024From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classificationcited by 2
  2. 2025DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilitiescited by 2
  3. 2026Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answeringcited by 2
  4. 2024MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorderscited by 1
  5. 2026IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inferencecited by 0
  6. 2026The Significance of Style Diversity in Annotation-Free Synthetic Data Generationcited by 0

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  1. 2024Intent Detection in the Age of LLMs
  2. 2025SWI: Speaking with Intent in Large Language Models
  3. 2025A Survey of LLM Alignment: Instruction Understanding, Intention Reasoning, and Reliable Generation
  4. 2025LLM-Driven Preference Data Synthesis for Proactive Prediction of the Next User Utterance in Human-Machine Dialogue
  5. 2023A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions
  6. 2026IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning
  7. 2025MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents
  8. 2024Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training