Synthetic Dialogue Generation

Related topics:

• A Little Human Data Goes A Long Way
  Faced with an expensive human annotation process, creators of NLP systems increasingly turn to synthetic data generation. While this method shows promise, the extent to which synthetic data can replac…
• Absolute Zero: Reinforced Self-play Reasoning with Zero Data
  Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards. Recent RLVR wo…
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  We hypothesize that users of conversational systems want a more personalized experience, and existing work shows that users are highly receptive to personalized questions (PQs). Question Generation ta…
• Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models
  This position paper introduces and explains the concepts of linear contexts (a single, continuous sequence of interactions) and non-linear contexts (branching or multi-path) in LLM systems. These conc…
• Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks
  “…there is currently a lack of systematic research on the behavioral characteristics of LLMs-driven social bots and their impact on social networks. We have curated data from Chirper, a Twitter-like s…
• Better Alignment with Instruction Back-and-Forth Translation
  We propose a new method, instruction back-and-forth translation, to construct high-quality synthetic data grounded in world knowledge for aligning large language models (LLMs). Given documents from a …
• Bigger is not always better: The importance of human-scale language modeling for psycholinguistics
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• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
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  We propose CoT-Self-Instruct, a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought (CoT) based on the given seed tasks, and then to generate a new synth…
• Collaborative Reasoner: Self-Improving Social Agents with Synthetic Conversations
  With increasingly powerful large language models (LLMs) and LLM-based agents tackling an ever-growing list of tasks, we envision a future where numerous LLM agents work seamlessly with other AI agents…
• Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
  Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we challenge this paradigm and propose Critique Fine-Tuning…
• DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications
  The scarcity of domain-specific dialogue datasets limits the development of dialogue systems across applications. Existing research is constrained by general or niche datasets that lack sufficient sca…
• Dialog Inpainting: Turning Documents into Dialogs
  Many important questions (e.g. “How to eat healthier?”) require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems have long been stymi…
• Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
  The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that …
• Dynamic Task-Oriented Dialogue: A Comparative Study of Llama-2 and Bert in Slot Value Generation
  One form of these are Task-Oriented Dialogue (TOD) systems that allow the user to fulfill a task, such as booking a hotel or making a reservation at a restaurant, by the usage of external services. To…
• Exploring the Potential of Large Language Models in Computational Argumentation
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• Foundation Priors
  Foundation models, and in particular large language models, can generate highly informative responses, prompting growing interest in using these “synthetic” outputs as data in empirical research and d…
• From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
  Abstract. In dialogue generation, the naturalness of responses is crucial for effective human-machine interaction. Personalized response generation poses even greater challenges, as the responses must…
• Grounding Gaps in Language Model Generations
  However, it is unclear whether large language models (LLMs) generate text that reflects human grounding. To this end, we curate a set of grounding acts and propose corresponding metrics that quantify …
• IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
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• Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation
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• Leveraging Large Language Models in Conversational Recommender Systems
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• Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
  Is it possible to synthesize high-quality instruction data at scale by extracting it directly from an aligned LLM? We present a self-synthesis method for generating large-scale alignment data named MA…
• OpinionConv: Conversational Product Search with Grounded Opinions
  “When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also t…
• Orchestrating Synthetic Data with Reasoning
  Many AI applications of interest require specialized multi-modal models. Yet, relevant data for training these models is inherently scarce. Human annotation is prohibitively expensive, error-prone, an…
• Personalized Dialogue Generation with Persona-Adaptive Attention
  Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona. Unlike conventional dialogue generation, the persona-based dialogue needs to con…
• Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents
  Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs…
• Post-training for Efficient Communication via Convention Formation
  Humans communicate with increasing efficiency in multi-turn interactions, by adapting their language and forming ad-hoc conventions. In contrast, prior work shows that LLMs do not naturally show this …
• Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
  At the start of a conversation, a pre-defined persona is stored in the memory pool. When a user sends a message, the clarifier rephrases it to resolve any ambiguities and passes it to the DPR model wh…
• Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
  Enhancing user engagement through personalization in conversational agents has gained significance, especially with the advent of large language models that generate fluent responses. Personalized dia…
• RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
  However, the closed-source nature of state-of-the-art LLMs and their general-purpose training limit role-playing optimization. In this paper, we introduce RoleLLM, a framework to benchmark, elicit, an…
• Scalable Language Models with Posterior Inference of Latent Thought Vectors
  We propose a novel family of language models, Latent-Thought Language Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. These latent…
• Scaling Synthetic Data Creation with 1,000,000,000 Personas
  Therefore, to create diverse synthetic data at scale (e.g., 1 billion diverse math problems), a large number of diverse prompts are needed. Previous research tends to diversify the data synthesis prom…
• Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
  Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the training data quality. …
• Self-Directed Synthetic Dialogues and Revisions Technical Report
  Synthetic data has become an important tool in the fine-tuning of language models to follow instructions and solve complex problems. Nevertheless, the majority of open data to date is often lacking mu…
• Simple Synthetic Data Reduces Sycophancy In Large Language Models
  “Language models have seen significant advancement in recent years, including the capacity to solve complex tasks that require reasoning (Brown et al., 2020; Chowdhery et al., 2022; OpenAI, 2023; Goog…
• Suppressing Pink Elephants with Direct Principle Feedback
  Existing methods for controlling language models, such as RLHF and Constitutional AI, involve determining which LLM behaviors are desirable and training them into a language model. However, in many ca…
• Synthetic Dialogue Dataset Generation using LLM Agents
  Linear programming (LP) problems are pervasive in real-life applications. However, despite their apparent simplicity, an untrained user may find it difficult to determine the linear model of their spe…
• Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
  In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinf…
• TaskLAMA: Probing the Complex Task Understanding of Language Models
  “Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute…
• ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis
  Supervised fine-tuning (SFT) is a common method to enhance the tool calling capabilities of Large Language Models (LLMs), with the training data often being synthesized. The current data synthesis pro…
• Training Dialogue Systems by AI Feedback for Improving Overall Dialogue Impression
  it is important to evaluate not only each response but also the user’s overall dialogue impression. For example, improving the dialogue system’s consistency of responses, personality, and empathy will…
• Voxtral
  We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art performance across a dive…
• WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
  Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because user…