MAFA: A multi-agent framework for annotation

Modern applications require accurate and efficient retrieval of information in response to user queries. Mapping user utterances to the most relevant Frequently Asked Questions (FAQs) is a crucial component of these systems. Traditional approaches often rely on a single model or technique, which may not capture the nuances of diverse user inquiries. In this paper, we introduce a multi-agent framework for FAQ annotation that combines multiple specialized agents with different approaches and a judge agent that reranks candidates to produce optimal results. Our agents utilize a structured reasoning approach inspired by Attentive Reasoning Queries (ARQs), which guides them through systematic reasoning steps using targeted, task-specific JSON queries. Our framework features a specialized few-shot example strategy, where each agent receives different few-shots, enhancing ensemble diversity and coverage of the query space. We evaluate our framework on a real-world banking dataset as well as public benchmark datasets (LCQMC and FiQA), demonstrating significant improvements over single-agent approaches across multiple metrics, including a 14% increase in Top-1 accuracy, an 18% increase in Top-5 accuracy, and a 12% improvement in Mean Reciprocal Rank on our dataset, and similar gains on public benchmarks when compared with traditional single agent annotation techniques. Our framework is particularly effective at handling ambiguous queries, making it well-suited for deployment in production applications while showing strong generalization capabilities across different domains and languages.

From Intent Discovery to Recognition with Topic Modeling and Synthetic Data

Understanding and recognizing customer intents in AI systems is crucial, particularly in domains characterized by short utterances and the cold start problem, where recommender systems must include new products or services without sufficient real user data. Customer utterances are characterized by infrequent word co-occurences and high term variability, which poses significant challenges for traditional methods in specifying distinct user needs and preparing synthetic queries. To address this, we propose an agentic LLM framework for topic modeling and synthetic query generation, which accelerates the discovery and recognition of customer intents. We first apply hierarchical topic modeling and intent discovery to expand a human-curated taxonomy from 36 generic user intents to 278 granular intents, demonstrating the potential of LLMs to significantly enhance topic specificity and diversity. Next, to support newly discovered intents and address the cold start problem, we generate synthetic user query data, which augments real utterances and reduces dependency on human annotation, especially in low-resource settings. Topic model experiments show substantial improvements in coherence and relevance after topic expansion, while synthetic data experiments indicate that in-class few-shot prompting significantly improves the quality and utility of synthetic queries without compromising diversity. We also show that LLM-generated intent descriptions and keywords can effectively substitute for human-curated versions when used as context for synthetic query generation. Our research underscores the scalability and utility of LLM agents in topic modeling and highlights the strategic use of synthetic utterances to enhance dataset variability and coverage for intent recognition. We present a comprehensive and robust framework for online discovery and recognition of new customer intents in dynamic domains.

Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks

Large language models (LLMs) excel in natural language generation but often confidently produce incorrect responses, especially in tasks like mathematical reasoning. Chain-of-thought prompting, self-verification, and multi-agent debate are among the strategies proposed to improve the reasoning and factual accuracy of LLMs. Building on Du et al.'s multi-agent debate framework, we find that multi-agent debate helps at any model scale, and that diversity of thought elicits stronger reasoning in debating LLMs. Across various model sizes, performance on mathematical reasoning tasks benefits most when diverse trained models are used. Remarkably, after 4 rounds of debate, a diverse set of medium-capacity models (Gemini-Pro, Mixtral 7BX8, and PaLM 2-M) outperforms GPT-4 on the GSM-8K benchmark, scoring 91% accuracy. By comparison, when 3 instances of Gemini-Pro are used, performance only reaches 82%. Finally, this diverse set of medium-capacity models sets a new state-of-the-art performance on the ASDiv benchmark (94%). These results underscore the idea that the future of AI is agentic, with diverse cooperating agents yielding emergent capabilities beyond even the most powerful individual models.