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Efficient and Reliable Generation of Source-grounded Information-seeking Dialogs: A Case Study on Meeting Transcripts

Core Concepts
Combining large language models (LLMs) prompting with human expertise enables efficient and reliable generation of source-grounded information-seeking dialog datasets, as demonstrated by the creation of the MISeD dataset focused on meeting transcripts.
The paper presents a novel methodology for generating source-grounded information-seeking dialog datasets. The key aspects are: Automatic Dialog Generation: User queries are generated by prompting a pre-trained LLM with templates that guide the model to produce different types of queries (general, specific, unanswerable, context-dependent). Agent responses are generated by prompting the LLM with the meeting transcript, dialog history, and the current query. Human Verification and Editing: Annotators validate the automatically generated queries and responses, discarding invalid ones and editing responses to ensure accuracy and faithfulness to the source text. Annotators also manually identify supporting transcript spans as attributions for the responses. The authors apply this methodology to create the MISeD dataset, the first information-seeking dialog dataset focused on meeting transcripts. Evaluations show that models trained on MISeD data achieve strong performance, outperforming zero-shot models and even matching the performance of much larger pre-trained LLMs. The authors also create an independent Wizard-of-Oz test set to assess model generalization beyond the semi-automated MISeD data.
The average length of meeting transcripts in MISeD is 5,800 words. 99% of MISeD responses for answerable queries are supported by transcript attributions, with a median of 2 attributing spans per response. The median length of attributing spans is 96 words, with a median distance of 350 words between subsequent spans.
"Existing methods for creating source-grounded information-seeking dialog datasets are often costly and hard to implement due to their sole reliance on human annotators." "Our first contribution is a new methodology that partially automates the WOZ process by prompting pre-trained LLMs, extending a recent trend of automating dialog dataset generation." "Our second contribution is applying our methodology to create MISeD – the first dataset for information-seeking dialogs over meeting transcripts, supporting the use-case of users catching up on meetings they have missed."

Deeper Inquiries

How could the proposed methodology be extended to other domains beyond meeting transcripts, such as technical manuals or legal documents?

The methodology proposed in the context of meeting transcripts can be extended to other domains such as technical manuals or legal documents by adapting the prompt structure and templates to suit the specific characteristics of these domains. Here are some ways in which the methodology could be extended: Domain-specific Prompt Templates: Just as the prompt templates were tailored for meeting transcripts, they can be customized for technical manuals or legal documents. For technical manuals, the prompts could guide the LLM to generate queries related to troubleshooting steps, product specifications, or installation procedures. In the case of legal documents, the prompts could focus on legal terminology, case references, or specific clauses. Contextual Understanding: LLMs can be fine-tuned on domain-specific data to enhance their understanding of technical or legal language. This would enable them to generate more accurate and contextually relevant queries and responses. Attribution Generation: In technical manuals, attributions could refer to specific sections or steps, while in legal documents, attributions might point to relevant case law or statutes. Adapting the attribution generation process to these specific requirements would be crucial for ensuring the accuracy and relevance of the generated content. Evaluation and Validation: It is essential to have domain experts review the generated dialogs to ensure accuracy and relevance in technical and legal contexts. Human validation and editing would play a crucial role in maintaining the quality of the dataset. By customizing the methodology to suit the nuances of technical manuals or legal documents, it can be effectively applied to generate source-grounded information-seeking dialogs in a variety of domains.

What are the potential limitations or biases that could arise from relying on LLM-generated content, and how could they be mitigated?

Relying on LLM-generated content for dialog generation comes with certain limitations and potential biases that need to be addressed: Lack of Factual Accuracy: LLMs may generate responses that are factually incorrect or misleading, especially in complex domains like legal or technical fields. This can lead to inaccuracies in the generated dialogs. Mitigation: Incorporating fact-checking mechanisms and human validation can help verify the accuracy of the generated content. Biased Language Generation: LLMs may exhibit biases present in the training data, leading to biased responses or attributions. This can perpetuate stereotypes or misinformation. Mitigation: Regular bias audits, diverse training data, and bias mitigation techniques during training can help reduce biases in the generated content. Contextual Understanding: LLMs may struggle with understanding context or domain-specific terminology, resulting in irrelevant or nonsensical responses. Mitigation: Fine-tuning the LLM on domain-specific data and providing contextual cues in the prompts can improve contextual understanding. Attribution Quality: LLMs may have limitations in accurately identifying attributions in complex texts like legal documents. This can impact the relevance and reliability of the generated content. Mitigation: Developing specialized models or algorithms for attribution detection and incorporating human validation for attribution accuracy can help mitigate this limitation. By being aware of these limitations and biases and implementing appropriate mitigation strategies, the reliance on LLM-generated content can be optimized for generating high-quality and reliable dialogs.

How might the attribution generation process be further automated to reduce the need for manual annotation, while maintaining high quality?

Automating the attribution generation process is crucial for reducing manual effort while ensuring high-quality attributions. Here are some strategies to automate attribution generation effectively: Utilize NLP Models: Implement advanced Natural Language Processing (NLP) models, such as Named Entity Recognition (NER) and Relation Extraction models, to automatically identify and extract attributions from the source text. These models can be fine-tuned on domain-specific data to enhance accuracy. Rule-based Systems: Develop rule-based systems that can identify patterns or structures in the text indicative of attributions. These rules can be based on linguistic cues, such as keywords, syntactic structures, or formatting conventions commonly found in attributions. Machine Learning Algorithms: Train machine learning algorithms, such as supervised classifiers or sequence labeling models, to automatically detect attributions in the text. These algorithms can learn from annotated data and improve attribution detection over time. Hybrid Approaches: Combine the strengths of NLP models, rule-based systems, and machine learning algorithms in a hybrid approach to leverage the benefits of each method. For example, using NLP models for entity recognition and machine learning for context-based attribution detection. Feedback Mechanisms: Implement feedback loops where human annotators validate and correct automatically generated attributions. This iterative process can help improve the accuracy of the automated attribution generation system over time. By integrating these strategies and technologies, the attribution generation process can be automated to a large extent, reducing manual effort while maintaining high-quality attributions in the generated dialogs.