Cohesive Long-form Answers from Passages in Natural Questions for Retrieval Augmented Generation (RAG) Systems

Core Concepts

CLAPNQ is a benchmark dataset for evaluating the full Retrieval Augmented Generation (RAG) pipeline, including retrieval, generation, and the combined RAG system. The dataset provides long, concise, complete, and cohesive answers that are grounded in a single gold passage, as well as unanswerable questions.

Abstract

The CLAPNQ dataset was created from the subset of Natural Questions (NQ) that have long answers (passages) but no short answers. The dataset consists of 4,946 questions, with 2,555 answerable and 2,391 unanswerable questions. For the answerable questions, the dataset provides the original question, the gold passage, and the relevant sentences and answers created by annotators. The answers are concise (around 57 words on average, which is 1/3 of the passage length), complete, and cohesive, with multiple non-contiguous relevant sentences from the passage. The unanswerable questions were selected from NQ and paired with a random passage, as NQ does not provide gold passages for unanswerable questions. CLAPNQ is designed to evaluate the full RAG pipeline, including retrieval, generation, and the combined RAG system. Baseline experiments are provided using state-of-the-art retrieval and generation models, as well as a fine-tuned encoder-decoder model (CLAPNQ-T5-LG). The results highlight the challenges of the RAG task, with a significant drop in performance compared to the gold passage generation task. A human evaluation was also conducted, which showed that the fine-tuned CLAPNQ-T5-LG model performed well in terms of faithfulness to the passage, but struggled with appropriateness compared to the reference answers. The evaluation also highlighted the importance of evaluating the full RAG pipeline, as the reference answers may not be in the retrieved passages, and the retrieved passages may not be relevant to the question.

Stats

The CLAPNQ dataset contains 4,946 questions, with 2,555 answerable and 2,391 unanswerable questions. The average length of the questions is 9 words, and the average length of the answers is 57 words, which is 1/3 of the average passage length of 156 words.

Quotes

"CLAPNQ is the first LFQA benchmark dataset to have grounded gold passages and a full corpus making it suitable for evaluating the full RAG pipeline." "Our experiments and results in Section 4 show that LLMs still need considerable work in answering LFQA, remaining faithful to the document, performing the full RAG pipeline, and knowing when a question should not be answered."

Key Insights Distilled From

CLAPNQ

by Sara Rosenth... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02103.pdf

Deeper Inquiries

How can the CLAPNQ dataset be extended to include more diverse types of questions and passages, such as those from different domains or languages?

To extend the CLAPNQ dataset to include more diverse types of questions and passages, several strategies can be implemented: Domain Expansion: Incorporate questions and passages from various domains such as science, history, technology, and literature. This will diversify the dataset and provide a broader range of topics for evaluation. Multilingual Support: Introduce questions and passages in different languages to create a multilingual dataset. This will enable the evaluation of models across various language contexts and improve their language understanding capabilities. Specialized Domains: Include questions and passages from specialized domains like medicine, law, or finance. This will test the models' ability to handle domain-specific terminology and concepts. Synthetic Data Generation: Generate synthetic data to augment the dataset with additional variations and complexities. This can help in training models to handle a wider range of scenarios and challenges. Crowdsourcing: Utilize crowdsourcing platforms to collect diverse questions and passages from a global pool of contributors. This can ensure a wide representation of topics and languages in the dataset. By incorporating these strategies, the CLAPNQ dataset can be expanded to encompass a more diverse set of questions and passages, enhancing its utility for evaluating models across different domains and languages.

How can models be trained to better distinguish between answerable and unanswerable questions, especially when the retrieved passages are not relevant to the question?

Training models to distinguish between answerable and unanswerable questions, particularly in cases where the retrieved passages are not relevant, can be achieved through the following approaches: Improved Passage Retrieval: Enhance the passage retrieval mechanism to ensure that the retrieved passages are more closely aligned with the question. This can involve refining the retrieval algorithms or incorporating context-aware retrieval techniques. Unanswerable Question Detection: Develop specific models or components within the system dedicated to identifying unanswerable questions. These models can analyze the question structure, context, and retrieved passages to determine if an answer exists. Contextual Understanding: Train models to have a deeper understanding of the context surrounding the question and passages. This can help in identifying subtle cues or nuances that indicate whether a question is answerable or unanswerable. Active Learning: Implement active learning strategies where the model actively seeks feedback on its predictions for unanswerable questions. This feedback loop can help the model improve its discernment over time. Ensemble Models: Utilize ensemble models that combine the outputs of multiple models specializing in different aspects of question answering, including unanswerable question detection. This can provide a more robust decision-making process. By incorporating these strategies, models can be trained to better distinguish between answerable and unanswerable questions, even in scenarios where the retrieved passages may not be directly relevant to the question.

What other techniques or architectures could be explored to improve the conciseness and coherence of the generated answers while maintaining faithfulness to the passage?

To enhance the conciseness, coherence, and faithfulness of generated answers, several techniques and architectures can be explored: Length Control Mechanisms: Implement mechanisms to control the length of generated answers, such as length constraints during training or decoding. This can encourage models to produce more concise responses. Multi-Document Fusion: Explore methods for fusing information from multiple retrieved passages to generate a coherent and comprehensive answer. Techniques like attention mechanisms or hierarchical modeling can help in integrating information effectively. Fine-Tuning on Answer Length: Fine-tune models on the desired answer length distribution to encourage the generation of answers that are neither too short nor too long. This can help in maintaining conciseness while providing sufficient information. Structured Output Generation: Utilize structured output generation techniques to guide the generation process, ensuring that the answer follows a coherent and organized structure. This can improve readability and coherence. Adversarial Training: Incorporate adversarial training strategies to penalize generated answers that deviate from the content or structure of the passage. This can encourage models to produce more faithful responses. Incorporating Reinforcement Learning: Integrate reinforcement learning to reward models for generating concise, coherent, and faithful answers. This can provide additional training signals to improve the overall quality of generated responses. By exploring these techniques and architectures, models can be optimized to generate answers that are not only faithful to the passage but also concise, coherent, and informative, enhancing their performance in long-form question answering tasks.