Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition

المفاهيم الأساسية

Employing template-based in-context learning to decompose answers into smaller information units, which enhances the semantic understanding and attribution of both abstractive and extractive answers.

الملخص

The paper proposes a novel approach to post-hoc attribution for long document comprehension, focusing on the factual decomposition of generated answers. The key insights are:

1. Coarse Grained Decomposition (CoG): The authors introduce a question-contextualized decomposition approach that breaks down answers into smaller information units, aligning with the specific context and requirements of the question. This helps identify the information that requires grounding in the source document.

2. Retriever-based Attributors: The authors evaluate various retrieval methods (BM25, GTR, MonoT5) as attributors and observe that on average, question-contextualized coarse-grain decomposition results are comparable, and in some settings, better than using non-decomposed sentences. This demonstrates the efficacy of decomposition for attributions.

3. LLM-based Attributors: The authors examine the use of LLMs (GPT-4, GPT-3.5, LLaMa 2) as post-hoc attributors and observe significant improvements in performance, both empirically and qualitatively, when providing question-contextualized coarse-grain decomposition of answers to the LLMs.

The authors conduct experiments on the Citation Verifiability and QASPER datasets, showcasing the benefits of their proposed approach. They also perform ablation studies to analyze the impact of different decomposers and the classifier used to identify sentences that do not require decomposition.

الإحصائيات

"To paint cast iron, you should first coat it with oil-based primer to create a smooth surface and help the paint adhere."
"You can find cast iron paint on Amazon."

اقتباسات

"Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system."
"Determining when to cite is crucial, as inappropriate or excessive citations can lead to redundancy."
"Existing methods treat answers as singular attributable elements and strive to map them back to long sequence contexts."

الرؤى الأساسية المستخلصة من

Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition

by Pritika Ramu... في arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.17073.pdf

استفسارات أعمق

How can the proposed approach be extended to handle multimodal inputs and outputs, such as incorporating attributions for tables, charts, and images?

To extend the proposed approach for handling multimodal inputs and outputs, several strategies can be implemented. First, the system can be designed to recognize and process various data types, including text, tables, charts, and images. This would involve developing specialized models or modules capable of interpreting the unique structures and semantics of these modalities. For instance, image recognition algorithms can be integrated to extract relevant information from images, while table parsing techniques can be employed to interpret data from tables.
Next, a unified framework for attribution can be established, where each modality is treated as a distinct source of information that contributes to the overall answer. This framework would require the development of multimodal embeddings that can capture the relationships between different types of data. By leveraging techniques such as cross-modal attention mechanisms, the system can learn to associate textual information with corresponding visual data, thereby enhancing the contextual understanding of the answer.
Additionally, the incorporation of visual grounding techniques can help in attributing specific parts of an answer to relevant visual elements. For example, if an answer references a chart, the system should be able to identify which specific data points or trends in the chart support the claims made in the answer. This can be achieved through the use of attention-based models that focus on relevant sections of the visual data while generating attributions.
Finally, user feedback mechanisms can be integrated to refine the attribution process. By allowing users to validate or challenge the attributions made for multimodal content, the system can learn from these interactions and improve its performance over time.

How can the system provide feedback loops to address unsupported claims in the generated answers?

To effectively address unsupported claims in generated answers, the system can implement feedback loops that facilitate continuous learning and improvement. One approach is to incorporate a verification module that assesses the factual accuracy of the claims made in the answers. This module can utilize external knowledge bases or fact-checking APIs to cross-reference the information presented in the answers with reliable sources.
When the verification module identifies unsupported claims, it can trigger a feedback mechanism that prompts the system to either revise the answer or provide additional context. For instance, if a claim lacks sufficient evidence, the system can automatically generate a follow-up question asking for clarification or additional information. This not only enhances the reliability of the answers but also engages users in a more interactive manner.
Moreover, the system can maintain a log of unsupported claims and the corresponding user feedback. This data can be analyzed to identify patterns or common areas of misinformation, allowing the system to adapt its answer generation process accordingly. By employing machine learning techniques, the system can learn to recognize and avoid making unsupported claims in future responses.
Additionally, integrating user ratings or feedback on the quality of attributions can help refine the attribution process. Users can be prompted to indicate whether they found the provided attributions satisfactory, which can inform the system's future performance and improve its ability to ground answers in reliable sources.

What other techniques, beyond in-context learning, can be explored to enhance the decomposition of answers and improve the overall attribution process?

Beyond in-context learning, several other techniques can be explored to enhance the decomposition of answers and improve the overall attribution process. One promising approach is the use of graph-based methods for information extraction. By representing the relationships between different pieces of information as a graph, the system can identify and decompose complex answers into interconnected information units. This allows for a more nuanced understanding of how different facts relate to one another, facilitating more accurate attributions.
Another technique is semantic role labeling (SRL), which can be employed to analyze the roles that different components of an answer play in relation to the question. By identifying the subject, verb, and object within sentences, the system can better understand the context and significance of each information unit, leading to more effective decomposition and attribution.
Hierarchical attention mechanisms can also be utilized to prioritize certain parts of an answer based on their relevance to the question. By applying attention at multiple levels—such as sentence, phrase, and word levels—the system can focus on the most pertinent information, improving the quality of the decomposed units and their corresponding attributions.
Additionally, transfer learning techniques can be leveraged to adapt models trained on large datasets to specific domains or tasks. By fine-tuning pre-trained models on domain-specific data, the system can enhance its understanding of the context and semantics of the answers, leading to better decomposition and attribution.
Lastly, incorporating user-driven annotation can provide valuable insights into the decomposition process. By allowing users to annotate or highlight key parts of answers that require attribution, the system can learn from these annotations to improve its future performance in identifying and decomposing relevant information units. This collaborative approach can significantly enhance the accuracy and reliability of the attribution process.