MARAGS: A Multi-Adapter Retrieval Augmented Generation System for Question Answering (2nd Place in KDD Cup 2024, Task 1)

Concepts de base

This paper introduces MARAGS, a multi-adapter retrieval augmented generation system that effectively addresses the challenges of multi-task question answering, achieving competitive results in the KDD Cup 2024 CRAG competition.

Résumé

Bibliographic Information:

DeHaven, M. (2024). MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering. In Proceedings of the KDD Cup 2024 (pp. TBD).

Research Objective:

This paper presents MARAGS, a novel multi-adapter system designed for multi-task retrieval augmented generation (RAG) in question answering. The research aims to address the limitations of traditional RAG systems in handling diverse question types, dynamic answers, and varying topic popularity, as highlighted by the CRAG benchmark.

Methodology:

MARAGS utilizes a pipeline approach involving webpage processing, API call generation, candidate ranking, and retrieval augmented generation. Web pages are segmented using BeautifulSoup4, while API calls are generated using a LoRa adapter trained on Llama 3. Candidate ranking employs a cross-encoder model, and final answer generation leverages Llama 3 8B with task-specific LoRa adapters. To mitigate hallucinations, the training data is relabeled to encourage "I don't know" responses when relevant information is unavailable.

Key Findings:

The paper demonstrates the effectiveness of MARAGS in handling various question answering tasks, achieving 2nd place in Task 1 and 3rd place in Task 2 of the KDD Cup 2024 CRAG competition. The results highlight the benefits of using a multi-adapter approach, task-specific fine-tuning, and strategies to reduce hallucinations. The study also identifies challenges related to specific domains (e.g., finance), question dynamism, and topic popularity.

Main Conclusions:

MARAGS offers a promising solution for building robust and accurate RAG systems for complex question answering tasks. The authors emphasize the importance of addressing hallucinations in LLM-based systems and propose techniques to mitigate this issue. The paper contributes to the ongoing research on improving the reliability and trustworthiness of AI systems for real-world applications.

Significance:

This research significantly contributes to the field of natural language processing, particularly in the area of question answering using RAG. The proposed MARAGS system and the insights gained from its evaluation provide valuable guidance for developing more effective and reliable question answering systems.

Limitations and Future Research:

The study acknowledges limitations in handling certain question types and domains, suggesting further research to address these challenges. Future work could explore larger language models, advanced techniques for catastrophic forgetting prevention, and improved methods for handling dynamic and less popular topics.

Stats

Our system achieved 2nd place for Task 1 as well as 3rd place on Task 2.
TF-IDF Accuracy: 0.2740, CRAG Score: -0.110
Biencoder Accuracy: 0.310, CRAG Score: -0.132
Cross-encoder Accuracy: 0.328, CRAG Score: -0.116
Ensemble (mean rank) Accuracy: 0.308, CRAG Score: -0.128
Llama 3 8B Accuracy: 0.328, Hallucination Rate: 0.4440, CRAG Score: -0.1160
Llama 3 8B - LoRa Accuracy: 0.398, Hallucination Rate: 0.602, CRAG Score: -0.204
Llama 3 8B - LoRa (relabeled) Accuracy: 0.242, Hallucination Rate: 0.056, CRAG Score: 0.186

Citations

"With the rising capabilities of LLMs, increasingly their outputs are taken at face value, despite the known issue of hallucinations."
"This has led to high profile incidents causing concern with their use."
"The CRAG score aims to punish hallucinated answers and encourages returning missing answers, equivalent to returning 'i don’t know' from the model, by giving scores of 1, 0, and -1 to correct, missing, and hallucinated answers respectively."

Idées clés tirées de

MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering

by Mitchell DeH... à arxiv.org 11-05-2024

https://arxiv.org/pdf/2409.03171.pdf

Questions plus approfondies

How can the performance of RAG systems be further improved in specialized domains like finance, where numerical reasoning and understanding of non-textual data are crucial?

Answer:
Improving RAG system performance in specialized domains like finance, particularly in areas requiring numerical reasoning and understanding of non-textual data, demands a multi-faceted approach:

Specialized Retrieval:

Domain-Specific Knowledge Graphs: Integrate specialized financial knowledge graphs containing relationships between financial entities, concepts, and events. This provides structured data for enhanced reasoning.
Financial Data Sources: Incorporate retrieval from financial news sources, SEC filings, company websites, and market data APIs to access up-to-date and comprehensive financial information.
Multimodal Retrieval: Develop retrieval methods capable of handling financial charts, tables, and graphs, potentially leveraging computer vision techniques to extract relevant information.

Enhanced Numerical Reasoning:

Numerical Representation Learning: Utilize techniques like embedding financial data into vector spaces that capture numerical relationships and enable mathematical operations.
Fine-tuning on Financial Data: Train LLMs on large datasets of financial text and code to improve their understanding of financial language, calculations, and reasoning patterns.
Integration with Numerical Solvers: Explore incorporating external numerical solvers or calculators into the RAG pipeline to handle complex financial calculations accurately.

Addressing Non-Textual Data:

Multimodal LLMs: Leverage multimodal LLMs capable of processing both text and visual information, enabling them to understand charts, graphs, and tables directly.
Data Augmentation: Generate synthetic financial data, including textual descriptions of charts and tables, to augment training data and improve the model's ability to handle non-textual information.

Evaluation and Refinement:

Domain-Specific Benchmarks: Develop evaluation benchmarks specifically designed to assess RAG system performance on financial tasks involving numerical reasoning and non-textual data.
Human-in-the-Loop Evaluation: Incorporate financial experts in the evaluation process to assess the accuracy, reliability, and interpretability of the system's outputs.

By addressing these challenges, we can develop more robust and reliable RAG systems for finance and other specialized domains that demand advanced numerical and non-textual data understanding.

While reducing hallucinations is essential, how can we strike a balance to prevent catastrophic forgetting, ensuring that models retain and utilize their pre-trained knowledge effectively?

Answer:
Balancing hallucination reduction with the prevention of catastrophic forgetting in RAG systems is crucial for maintaining both reliability and the breadth of pre-trained knowledge. Here are some strategies:

Selective retraining:

Identify and retrain on problematic examples: Instead of retraining the entire model, focus on examples where hallucinations occur or where catastrophic forgetting is evident.
Use smaller learning rates: When retraining on specific examples, employ smaller learning rates to fine-tune the model without drastically overwriting existing knowledge.

Regularization techniques:

Knowledge distillation: Train a smaller, specialized model on the outputs of the larger pre-trained model, encouraging knowledge transfer while reducing the risk of catastrophic forgetting.
Elastic weight consolidation (EWC): Assign importance weights to parameters based on their relevance to pre-trained tasks, making it harder to overwrite crucial knowledge during fine-tuning.

Hybrid architectures:

Combine pre-trained and specialized models: Utilize a pre-trained LLM for general knowledge and language understanding, and integrate it with a smaller, specialized model fine-tuned on domain-specific data.
Modular design: Develop modular architectures where specific components are responsible for different aspects of the task, allowing for targeted updates without affecting other modules.

Continual learning approaches:

Experience replay: Store a subset of previous training data and periodically retrain the model on it, reinforcing previously learned knowledge.
Dynamically expanding networks: Explore architectures that can dynamically grow and adapt to new information without overwriting existing knowledge.

Robust evaluation and monitoring:

Develop metrics for both hallucination and forgetting: Track both hallucination rates and performance on previously learned tasks to ensure a balanced approach.
Continuously monitor and analyze model outputs: Regularly review model generations for signs of hallucination or forgetting, and adjust training strategies accordingly.

By implementing these strategies, we can mitigate the risks of catastrophic forgetting while effectively reducing hallucinations, ensuring that RAG systems retain their pre-trained knowledge base while adapting to new domains and tasks.

Considering the increasing complexity and capabilities of AI systems like MARAGS, how can we develop robust evaluation metrics that go beyond simple accuracy and address aspects like fairness, bias, and potential societal impact?

Answer:
As AI systems like MARAGS become more sophisticated, evaluating them solely on accuracy becomes insufficient. We need robust evaluation metrics that encompass broader ethical and societal considerations:

Fairness and Bias Detection:

Group Fairness Metrics: Measure performance disparities across different demographic groups (e.g., gender, race, location) to identify and mitigate biases in model outputs.
Counterfactual Analysis: Assess how model predictions change when sensitive attributes are altered, revealing potential biases in decision-making.
Bias Audits: Conduct independent audits of training data and model behavior to identify and address potential sources of bias.

Explainability and Interpretability:

Rationale Generation: Develop methods for AI systems to provide human-understandable explanations for their outputs, increasing transparency and trust.
Feature Importance Analysis: Identify the most influential factors driving model predictions, allowing for scrutiny of potential biases and unfairness.
Visualization Techniques: Utilize visualizations to illustrate model decision boundaries and highlight potential areas of concern regarding fairness and bias.

Societal Impact Assessment:

Downstream Impact Analysis: Evaluate the potential consequences of AI system deployment on individuals, communities, and society as a whole.
Stakeholder Engagement: Involve diverse stakeholders, including ethicists, social scientists, and affected communities, in the evaluation and development process.
Long-Term Monitoring: Establish mechanisms for ongoing monitoring of AI systems after deployment to track their real-world impact and address any unforeseen consequences.

Robustness and Reliability:

Adversarial Testing: Evaluate system resilience to adversarial attacks, ensuring they are robust to malicious manipulation and unexpected inputs.
Uncertainty Quantification: Develop methods for AI systems to express uncertainty in their predictions, enabling more informed decision-making and risk assessment.
Out-of-Distribution Generalization: Assess how well models perform on data significantly different from their training distribution, ensuring they are reliable in real-world scenarios.

By incorporating these multifaceted evaluation metrics, we can move beyond simple accuracy and develop a more comprehensive understanding of AI systems' capabilities, limitations, and potential impact on individuals and society. This holistic approach is crucial for ensuring that AI technologies are developed and deployed responsibly and ethically.