RAFFLES: Reasoning-based Attribution of Faults for LLM Systems
RAFFLES uses iterative reasoning to detect faults in LLM systems, achieving 50% accuracy improvement on Who&When dataset.
Key Findings
Methodology
RAFFLES is an offline evaluation architecture incorporating iterative reasoning. It features a central Judge and multiple specialized Evaluators. The Judge systematically identifies faults, while Evaluators assess the quality of candidate faults and the Judge's rationale. This multi-component pipeline allows RAFFLES to pinpoint faults more accurately.
Key Results
- RAFFLES improved accuracy on the Who&When dataset from 18.20% to 27.59% for hand-crafted data and from 38.10% to 51.59% for algorithmically-generated data.
- On the ReasonEval dataset, RAFFLES achieved over 80% accuracy, significantly outperforming baseline methods.
- RAFFLES excels across diverse model families, especially in complex long trajectories, through structured reasoning.
Significance
RAFFLES significantly enhances automated fault detection, reducing the workload of manual review. This research offers new insights into evaluating autonomous systems, better understanding where and when failures originate in complex systems, filling a gap in current evaluation strategies.
Technical Contribution
RAFFLES introduces a structured reasoning Judge-Evaluator architecture, providing new theoretical guarantees and engineering possibilities. Compared to existing methods, RAFFLES excels in detecting faults in complex systems, particularly in long trajectories and multi-component systems.
Novelty
RAFFLES is the first to combine iterative reasoning with a multi-component evaluation framework, accurately pinpointing faults in complex LLM systems. This approach pioneers a new direction in fault detection, fundamentally differing from existing single-pass evaluation methods.
Limitations
- RAFFLES may encounter performance bottlenecks when dealing with extremely long trajectories, especially with limited computational resources.
- For certain specific fault types, RAFFLES may require further optimization to improve detection accuracy.
Future Work
Future research could explore RAFFLES' application in other types of multi-component systems and optimize its performance under extreme conditions. Additionally, integrating real-time evaluation techniques could further enhance its practicality.
AI Executive Summary
As large language model (LLM) systems grow in complexity, identifying when and where these systems fail becomes increasingly challenging. Existing evaluation methods often rely on simple metrics and manual review, struggling to cope with the complexity of multi-component systems. RAFFLES introduces a novel solution by incorporating iterative reasoning and a multi-component evaluation framework.
At the core of RAFFLES is a central Judge and multiple specialized Evaluators. The Judge identifies faults within the system, while Evaluators assess the quality of candidate faults and the rationale provided by the Judge. This structured reasoning process allows RAFFLES to accurately pinpoint faults in complex long trajectories.
Experimental results show that RAFFLES significantly outperforms baseline methods on the Who&When and ReasonEval datasets, particularly in complex multi-component systems. By reducing the workload of manual review, RAFFLES offers new possibilities for automated fault detection. However, RAFFLES still faces performance challenges when dealing with extremely long trajectories, and future research will focus on optimizing its performance under extreme conditions.
Deep Analysis
Background
As LLM systems evolve, evaluating their performance and detecting faults becomes increasingly important. Traditional methods often rely on simple metrics and manual review, struggling to handle complex multi-component systems. Recently, the emergence of language-conditioned agent architectures like ReAct and Toolformer has enabled systems to plan, reason, and act over long horizons, but also introduced new failure modes.
Core Problem
Existing evaluation strategies are primarily confined to isolated metrics, struggling to detect faults in long trajectories. This leaves manual debugging as the only method, which is time-consuming and costly. In multi-component systems, a single subtle error can quickly cascade, causing a ripple effect of unexpected behaviors. Thus, automated fault attribution has become an emerging focus for the evaluation community.
Innovation
RAFFLES introduces a structured reasoning Judge-Evaluator architecture, capable of accurately pinpointing faults in complex LLM systems. Unlike existing single-pass evaluation methods, RAFFLES combines iterative reasoning with a multi-component evaluation framework, better understanding where and when failures originate in complex systems.
Methodology
- �� RAFFLES uses a central Judge to identify faults and multiple Evaluators to assess the quality of candidate faults.
- �� The Judge provides structured reasoning rationales, and Evaluators assess their soundness and provide confidence scores.
- �� Through iterative reasoning, the Judge continuously refines its evaluation strategy until a decisive fault is determined.
Experiments
Experiments were conducted on the Who&When and ReasonEval datasets, using various models for evaluation. RAFFLES significantly outperformed baseline methods on these datasets, particularly in complex long trajectories. The experimental design included comparisons with multiple baseline methods and performance evaluations across different model families.
Results
RAFFLES improved accuracy on the Who&When dataset from 18.20% to 27.59% for hand-crafted data and from 38.10% to 51.59% for algorithmically-generated data. On the ReasonEval dataset, RAFFLES achieved over 80% accuracy, significantly outperforming baseline methods.
Applications
RAFFLES can be used for fault detection in complex multi-component systems, reducing the workload of manual review. Its superior performance in long trajectories makes it suitable for scenarios requiring high-precision fault detection, such as autonomous driving and intelligent customer service.
Limitations & Outlook
RAFFLES may encounter performance bottlenecks when dealing with extremely long trajectories, especially with limited computational resources. Additionally, for certain specific fault types, RAFFLES may require further optimization to improve detection accuracy.
Plain Language Accessible to non-experts
Imagine you're working in a complex factory with many machines and workers. Each worker has their own task, but if one worker makes a mistake, it can affect the entire production line. RAFFLES is like a smart manager who can quickly find out which worker made a mistake and when, and tell them how to fix it. This way, the factory can run more efficiently without spending too much time figuring out where the problem is.
ELI14 Explained like you're 14
Imagine you're playing a super complex video game with lots of characters, each with their own tasks. Sometimes, one character might make a mistake, causing the whole game to go wrong. RAFFLES is like a super smart game assistant that helps you find out which character messed up and when, and tells you how to fix it. This way, you can beat the game faster without getting stuck in one place!
Glossary
RAFFLES
RAFFLES is an offline evaluation architecture for fault detection in LLM systems, incorporating iterative reasoning and multi-component evaluation.
Used to identify and assess faults in complex systems.
Judge
The central component in RAFFLES responsible for identifying system faults and providing reasoning rationales.
Used for preliminary fault identification in the RAFFLES framework.
Evaluator
Components in RAFFLES responsible for assessing the quality of candidate faults identified by the Judge.
Used to evaluate the soundness and confidence of faults.
Who&When dataset
A benchmark dataset for fault attribution in multi-agent systems, consisting of agent logs and fault pairs.
Used to test RAFFLES' performance in multi-component systems.
ReasonEval dataset
A benchmark dataset for diagnosing step-level mathematical reasoning errors, containing annotations of reasoning chains.
Used to validate RAFFLES' effectiveness in complex reasoning chains.
Open Questions Unanswered questions from this research
- 1 How to improve RAFFLES' performance in extremely long trajectories? Current methods perform poorly with limited resources.
- 2 How to enhance RAFFLES' accuracy for specific fault types? Further optimization is needed.
Applications
Immediate Applications
Autonomous Driving Fault Detection
RAFFLES can be used to detect faults in autonomous driving systems, reducing manual intervention and enhancing safety.
Long-term Vision
Intelligent Customer Service Optimization
With RAFFLES, intelligent customer service systems can quickly identify and correct errors, improving user experience.
Abstract
The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that currently exist today are limited in that they often focus on simple metrics, end-to-end outcomes, and are dependent on the perspectives of humans. In order to match the increasing complexity of these many component systems, evaluation frameworks must also be able to reason, probe, iterate, and understand the nuanced logic passing through these systems. In this paper, we present RAFFLES, an offline evaluation architecture that incorporates iterative reasoning. Specifically, RAFFLES operates as an iterative, multi-component pipeline, using a central Judge to systematically identify faults and a set of specialized Evaluators to assess the quality of the candidate faults as well as rationales of the Judge. We evaluated RAFFLES with several benchmarks - the Who&When dataset to identify step-level faults in multi-agent systems and the ReasonEval datasets to diagnose step-level mathematical reasoning errors. RAFFLES outperforms strong baselines, achieving an accuracy of over 20% and 50% on the Who&When Hand-Crafted and Algorithmically-Generated datasets, and over 80% on the ReasonEval datasets. These results demonstrate a key step towards introducing automated fault detection for autonomous systems over labor-intensive manual review.