Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics

Temel Kavramlar

LLM-based classifiers effectively detect hallucination and coverage errors in retrieval augmented generation for controversial topics.

Özet

The article explores error detection methods in Large Language Models (LLMs) used in chatbots for controversial topics. It introduces the NPOV Response Task, focusing on response generation from provided perspectives. Methods like ROUGE, salience, and LLM-based classifiers are evaluated for detecting errors. Synthetic error datasets are used to train and evaluate these methods. Results show that classifiers trained on synthetic errors perform well, with ROUGE being a strong baseline method. Salience is effective for word-level error detection, capturing semantics better than ROUGE in some cases.

İstatistikler

Our results demonstrate that LLM-based classifiers achieve high error detection performance.
ROC AUC scores of 95.3% for hallucination and 90.5% for coverage error detection were achieved.
Even without access to annotated data, good results were obtained on hallucination (84.0%) and coverage error (85.2%) detection.
Classifier performance improves with more training data, especially on full organic errors.
Salience performs equally to or better than ROUGE for detecting both hallucinated words and uncovered words.

Alıntılar

"Large Language Models have achieved state-of-the-art performance but struggle with factuality and bias."
"Our work focuses on response generation after pro and con arguments are provided to an LLM."
"Classifier performance improves with more training data."
"Salience is effective for word-level error detection."

Önemli Bilgiler Şuradan Elde Edildi

Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics

by Tyler A. Cha... : arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.08904.pdf

Daha Derin Sorular

How can we ensure diverse perspectives are included in databases used by LLMs?

Incorporating diverse perspectives into databases utilized by Large Language Models (LLMs) is crucial to mitigate biases and provide a more comprehensive view on controversial topics. Here are some strategies to ensure diversity:

Curate Diverse Sources: Utilize a wide range of sources representing various viewpoints, ideologies, cultures, and demographics. This includes academic papers, reputable news outlets, opinion pieces from different authors, and community forums.

Expert Consultation: Involve experts from different fields or backgrounds to review the content for accuracy and inclusivity. Their insights can help identify gaps in representation and suggest additional perspectives.

Community Engagement: Engage with communities directly affected by the topics discussed in the database. Incorporating their lived experiences and opinions can offer valuable insights that might be overlooked otherwise.

Regular Updates: Continuously update the database to reflect evolving societal norms, emerging research findings, and changing perspectives on controversial issues.

Bias Detection Algorithms: Implement algorithms that flag potential biases or lack of diversity in the content of the database. These tools can help maintain balanced representations across all viewpoints.

Transparency & Accountability: Maintain transparency about the sources used in curating the database and establish mechanisms for accountability if any biases or gaps are identified.

What ethical considerations should be taken into account when developing chatbots for controversial topics?

When developing chatbots for discussing controversial topics, several ethical considerations must be prioritized:

Neutrality & Fairness: Ensure that chatbot responses remain neutral without promoting specific agendas or biased viewpoints.

Informed Consent & Privacy: Obtain user consent before engaging them in conversations on sensitive subjects; safeguard user privacy by not storing personal information shared during interactions.

Avoiding Harmful Content: Prevent dissemination of harmful misinformation or extremist views through stringent content moderation practices.

4 .Diversity & Inclusivity: Represent diverse voices within chatbot responses to avoid reinforcing stereotypes or marginalizing certain groups.
5 .Transparency: Clearly disclose that users are interacting with an AI-powered system rather than a human being; clarify limitations regarding advice-giving capabilities on complex issues like mental health or legal matters.
6 .User Empowerment: Provide resources for further reading or access to support services when discussing emotionally charged topics such as suicide prevention or domestic violence.
7 .Continuous Monitoring: Regularly monitor chatbot interactions for inappropriate language use, hate speech propagation, or other forms of harmful behavior; implement swift corrective actions when necessary.

How can the computational footprint of models like the NPOV Response Generator be reduced while maintaining accuracy?

Reducing computational footprint while preserving model accuracy is essential for efficient deployment of models like NPOV Response Generators:
1 .Model Pruning: Identify redundant parameters within the model architecture using techniques like magnitude-based pruning or structured pruning without compromising performance significantly.
2 .Quantization Techniques:
Implement quantization methods such as weight quantization (e.g., INT8), which reduce memory requirements without sacrificing model precision substantially.
3 .Knowledge Distillation:
Utilize knowledge distillation where a smaller student network learns from a larger teacher network's outputs but with fewer parameters—reducing overall complexity while retaining performance levels.
4 .Low-Rank Approximation:
Apply low-rank approximation methods to compress weight matrices effectively while maintaining representational capacity through matrix factorization techniques like Singular Value Decomposition (SVD).
5 .Sparsity Induction:
Introduce sparsity constraints during training phases so that many weights become zero post-training—leading to reduced memory usage during inference via sparse matrix operations optimizations
6 ．

Model Compression:
Employ advanced compression algorithms such as Huffman coding,
arithmetic encoding,
etc.,
tailored specifically
-for natural language processing tasks
-to minimize storage requirements without compromising output quality
7 ．
- Hardware Optimization:
Optimize hardware configurations
-by leveraging specialized accelerators
-like GPUs,
TPUs,
FPGAs designed explicitly
-for neural network computations
enhancing speed efficiency
By implementing these strategies judiciously based on specific use cases' requirements，the computational overhead associated with models like NPOV Response Generators can be significantly reduced while ensuring optimal performance levels maintained at inference time