Investigating the Reliability of Large Language Model Responses through Self-Consistency Analysis

Główne pojęcia

Leveraging the self-consistency of multiple language model samples to assess the reliability and factuality of generated text.

Streszczenie

The paper proposes an interactive system called RELIC that helps users verify and steer the accuracy of text generated by large language models (LLMs). The key idea is to measure the semantic-level consistency between multiple samples generated by the same LLM to assess its confidence in individual claims.
The formative study found that users struggle to comprehend the reliability of LLM responses using existing interfaces that focus on token-level probabilities. Users require a clear visual summary of the model's confidence at the semantic level, as well as interactive tools to validate specific claims by examining supporting and contradicting evidence across samples.
To address these needs, the RELIC system breaks down the generated text into atomic claims, generates questions for each claim, and retrieves answers from multiple samples. It then clusters semantically equivalent answers and visualizes the proportions of supporting, contradicting, and neutral samples for each claim. Users can interactively select claims, inspect the evidence, and edit the text to conduct what-if analyses.
A user study with 10 participants demonstrates the effectiveness of the RELIC system in helping users better verify the reliability of LLM responses. The results highlight the importance of providing users with transparency into the model's confidence at the semantic level and enabling interactive exploration of the supporting and contradicting evidence.

Statystyki

Don Featherstone was born in 1920, 1933, 1932, 1927, and 1937.
Don Featherstone is best known for creating the iconic pink plastic lawn flamingo.
Don Featherstone worked at Union Products for over 40 years.

Cytaty

"If I have to choose, I will pick 1936 since over half of the samples support this number."
"The content seems coherent and reasonable."

Kluczowe wnioski z

RELIC

by Furu... o arxiv.org 04-05-2024

https://arxiv.org/pdf/2311.16842.pdf

Głębsze pytania

How can we extend the self-consistency analysis to other types of language model outputs beyond long-form text, such as dialogue or code generation?

To extend the self-consistency analysis to other types of language model outputs, such as dialogue or code generation, we can adapt the same principles used for long-form text analysis. Here are some ways to achieve this:

Dialogue Generation: For dialogue outputs, we can break down the conversation into atomic claims or key statements. By comparing multiple samples of dialogues generated by the language model, we can assess the consistency in responses to the same prompts. Users can then verify the reliability of the dialogue by examining the variations in responses and identifying any inconsistencies.

Code Generation: In the case of code generation, we can analyze the generated code snippets as atomic claims. By comparing multiple samples of code generated by the model for the same task or prompt, we can evaluate the self-consistency of the code outputs. Users can then verify the accuracy and reliability of the code by examining the variations in the generated code snippets.

Visualizations and Interactions: Similar to the Keyword Annotation and Evidence View in the RELIC system for text analysis, we can develop visualizations and interactions tailored to dialogue or code outputs. This could include highlighting key phrases or functions in the code, clustering similar code snippets, and providing evidence for the correctness of the generated code.

By adapting the self-consistency analysis approach to different types of language model outputs, we can help users verify the reliability and accuracy of dialogue or code generated by the models.

What are the potential biases or limitations of using self-consistency as the primary metric for assessing the reliability of language model responses?

While self-consistency can be a valuable metric for assessing the reliability of language model responses, there are potential biases and limitations to consider:

Limited Scope: Self-consistency may not capture all aspects of reliability. It focuses on the internal consistency of the model's outputs but may overlook external factors such as factual accuracy, context relevance, or ethical considerations.

Confirmation Bias: Relying solely on self-consistency could lead to confirmation bias, where users only seek evidence that supports their initial beliefs or expectations. This could result in overlooking contradictory information that may be crucial for verifying the accuracy of the responses.

Semantic Ambiguity: Language models may generate responses that are semantically equivalent but syntactically different. Self-consistency analysis may struggle to identify these variations, leading to potential inaccuracies in assessing the reliability of the responses.

Overlooking Context: Self-consistency analysis may not consider the broader context in which the responses are generated. Factors such as the prompt, user intent, or domain-specific knowledge may influence the reliability of the responses but are not fully captured by self-consistency alone.

Model Biases: Language models themselves may have inherent biases that can impact the self-consistency of their responses. These biases could influence the consistency of outputs and affect the overall reliability assessment.

Considering these biases and limitations, it is essential to complement self-consistency analysis with other verification methods and tools to ensure a comprehensive assessment of the reliability of language model responses.

How can we integrate the RELIC system with other fact-checking or knowledge verification tools to provide a more comprehensive solution for ensuring the trustworthiness of language model outputs?

Integrating the RELIC system with other fact-checking or knowledge verification tools can enhance the overall solution for ensuring the trustworthiness of language model outputs. Here are some ways to achieve this integration:

Cross-Validation: RELIC can be integrated with existing fact-checking platforms or knowledge databases to cross-validate the information generated by the language model. By comparing the model's outputs with verified facts from reliable sources, users can gain a more comprehensive understanding of the trustworthiness of the responses.

External Verification: The RELIC system can provide links or references to external fact-checking websites or databases where users can further verify the information. By incorporating external verification sources, users can validate the accuracy of the model's responses beyond self-consistency analysis.

Real-Time Updates: Integrate RELIC with real-time fact-checking services that continuously update information and verify the accuracy of statements. This integration ensures that users have access to the most up-to-date and verified information when assessing the reliability of language model outputs.

User Feedback Mechanism: Implement a user feedback mechanism within RELIC that allows users to report inaccuracies or provide additional evidence for verification. This user-generated data can supplement the self-consistency analysis and enhance the overall trustworthiness assessment.

Machine Learning Models: Incorporate machine learning models for sentiment analysis or bias detection to complement the self-consistency analysis provided by RELIC. By leveraging multiple verification tools, users can gain a more holistic view of the reliability and trustworthiness of language model outputs.

By integrating RELIC with other fact-checking and knowledge verification tools, users can benefit from a comprehensive solution that enhances the trustworthiness and accuracy of language model responses.