RELIC: Investigating Large Language Model Responses using Self-Consistency

The paper titled “RELIC: Investigating Large Language Model Responses using Self-Consistency” focuses on addressing the challenge of misinformation generated by Large Language Models (LLMs), which often blend fact and fiction, creating ethical and legal issues​​. The authors conducted a formative study to understand the limitations of existing LLM interfaces, such as OpenAI Playground, and to identify user requirements for an efficient Natural Language Generation (NLG) self-consistency understanding workflow. This study highlighted the need for a clear, visual summary of the model’s confidence, interactive and in-context validations, and an evidence-driven approach for validation​​.

To address these needs, the authors propose RELIC, an interactive system built upon a novel self-consistency-checking algorithm. This system assists LLM users in identifying and steering clear of unreliable information in generated text by understanding the consistency between multiple responses. The system uses a computational pipeline to break down the generated text into atomic claims and employs a natural language inference model to assess the support for each claim from additional samples, providing a measure of the model’s self-consistency​​. The RELIC system is specifically designed to help users identify and correct inaccuracies in generated text by inspecting variations between multiple responses​​. The paper also includes a case study involving ten LLM users to evaluate the usability and usefulness of the RELIC system​​.

Author Caliber:

• Furui Cheng, Vilém Zouhar, Mrinmaya Sachan, Mennatallah El-Assady: Associated with ETH Zurich, Switzerland, a leading university known for its strong research in science and technology.
• Simran Arora: Affiliated with Stanford University, United States, renowned for its research and academic excellence, particularly in technology and engineering fields.
• Hendrik Strobelt: Part of IBM Research, United States, which is well-regarded in the field of computer science research.

Merit:

1. Addresses a significant challenge in AI ethics: misinformation from LLMs.
2. Utilizes a novel self-consistency-checking algorithm.
3. Employs a user-centric approach to understand and improve LLM interfaces.
4. Combines computational techniques with user experience design.
5. Includes a case study with actual users to evaluate the system’s effectiveness.

Commercial Applications:

1. Development of more reliable and transparent AI-driven writing assistants and chatbots.
2. Enhanced fact-checking tools for journalists and researchers relying on LLM-generated content.
3. Improvement of search engine algorithms for better accuracy in information retrieval.
4. Creation of educational tools that leverage LLMs for teaching and learning purposes.
5. Implementation in customer service platforms to improve the accuracy of automated responses.

Findings and Conclusions:

1. Existing LLM interfaces have limitations in conveying self-consistency, necessitating improvements.
2. Users need visual summaries, interactive validations, and evidence-driven approaches for better understanding LLM outputs.
3. The proposed RELIC system effectively assists users in identifying and correcting misinformation in LLM-generated text.
4. The case study demonstrates the system’s usability and practical applicability in real-world scenarios.