LLM-as-an-Interviewer: A New Approach to Assessing the Abilities of Language Models | Large language models tutorial python pdf | Best large language models | Llm meaning software | Turtles AI

A new framework called LLM-as-an-Interviewer introduces a dynamic methodology for evaluating advanced language models (LLMs), overcoming the limitations of static techniques. By iteratively adapting to the model’s responses, this methodology provides a more precise assessment of its capabilities in realistic scenarios.

Key Points:

• Dynamic adaptation: The framework modifies questions and provides iterative feedback to improve answers.
• Multi-turn approach: Allows to evaluate the model’s ability to interact in multiple dialogue rounds.
• Bias reduction: Minimizes the influence of verbosity and self-assessment on performance.
• Contamination mitigation: Clear distinction between genuine ability and training artifacts.

The landscape of evaluating large language models (LLMs) has traditionally been dominated by static approaches, which, while useful, have significant gaps in analyzing the real capabilities of these models. The new LLM-as-an-Interviewer framework, developed by a team of researchers from prestigious universities such as KAIST, Stanford, Carnegie Mellon and Contextual AI, offers an innovative and dynamic solution to address these critical issues. Unlike traditional static methods, which rely on immutable benchmarks, the new approach mimics human interactions, creating adaptive questions and providing real-time feedback. This feature allows for a more nuanced and realistic evaluation, capable of capturing the essence of the models’ skills, including adaptability and iterative improvement.

The framework process is divided into three main phases: problem formulation, feedback and review, and finally generation of follow-up questions. During the first phase, personalized questions are constructed, calibrated to challenge the model’s capabilities. The system then provides detailed feedback and uses incomplete or inaccurate answers to ask new questions, creating a continuous cycle of learning and testing. At the end of the interaction, a comprehensive report is produced that summarizes the results, error analyses, and potential areas for improvement, providing a valuable tool for analyzing the applicability of models in practical contexts.

Experiments conducted on specific datasets such as MATH and DepthQA demonstrate the effectiveness of this methodology. In the case of MATH, which assesses mathematical reasoning, advanced models such as GPT-4 improved their accuracy from 72% to 84% thanks to iterative feedback, highlighting the positive impact of the dynamic approach. Similarly, DepthQA, which is oriented towards open-ended questions, highlighted the framework’s ability to uncover knowledge gaps and drive significant improvements in model answers. For example, GPT-3.5 recorded a 25% increase in the adaptability metric, showing a clear progression following the iterative interaction.

One of the most innovative aspects of the framework is the reduction of biases that have historically affected LLM assessments. The verbosity bias, i.e. the preference for long answers, decreases as the model interacts with the system, leading to a less significant correlation between the length of the answers and the assigned scores. In parallel, the self-improvement bias, which leads models to favorably evaluate their own answers, is effectively mitigated thanks to comparative and dynamic feedback.

Finally, the framework addresses the critical issue of data contamination. By continuously modifying the questions and introducing new cues, it is able to distinguish between genuine model skills and artifacts resulting from training on already known data sets. This approach has proven particularly effective in identifying and correcting anomalies related to overlapping training data, ensuring a fairer and more accurate assessment of the real capabilities of the models.

With its iterative design and focus on a realistic simulation of human interactions, LLM-as-an-Interviewer represents a fundamental step towards a more precise and meaningful assessment of LLMs.

This methodology promises to change evaluation standards, supporting the development of increasingly robust and reliable linguistic models.