Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs

• URL: http://arxiv.org/abs/2505.23299v1
• Date: Thu, 29 May 2025 09:50:56 GMT
• Title: Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs
• Authors: Julia Belikova, Konstantin Polev, Rauf Parchiev, Dmitry Simakov,
• Abstract summary: Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly deployed in industry applications.<n>Their reliability remains hampered by challenges in detecting hallucinations.<n>This paper addresses the bottleneck of data annotation by investigating the feasibility of reducing training data requirements.
• Score: 1.6332728502735252
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly deployed in industry applications, yet their reliability remains hampered by challenges in detecting hallucinations. While supervised state-of-the-art (SOTA) methods that leverage LLM hidden states -- such as activation tracing and representation analysis -- show promise, their dependence on extensively annotated datasets limits scalability in real-world applications. This paper addresses the critical bottleneck of data annotation by investigating the feasibility of reducing training data requirements for two SOTA hallucination detection frameworks: Lookback Lens, which analyzes attention head dynamics, and probing-based approaches, which decode internal model representations. We propose a methodology combining efficient classification algorithms with dimensionality reduction techniques to minimize sample size demands while maintaining competitive performance. Evaluations on standardized question-answering RAG benchmarks show that our approach achieves performance comparable to strong proprietary LLM-based baselines with only 250 training samples. These results highlight the potential of lightweight, data-efficient paradigms for industrial deployment, particularly in annotation-constrained scenarios.

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