Paper: arXiv 2512.03107

Authors: Mainak Singha

Abstract

Large language models (LLMs) produce fluent but unsupported answers - hallucinations - limiting safe deployment in high-stakes domains. We propose ECLIPSE, a framework that treats hallucination as a mismatch between a model’s semantic entropy and the capacity of available evidence. We combine entropy estimation via multi-sample clustering with a novel perplexity decomposition that measures how models use retrieved evidence. We prove that under mild conditions, the resulting entropy-capacity objective is strictly convex with a unique stable optimum. We evaluate on a controlled financial question answering dataset with GPT-3.5-turbo (n=200 balanced samples with synthetic hallucinations), where ECLIPSE achieves ROC AUC of 0.89 and average precision of 0.90, substantially outperforming a semantic entropy-only baseline (AUC 0.50). A controlled ablation with Claude-3-Haiku, which lacks token-level log probabilities, shows AUC dropping to 0.59 with coefficient magnitudes decreasing by 95% - demonstrating that ECLIPSE is a logprob-native mechanism whose effectiveness depends on calibrated token-level uncertainties. The perplexity decomposition features exhibit the largest learned coefficients, confirming that evidence utilization is central to hallucination detection. We position this work as a controlled mechanism study; broader validation across domains and naturally occurring hallucinations remains future work.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces a novel information-theoretic framework with proven convexity and a structured perplexity decomposition, indicating high mathematical complexity. However, the empirical validation is limited to a small, synthetic dataset (n=200) without backtests or real-world implementation details, placing it in the exploratory ‘Lab Rats’ quadrant.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Detect AI Hallucinations in Finance"] --> B["Methodology:<br>ECLIPSE Framework"]
  B --> C["Input Data:<br>Financial QA Dataset<br>(n=200 samples)"]
  C --> D["Computational Process:<br>Multi-sample Clustering<br>+ Perplexity Decomposition"]
  D --> E["Key Finding 1:<br>ROC AUC 0.89<br>(vs 0.50 baseline)"]
  D --> F["Key Finding 2:<br>92% Hallucination Rate Cut"]
  D --> G["Key Finding 3:<br>Logprob Dependency Confirmed<br>(AUC drops to 0.59 w/o tokens)"]

Cited References (31)

  1. The Internal State of an LLM Knows When It’s Lying (2023)
  2. Chat L aw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases (2023) — link
  3. On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models? (2022)
  4. On the Foundations of Noise-Free Selective Classification (2010)
  5. Dropout as a B ayesian Approximation: Representing Model Uncertainty in Deep Learning (2016)
  6. Selective Classification for Deep Neural Networks (2017)
  7. On Calibration of Modern Neural Networks (2017)
  8. REALM : Retrieval-Augmented Language Model Pre-Training (2020)
and 23 more
  1. The Curious Case of Neural Text Degeneration (2020)
  2. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2023) — link
  3. Atlas: Few-shot Learning with Retrieval Augmented Language Models (2023)
  4. Survey of Hallucination in Natural Language Generation (2023)
  5. Language Models (Mostly) Know What They Know (2022) — link
  6. Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs (2024) — link
  7. Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation (2023)
  8. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020)
  9. Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models (2024)
  10. Evaluating Verifiability in Generative Search Engines (2023)
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  12. On Faithfulness and Factuality in Abstractive Summarization (2020)
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  14. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods (1999)
  15. Large Language Models Can Be Easily Distracted by Irrelevant Context (2023)
  16. Retrieval Augmentation Reduces Hallucination in Conversation (2021)
  17. Large Language Models Encode Clinical Knowledge (2023)
  18. Investigating Selective Prediction Approaches Across Several Tasks in IID , OOD , and Adversarial Settings (2022)
  19. Bloomberg GPT : A Large Language Model for Finance (2023) — link
  20. Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs (2024)
  21. A Theory of Usable Information Under Computational Constraints (2020) — link
  22. Making Retrieval-Augmented Language Models Robust to Irrelevant Context (2024)
  23. Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models (2023) — link

0 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.