CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation

• URL: http://arxiv.org/abs/2606.32012v1
• Date: Tue, 30 Jun 2026 17:46:09 GMT
• Title: CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation
• Abstract summary: Uncertainty estimation has been a long-standing challenge in AI models.<n>We propose CoMet, an MLLM uncertainty estimation method by decomposing uncertainty into a context-specific term and a multiplicity-specific term.<n>We train a lightweight post-hoc uncertainty module to estimate these quantities, which enables efficient uncertainty estimation without autoregressive answer generation or repeated sampling.
• Score: 45.629365817272536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty estimation has been a long-standing challenge in AI models; it amounts to "knowing what you don't know," and metacognition is notoriously difficult even for humans (cf. the Dunning-Kruger effect). Although it is still far from solved even in simpler classification systems, tackling it in multimodal large language models (MLLMs) is becoming increasingly important. Within MLLMs, uncertainty can stem from any of the diverse sources as well as from their relationships, and further can stem from the unbounded answers in the open-ended setting. To tackle the issues, we propose CoMet, an MLLM uncertainty estimation method by decomposing uncertainty into a context-specific term and a multiplicity-specific term. The former captures ambiguity induced by the given context (e.g., task or prompt), while the latter captures how many plausible answers determined by the context remain compatible with the given input. We train a lightweight post-hoc uncertainty module to estimate these quantities, which enables efficient uncertainty estimation without autoregressive answer generation or repeated sampling. Experiments on various open-ended multimodal benchmarks, hallucination detection, and multiple-choice visual question answering benchmarks show that CoMet consistently improves uncertainty estimation over existing baselines while remaining efficient in practice. Code is available at https://github.com/princetonvisualai/comet_uncertainty

Related papers

• Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models [44.59799077650502]
  We propose a simple yet effective self-assessment method for uncertainty quantification in large language models.<n>Our approach groups sampled generations into semantically distinct clusters, converts them into answer options in a structured multiple-choice question, and uses the probability assigned by the model to each option as a confidence estimate.
  arXiv  Detail & Related papers  (2026-06-02T16:25:54Z)
• Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty [47.95943057892318]
  Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight.<n>We introduce DiverseAgentEntropy, a novel, theoretically-grounded method employing multi-agent interaction for uncertainty estimation.
  arXiv  Detail & Related papers  (2024-12-12T18:52:40Z)
• CLUE: Concept-Level Uncertainty Estimation for Large Language Models [49.92690111618016]
  We propose a novel framework for Concept-Level Uncertainty Estimation for Large Language Models (LLMs) We leverage LLMs to convert output sequences into concept-level representations, breaking down sequences into individual concepts and measuring the uncertainty of each concept separately. We conduct experiments to demonstrate that CLUE can provide more interpretable uncertainty estimation results compared with sentence-level uncertainty.
  arXiv  Detail & Related papers  (2024-09-04T18:27:12Z)
• MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty [10.154013836043816]
  We investigate previous uncertainty quantification methods under the presence of data uncertainty.<n>Our findings show that previous methods relatively struggle compared to single-answer settings.<n>We observe that entropy- and consistency-based methods effectively estimate model uncertainty, even in the presence of data uncertainty.
  arXiv  Detail & Related papers  (2024-08-13T11:17:31Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• To Believe or Not to Believe Your LLM [51.2579827761899]
  We explore uncertainty quantification in large language models (LLMs) We derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large. We conduct a series of experiments which demonstrate the advantage of our formulation.
  arXiv  Detail & Related papers  (2024-06-04T17:58:18Z)
• Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space [14.715989394285238]
  Existing Large Language Models (LLMs) do not have an inherent functionality to provide the users with an uncertainty/confidence metric for each response it generates. A new framework is proposed in this paper to address these issues. Semantic density extracts uncertainty/confidence information for each response from a probability distribution perspective in semantic space.
  arXiv  Detail & Related papers  (2024-05-22T17:13:49Z)
• Uncertainty Quantification for In-Context Learning of Large Language Models [52.891205009620364]
  In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.
  arXiv  Detail & Related papers  (2024-02-15T18:46:24Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.