Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models

• URL: http://arxiv.org/abs/2411.16797v1
• Date: Mon, 25 Nov 2024 10:18:17 GMT
• Title: Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models
• Authors: Alireza Amiri-Margavi, Iman Jebellat, Ehsan Jebellat, Seyed Pouyan Mousavi Davoudi,
• Abstract summary: We explore the collaborative dynamics of an innovative language model interaction system involving advanced models. These models generate and answer complex, PhD-level statistical questions without exact ground-truth answers. Our study investigates how inter-model consensus enhances the reliability and precision of responses.
• Score: 1.6874375111244329
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We explore the collaborative dynamics of an innovative language model interaction system involving advanced models such as GPT-4-0125-preview, Meta-LLaMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash. These models generate and answer complex, PhD-level statistical questions without exact ground-truth answers. Our study investigates how inter-model consensus enhances the reliability and precision of responses. By employing statistical methods such as chi-square tests, Fleiss' Kappa, and confidence interval analysis, we evaluate consensus rates and inter-rater agreement to quantify the reliability of collaborative outputs. Key results reveal that Claude and GPT-4 exhibit the highest reliability and consistency, as evidenced by their narrower confidence intervals and higher alignment with question-generating models. Conversely, Gemini and LLaMA show more significant variability in their consensus rates, as reflected in wider confidence intervals and lower reliability percentages. These findings demonstrate that collaborative interactions among large language models (LLMs) significantly improve response reliability, offering novel insights into autonomous, cooperative reasoning and validation in AI systems.

Related papers

• Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions [8.069858557211132]
  Large Language Models (LLMs) have shown remarkable capabilities across various tasks, but their deployment in high-stake domains requires consistent performance across multiple interaction rounds. This paper introduces a comprehensive framework for evaluating and improving LLM response consistency, making three key contributions.
  arXiv  Detail & Related papers  (2025-03-28T11:49:56Z)
• RECSIP: REpeated Clustering of Scores Improving the Precision [0.0]
  We introduce the framework REpeated Clustering of Scores Improving the Precision (RECSIP) RECSIP focuses on improving the precision of Large Language Models (LLMs) by asking multiple models in parallel, scoring and clustering their responses to ensure a higher reliability on the response. The evaluation of our reference implementation recsip on the benchmark MMLU-Pro using the models GPT-4o, Claude and Gemini shows an overall increase of 5.8 per cent points compared to the best used model.
  arXiv  Detail & Related papers  (2025-03-15T12:36:32Z)
• Collective Reasoning Among LLMs A Framework for Answer Validation Without Ground Truth [0.0]
  This study explores how inter-model consensus enhances response reliability and serves as a proxy for assessing the quality of generated questions. We present a collaborative framework where multiple large language models, namely GPT-4-0125-preview, Meta-LLaMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash, work together to generate and respond to complex PhD-level probability questions.
  arXiv  Detail & Related papers  (2025-02-28T06:20:52Z)
• On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models [0.16874375111244325]
  We investigate the correlation between adversarial robustness and OOD robustness in large language models (LLMs) Our findings highlight nuanced interactions between adversarial robustness and OOD robustness, with results indicating limited transferability. Further research is needed to evaluate these interactions across larger models and varied architectures.
  arXiv  Detail & Related papers  (2024-12-13T20:04:25Z)
• Graph-based Confidence Calibration for Large Language Models [22.394717844099684]
  We propose a novel method to develop a well-calibrated confidence estimation model. We use a weighted graph to represent the consistency among the large language models' responses to a question. We then train a graph neural network to estimate the probability of correct responses.
  arXiv  Detail & Related papers  (2024-11-03T20:36:44Z)
• ECon: On the Detection and Resolution of Evidence Conflicts [56.89209046429291]
  The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems. This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios.
  arXiv  Detail & Related papers  (2024-10-05T07:41:17Z)
• The BRAVO Semantic Segmentation Challenge Results in UNCV2024 [68.20197719071436]
  We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models.
  arXiv  Detail & Related papers  (2024-09-23T15:17:30Z)
• Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design [63.24275274981911]
  Compound AI Systems consisting of many language model inference calls are increasingly employed. In this work, we construct systems, which we call Networks of Networks (NoNs) organized around the distinction between generating a proposed answer and verifying its correctness. We introduce a verifier-based judge NoN with K generators, an instantiation of "best-of-K" or "judge-based" compound AI systems.
  arXiv  Detail & Related papers  (2024-07-23T20:40:37Z)
• Large Language Model Confidence Estimation via Black-Box Access [30.490207799344333]
  We propose a simple framework where, we engineer novel features and train a (interpretable) model to estimate the confidence. We empirically demonstrate that our framework is effective in estimating confidence of Flan-ul2,-13b and Mistral-7b on four benchmark Q&A tasks. Our interpretable approach provides insight into features that are predictive of confidence, leading to the interesting and useful discovery.
  arXiv  Detail & Related papers  (2024-06-01T02:08:44Z)
• Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models [14.5291643644017]
  We introduce the concept of Confidence-Probability Alignment. We probe the alignment between models' internal and expressed confidence. Among the models analyzed, OpenAI's GPT-4 showed the strongest confidence-probability alignment.
  arXiv  Detail & Related papers  (2024-05-25T15:42:04Z)
• Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models [27.63938857490995]
  This work focuses on improving the confidence estimation of large language models. Considering the fragility of self-awareness in language models, we introduce a Multi-Perspective Consistency (MPC) method. The experimental results on eight publicly available datasets show that our MPC achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-02-17T13:37:39Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication [76.04373033082948]
  Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. We propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving.
  arXiv  Detail & Related papers  (2023-12-04T11:53:56Z)
• Methods to Estimate Large Language Model Confidence [2.4797200957733576]
  This study evaluates methods to measure Large Language Models confidence when suggesting a diagnosis for challenging clinical vignettes. SC Agreement Frequency is the most useful proxy for model confidence, especially for medical diagnosis.
  arXiv  Detail & Related papers  (2023-11-28T05:44:06Z)
• JAB: Joint Adversarial Prompting and Belief Augmentation [81.39548637776365]
  We introduce a joint framework in which we probe and improve the robustness of a black-box target model via adversarial prompting and belief augmentation. This framework utilizes an automated red teaming approach to probe the target model, along with a belief augmenter to generate instructions for the target model to improve its robustness to those adversarial probes.
  arXiv  Detail & Related papers  (2023-11-16T00:35:54Z)
• PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems [59.1250765143521]
  Current knowledge-grounded dialogue systems often fail to align the generated responses with human-preferred qualities. We propose Polished & Informed Candidate Scoring (PICK), a generation re-scoring framework. We demonstrate the effectiveness of PICK in generating responses that are more faithful while keeping them relevant to the dialogue history.
  arXiv  Detail & Related papers  (2023-09-19T08:27:09Z)
• Consistency Analysis of ChatGPT [65.268245109828]
  This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour. Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions.
  arXiv  Detail & Related papers  (2023-03-11T01:19:01Z)
• FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness [56.263482420177915]
  We study the faithfulness of existing systems from a new perspective of factual robustness. We propose a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarial perturbations.
  arXiv  Detail & Related papers  (2022-11-01T06:09:00Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)

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.