DeCoRe : Décodage par Contraste des Têtes de Récupération pour Atténuer les Hallucinations
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Résumé
Summary
AI-Generated Summary
Paper Overview
This literature introduces DeCoRe, a decoding strategy for language models that contrasts base model outputs with masked retrieval heads to enhance contextual fidelity and reduce hallucinations. DeCoRe significantly improves model accuracy in tasks requiring contextual faithfulness and factual recall. It outperforms baselines in various tasks like summarization, instruction-following, and open-book question answering.
Core Contribution
DeCoRe introduces a novel decoding strategy that leverages masked retrieval heads and dynamic entropy-controlled contrastive decoding to enhance contextual fidelity and reduce hallucinations in language models.
Research Context
The study positions itself within the realm of large language models (LLMs) and decoding strategies, focusing on improving contextual fidelity, factuality, and reasoning capabilities while mitigating hallucinations.
Keywords
DeCoRe, Language Models, Contextual Fidelity, Factuality, Hallucinations, Contrastive Decoding, Masked Retrieval Heads
Background
This research addresses the challenges faced by large language models in maintaining contextual fidelity and reducing hallucinations by proposing the DeCoRe decoding strategy. The rationale lies in the need to enhance model performance in tasks requiring accurate contextual understanding and factual recall.
Research Gap
Existing literature lacks efficient strategies to mitigate hallucinations and improve contextual fidelity in language models, especially in tasks like summarization and open-book question answering.
Technical Challenges
The primary technical obstacles include reducing hallucinations, enhancing contextual fidelity, and improving factuality in language model outputs while maintaining high performance across various tasks.
Prior Approaches
Previous solutions have focused on internal mechanisms of LLMs and constrained decoding methods but have not adequately addressed the challenges of hallucinations and contextual fidelity.
Methodology
The research methodology involves studying the impact of masked retrieval heads on language model performance, implementing the DeCoRe strategy without training, and evaluating its effectiveness in various tasks.
Theoretical Foundation
DeCoRe is based on the principle of contrasting base model outputs with masked retrieval heads to improve contextual fidelity and reduce hallucinations in language model generations.
Technical Architecture
DeCoRe utilizes masked retrieval heads and dynamic entropy-controlled contrastive decoding to enhance model outputs' contextual fidelity and reduce hallucinations.
Implementation Details
The study implements DeCoRe without training, focusing on the contrast between base model predictions and masked model outputs to improve contextual fidelity and factuality.
Innovation Points
DeCoRe introduces a novel approach to decoding in language models, significantly improving contextual fidelity, factuality, and reasoning capabilities across various tasks.
Experimental Validation
The experimental validation involves evaluating DeCoRe's performance in tasks like summarization, instruction-following, and question answering to demonstrate its effectiveness in enhancing contextual fidelity and reducing hallucinations.
Setup
Exact configurations, parameters, and datasets like NQ-Swap, NQ-Open, and MuSiQue are used to evaluate model performance in various tasks requiring factual recall and contextual fidelity.
Metrics
Evaluation criteria include metrics like EM scores, conditional entropy, and factuality assessments to quantify the improvements in contextual fidelity and factuality achieved by DeCoRe.
Results
Quantitative and qualitative findings show significant improvements in contextual fidelity, factuality, and reasoning capabilities of language models using DeCoRe compared to baselines.
Comparative Analysis
DeCoRe outperforms traditional strategies like DoLA and static contrastive decoding in tasks like summarization, instruction-following, and open-book question answering, showcasing its superiority in enhancing model performance.
Impact and Implications
The study's findings have significant implications for improving language model performance in tasks requiring contextual fidelity, factuality, and reasoning capabilities while reducing hallucinations.
Key Findings
DeCoRe significantly enhances contextual fidelity, factuality, and reasoning capabilities of language models, leading to improved performance in various tasks like summarization and open-book question answering.
Limitations
The study acknowledges limitations in certain tasks and the need for further research to optimize DeCoRe's performance across a broader range of language model applications.
Future Directions
Concrete research opportunities include exploring the application of DeCoRe in sensitive domains, refining the contrastive decoding process, and enhancing model performance in tasks requiring high contextual fidelity.
Practical Significance
DeCoRe offers practical applications in improving language model outputs' accuracy, especially in tasks requiring accurate contextual understanding, factuality, and reasoning capabilities.