sheryc/Qwen2.5-14B-Instruct-CARE
sheryc/Qwen2.5-14B-Instruct-CARE is a 14.7 billion parameter instruction-tuned language model based on Qwen2.5-14B-Instruct, developed by the authors of the CARE framework. It features native retrieval-augmented reasoning capabilities, specifically trained to improve context fidelity and reduce hallucinations by explicitly integrating in-context evidence. This model excels at complex reasoning tasks requiring evidence integration and generates structured reasoning outputs with explicit citations, making it ideal for applications demanding high factual accuracy and explainability.
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sheryc/Qwen2.5-14B-Instruct-CARE: Context-Aware Retrieval-Enhanced Reasoning
This model, based on the 14.7 billion parameter Qwen2.5-14B-Instruct, is enhanced with the CARE (Context-Aware Retrieval-Enhanced reasoning) framework. Its core innovation lies in its native retrieval-augmented reasoning capabilities, designed to significantly improve context fidelity and reduce hallucinations by teaching the model to explicitly integrate in-context evidence into its reasoning process.
Key Capabilities
- Native Retrieval-Augmented Reasoning: Dynamically identifies and incorporates relevant evidence from the input context, leading to more grounded responses.
- Improved Context Fidelity: Demonstrates superior adherence to provided context, even when it contradicts the model's parametric knowledge, minimizing factual errors.
- Enhanced Multi-Hop Reasoning: Excels at complex reasoning tasks that require synthesizing information from multiple pieces of evidence.
- Structured Reasoning Output: Generates transparent reasoning chains, explicitly citing evidence using
<think>and<retrieval>tags, improving explainability. - Two-Phase Training: Utilizes a novel Supervised Fine-Tuning (SFT) phase on HotpotQA for evidence integration patterns, followed by a Reinforcement Learning phase with Group Relative Policy Optimization (GRPO) on datasets like DROP and MS MARCO, optimizing for accuracy, format, and retrieval consistency.
Good For
- Applications requiring high factual accuracy: Ideal for scenarios where grounding responses in provided context is critical.
- Complex question answering: Particularly effective for questions demanding multi-step reasoning and evidence synthesis.
- Explainable AI (XAI): The structured output with explicit citations makes it suitable for use cases where understanding the model's reasoning process is important.
- Reducing hallucinations: Designed to mitigate common LLM issues by enforcing context adherence.