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LLMs have demonstrated impressive proficiency in generating coherent and high-quality text, making them valuable across a range of text- generation tasks. However, rigorous evaluation of this generated content is crucial, as ensuring its quality remains a significant challenge due to persistent issues such as factual inaccuracies and hallucination. This paper introduces three fine-tuned general-purpose LLM auto-evaluators, REC-8B, REC-12B and REC-70B, specifically designed to evaluate generated text across sev- eral dimensions: faithfulness, instruction follow- ing, coherence, and completeness. These mod- els not only provide ratings for these metrics but also offer detailed explanation and verifiable citation, thereby enhancing trust in the content. Moreover, the models support various citation modes, accommodating different requirements for latency and granularity. Extensive evalua- tions on diverse benchmarks demonstrate that our general-purpose LLM auto-evaluator, REC-70B, outperforms state-of-the-art LLMs, excelling in content evaluation by delivering better quality ex- planation and citation with minimal bias. Our REC dataset and models are available at https: //github.com/adelaidehsu/REC.