Large language models (LLMs) are increasingly used for tasks like writing and translation, but evaluating their output is tricky. Traditional metrics often fall short, and human evaluation is costly and time-consuming. What if LLMs could evaluate themselves? New research explores this intriguing possibility by introducing a framework called "Self-Assessing LLMs with Context-Aware Criteria," or SALC. This framework allows LLMs to generate their own evaluation criteria tailored to each specific task, rather than relying on static, pre-defined rules. Imagine an LLM tasked with summarizing the impact of climate change on polar bears. Instead of just checking for keywords, SALC allows the LLM to generate criteria like relevance to the instruction, completeness of information (including crucial details about habitat loss), and alignment with a given reference text. This dynamic approach allows for a more nuanced and context-aware evaluation, much like a human grader would adapt their criteria based on the specific assignment. SALC then uses these self-generated criteria to assess its own response, providing feedback and even assigning itself a score. This method moves beyond simple right-or-wrong assessments, allowing the LLM to identify areas where its response could be improved, such as missing details about polar bear hunting challenges. But can a smaller, open-source LLM achieve similar results? The researchers also developed SALC-Tune, which fine-tunes smaller models using the criteria and feedback generated by a larger LLM like GPT-4. The results are promising. SALC-Tune not only achieves comparable performance to larger models at a lower computational cost, but it also significantly outperforms existing open-source evaluation models. Moreover, using SALC as a reward model in reinforcement learning from human feedback boosts performance even further. This research opens exciting doors for making LLM evaluation more efficient, accurate, and scalable. It could lead to LLMs that continuously learn and improve, providing even more reliable and high-quality outputs in the future. While there are still challenges to overcome, the possibility of self-assessing LLMs suggests a significant step forward in the quest for truly intelligent and autonomous AI.