From: aidotengineer
A significant challenge in AI development, particularly with large language models (LLMs), revolves around their ability to correctly understand and adhere to provided context, even when general accuracy metrics appear high [01:28:00]. This concern led to an evaluation questioning whether continued development of domain-specific models is necessary if general models achieve high accuracy [01:53:02].
The FAIL Benchmark for Evaluation
To address this, a real-world scenario evaluation set called “FAIL” was created to test model performance in diverse, challenging conditions [03:12:35]. This benchmark includes two main categories of failures: Query Failure and Context Failure [03:34:06].
Context Failure Categories
The “context failure” category specifically introduces issues related to the provided context, which is crucial for evaluating context grounding [04:23:07]. This category includes three subcategories:
- Missing Context The LLM is asked questions about context that does not exist in the prompt [04:33:48].
- OCR Errors Introduction of character issues, incorrect spacing, or merged words, mimicking errors from converting physical documents to text [04:44:27].
- Irrelevant Context A completely wrong document is uploaded, and the evaluation assesses if the LLM still attempts to answer or recognizes the irrelevance [05:08:00].
The evaluation’s key metrics focused on whether the model gave a correct answer and whether it followed the grounding or context correctly [05:52:16].
Observed Challenges in Context Grounding
When evaluating general and thinking models, interesting results emerged regarding context grounding [06:32:00].
- Failure to Follow Context Many “thinking models” were observed not to refuse to answer, even when given wrong or irrelevant context or data [06:52:00]. This leads to a significantly higher rate of hallucination [07:11:00].
- High Hallucination Rates While models generally provide an answer, particularly reasoning or thinking models, their performance in context grounding for tasks like text generation or question answering is not good [07:33:00]. This suggests that in domain-specific tasks, these models might not be “thinking” but rather producing high hallucination rates [09:34:00].
- Smaller Models Outperform Larger Ones in Grounding Surprisingly, smaller models showed better performance in context grounding compared to larger, more “thinking” models, which gave worse results [08:46:00]. The larger models often did not follow the attached context, with answers existing outside the provided information [09:01:00].
- Robustness vs. Grounding Gap There is a significant gap between a model’s robustness (ability to handle misspelled queries or incomplete input) and its ability to correctly ground answers in the provided context [09:54:00]. Even the best models achieved only around 81% in context grounding and robustness, meaning approximately 20% of requests could be completely wrong [10:14:00].
Conclusion
The data suggests that despite increasing general accuracy of LLMs, challenges with instruction following in AI remain, particularly concerning proper context grounding and relevance [11:22:00]. Therefore, the continued development and implementation of domain-specific models are still essential [11:11:00]. To create reliable AI applications, a “full stack” approach is needed, incorporating robust systems, grounding mechanisms, and guardrails built around the entire system [10:44:00].