Conversational Question Answering With Uncertainty Quantification

via “unanswerable question detection”

question-answering model by undefined. 1,45,572 downloads.

Unique: Explicitly trained on SQuAD 2.0's adversarial unanswerable questions (33% of dataset), learning to recognize when context genuinely lacks information rather than defaulting to low-confidence extractions like SQuAD 1.1-only models

vs others: More reliable than post-hoc confidence filtering because the model learned unanswerable patterns during training, rather than relying on threshold heuristics applied to models trained only on answerable questions

via “question-answering with source awareness and uncertainty expression”

Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the...

Unique: Hermes 3 405B's uncertainty expression capabilities are improved through instruction-tuning on datasets emphasizing appropriate confidence expression and the 405B scale enabling better nuanced understanding of knowledge boundaries.

vs others: Provides better uncertainty expression than Llama 2 Chat due to explicit training, though calibration may not match Claude 3 which has more sophisticated uncertainty modeling.

via “question-answering with source attribution and uncertainty quantification”

Hermes 3 is a generalist language model with many improvements over [Hermes 2](/models/nousresearch/nous-hermes-2-mistral-7b-dpo), including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the...

Unique: Hermes 3 is instruction-tuned to express uncertainty and cite sources more reliably than base Llama 3.1, with training on QA datasets that teach the model to distinguish between confident and uncertain responses and attribute answers to sources

vs others: More cost-effective than Claude 3 Sonnet for QA with source attribution while maintaining comparable accuracy, and outperforms Hermes 2 on uncertainty quantification and source citation reliability

GPT-5.3 Chat is an update to ChatGPT's most-used model that makes everyday conversations smoother, more useful, and more directly helpful. It delivers more accurate answers with better contextualization and significantly...

Unique: GPT-5.3 includes improved uncertainty calibration and explicit training to acknowledge knowledge gaps, reducing overconfident false answers compared to GPT-4, with better ability to distinguish between high-confidence factual knowledge and speculative reasoning

vs others: More transparent about uncertainty than Llama 2 or Mistral due to RLHF training specifically targeting honest uncertainty expression, though specialized QA systems with external knowledge bases (Retrieval-Augmented Generation) may be more reliable for fact-critical applications

via “question answering with source attribution and uncertainty quantification”

Cogito v2.1 671B MoE represents one of the strongest open models globally, matching performance of frontier closed and open models. This model is trained using self play with reinforcement learning...

Unique: Self-play RL training optimizes the model to explicitly express uncertainty and distinguish between confident and uncertain knowledge, creating more reliable question-answering behavior than models trained purely on supervised data. The reasoning capabilities enable the model to explain answer derivation, supporting human evaluation of correctness.

vs others: Provides better uncertainty handling and reasoning transparency than general LLMs, though without access to external knowledge bases like retrieval-augmented generation systems, making it suitable for domain-specific Q&A where training data coverage is sufficient.

via “uncertainty-quantification-and-confidence-signaling”

Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers...

Unique: Explicitly signals confidence and uncertainty in responses through linguistic hedging and implicit confidence assessment, rather than presenting all claims with uniform confidence

vs others: More transparent than LLMs that present speculative claims with false confidence; more nuanced than binary 'confident/not confident' systems