Papers with uncertainty metrics
Active Prompting with Chain-of-Thought for Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to annotate large language models rely on a fixed set of human-annotated exemplars, which are not always the most effective for different tasks. |
| Approach: | They propose a method to adapt large language models to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning) they introduce several metrics to characterize uncertainty so as to select the most uncertain questions for annotation. |
| Outcome: | The proposed method significantly improves performance on eight complex reasoning tasks. |
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for uncertainty estimation are inadequate for safety-critical applications. |
| Approach: | They propose a method that uses the distances from neighbors and the ratio of labels in neighbors to estimate uncertainty. |
| Outcome: | The proposed method outperforms baseline and density-based methods in calibration and uncertainty metrics. |
Investigating the Impact of Model Instability on Explanations and Uncertainty (2024.findings-acl)
Copied to clipboard
| Challenge: | Explainable AI methods are typically evaluated holistically, but small perturbations to inputs can vastly distort explanations. |
| Approach: | They artificially simulate epistemic uncertainty in text input by introducing noise at inference time and measure the effect on the output of pre-trained language models. |
| Outcome: | The proposed model can detect salient tokens when uncertain, but it is not reliable when small perturbations are exposed during training. |
Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Previous work bases the timing of questions on supervised models learned from interactions between humans. |
| Approach: | They propose to ground the need for questions in the acting agent's predictive uncertainty by using the T5 encoder-decoder architecture to solve a Minecraft Collaborative Building task. |
| Outcome: | The proposed model can detect ambiguous instructions and predict responses better than previous models. |
DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-agent debates can improve the accuracy of Large Language Models by having multiple agents discuss solutions over several rounds of debate. |
| Approach: | a debate framework that uses uncertainty metrics to assess agent confidence is proposed . the framework uses textual prompts or a modified attention mechanism that adjusts token weights . |
| Outcome: | The proposed framework assesses agent confidence using uncertainty metrics . the framework is available at https://github.com/lukeyoffe/debunc. |