Calibrated Interpretation: Confidence Estimation in Semantic Parsing (2023.tacl-1)
Copied to clipboard
| Challenge: | Sequence generation models are increasingly being used to translate natural language into programs . calibration of such models is a key component of safety, says aaron sagar . |
| Approach: | They investigate whether calibration of popular generation models varies across models and datasets . they find that calibration varies among models and data sets, and that it is important to include it in evaluations if it is included . |
| Outcome: | The calibration of popular generation models varies across models and datasets . the authors find that the accuracy of models is dependent on confidence . |
Similar Papers
Confidence Modeling for Neural Semantic Parsing (P18-1)
Copied to clipboard
| Challenge: | Experimental results show that neural semantic parsers are difficult to interpret due to their complexity. |
| Approach: | They propose to use confidence models to estimate predictions for neural semantic parsers . they outline three major causes of uncertainty and use metrics to quantify them . |
| Outcome: | The proposed model outperforms a widely used method that relies on posterior probability and improves interpretation quality. |
Semantic Accuracy in Natural Language Generation: A Thesis Proposal (2023.acl-srw)
Copied to clipboard
| Challenge: | Using large pre-trained language models, it is essential to research their reliability . if a human does not know the answer to a question, the socially acceptable behavior is to say 'I do not know' failing to fulfill this expectation can lead to distrust, or spread of misinformation. |
| Approach: | They propose a method for evaluating semantic accuracy and a benchmark for NLG metrics. |
| Outcome: | The proposed method evaluates semantic accuracy and provides a benchmark for NLG metrics. |
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution’s Characteristics (2025.acl-short)
Copied to clipboard
| Challenge: | Existing methods for estimating confidence in text generation do not account for many valid answers in generation tasks. |
| Approach: | They propose task-agnostic confidence metrics that rely solely on model probabilities without the need for further fine-tuning or heuristics. |
| Outcome: | The proposed models improve the accuracy of BART and Flan-T5 on summarization, translation, and question answering datasets. |
A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
| Approach: | They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned. |
| Outcome: | The proposed methods can be used to assess the reliability of models and to calibrate them across tasks. |
On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure . |
| Approach: | They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training. |
| Outcome: | The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration. |
Did You Mean...? Confidence-based Trade-offs in Semantic Parsing (2023.emnlp-main)
Copied to clipboard
| Challenge: | a calibrated model can help balance common trade-offs in task-oriented parsing. |
| Approach: | They propose a model which rephrases low-confidence inputs to improve usability and safety. |
| Outcome: | The proposed system reduces the number of incorrect low-confidence programs executed, but at a cost to usability. |
A Close Look into the Calibration of Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. |
| Approach: | They conduct fine-grained control experiments to study the dynamic change in PLMs’ calibration performance in training. |
| Outcome: | The proposed methods significantly reduce PLMs’ confidence in wrong predictions. |
Uncertainty in Language Models: Assessment through Rank-Calibration (2024.emnlp-main)
Copied to clipboard
Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban
| Challenge: | Language Models (LMs) have shown promising performance in natural language generation . however, it is crucial to correctly quantify their level of uncertainty in responding to inputs. |
| Approach: | They propose a framework to quantify uncertainty and confidence for Large Language Models . they use a Rank-calibration framework to measure uncertainty and confident responses . |
| Outcome: | The proposed framework assesses uncertainty and confidence measures for LMs. |
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering (2021.tacl-1)
Copied to clipboard
| Challenge: | Recent studies have shown that language models capture different types of knowledge regarding facts or commonsense knowledge. |
| Approach: | They examine how language models can be calibrated to make their confidence scores correlate better with the likelihood of correctness. |
| Outcome: | The proposed calibration methods improve confidence scores on QA tasks and improve accuracy. |
Calibrating Structured Output Predictors for Natural Language Processing (2020.acl-main)
Copied to clipboard
| Challenge: | Several modern machine-learning based NLP systems can provide a confidence score with their output predictions. |
| Approach: | They propose a general calibration scheme for output entities of interest in NLP applications that can be used to calibrate confidence scores. |
| Outcome: | The proposed calibration scheme outperforms current calibration techniques for Named Entity Recognition, Part-of-speech tagging and Question Answering systems. |