Challenge: Existing methods to evaluate reliability of generated text are lacking in natural language generation.
Approach: They propose a non-exchangeable conformal prediction method that provides bounds on coverage . they validated their method with k-NN retrieval and show that it produces encouraging results .
Outcome: The proposed method produces encouraging results in machine translation and language modeling tasks.

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ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)

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Challenge: Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs.
Approach: They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets.
Outcome: Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks.
SConU: Selective Conformal Uncertainty in Large Language Models (2025.acl-long)

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Challenge: Existing frameworks fail to identify outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets.
Approach: They propose a method that implements significance tests to determine whether a given sample deviates from the uncertainty distribution of the calibration set.
Outcome: The proposed approach facilitates rigorous management of miscoverage rates across single-domain and interdisciplinary contexts, and enhances the efficiency of predictions.
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access (2024.findings-emnlp)

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Challenge: Existing methods for quantifying uncertainty in large language models with black-box API access are limited due to the complex data distributions and inner model mechanism.
Approach: They propose a conformal prediction method that minimizes the size of prediction sets and ensures a statistical guarantee of the user-defined coverage.
Outcome: The proposed method outperforms existing methods on close-ended and open-ended questions.
Data-to-text Generation by Splicing Together Nearest Neighbors (2021.emnlp-main)

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Challenge: Existing work on data-to-text generation relies on retrieved "neighbors" but instead generates text token-by-token, left-to right.
Approach: They propose to splice together retrieved segments of text from "neighbor" source-target pairs to generate text token-by-token, left-to-right.
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Conformal Nucleus Sampling (2023.findings-acl)

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Challenge: Modern language generation methods employ one of a handful of standard decoding strategies . greedy search and other methods generate dull text or degenerate text .
Approach: They employ a calibration procedure to calibrate the parameter p as a function of the entropy of the next word distribution.
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Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts (2024.findings-emnlp)

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Challenge: 88-98% of cases return distinguishable generation probability and uncertainty distributions to unfaithfully hallucinated texts, regardless of their size and structure.
Approach: They examine 24 pre-trained language models on 6 data sets to examine their ability to distinguish unfaithfully hallucinated texts.
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Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation (2023.emnlp-main)

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Challenge: Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies.
Approach: They propose a new decoding method that augments the contrastive search framework with context-aware regularization terms to promote tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text.
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How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue (2020.emnlp-main)

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Challenge: Neural Natural Language Generation (NLG) systems are well known for their unreliability.
Approach: They propose a data augmentation approach which restricts the output of a neural network and guarantees reliability.
Outcome: The proposed approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)

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Challenge: Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text.
Approach: They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text.
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NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)

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Challenge: Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding.
Approach: They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding.
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