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Found 13 papers, 5 papers with code
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it.
Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach
Existing explanation methods for image classification struggle to provide faithful and plausible explanations.
A Survey of Text Style Transfer: Applications and Ethical Implications
Text style transfer (TST) is an important task in controllable text generation, which aims to control selected attributes of language use, such as politeness, formality, or sentiment, without altering the style-independent content of the text.
Generating clickbait spoilers with an ensemble of large language models
Current state-of-the-art methods are based on passage retrieval or question answering approaches and are limited to generating spoilers only in the form of a phrase or a passage.
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions.
Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs
Natural Language Processing (NLP) research is increasingly focusing on the use of Large Language Models (LLMs), with some of the most popular ones being either fully or partially closed-source.
The Problem of Coherence in Natural Language Explanations of Recommendations
Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user.
Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation
Our method does not need any changes to the underlying LM's architecture or training procedure and can thus be combined with any model and decoding operating on word probabilities.
With a Little Help from the Authors: Reproducing Human Evaluation of an MT Error Detector
This work presents our efforts to reproduce the results of the human evaluation experiment presented in the paper of Vamvas and Sennrich (2022), which evaluated an automatic system detecting over- and undertranslations (translations containing more or less information than the original) in machine translation (MT) outputs.
Three Ways of Using Large Language Models to Evaluate Chat
This paper describes the systems submitted by team6 for ChatEval, the DSTC 11 Track 4 competition.
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping
In this work, we study the unsupervised cross-lingual word embeddings mapping method presented by Artetxe et al. (2018).
