Search Results for author:
Found 9 papers, 4 papers with code
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.
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.
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.
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).
