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Found 9 papers, 5 papers with code
Prefix Embeddings for In-context Machine Translation
We analyze the resulting embeddings’ training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.
Where does In-context Translation Happen in Large Language Models
Self-supervised large language models have demonstrated the ability to perform Machine Translation (MT) via in-context learning, but little is known about where the model performs the task with respect to prompt instructions and demonstration examples.
Anti-LM Decoding for Zero-shot In-context Machine Translation
Zero-shot In-context learning is the phenomenon where models can perform the task simply given the instructions.
In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models
In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i. e., the prompt examples.
Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI
Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors.
Clustering with UMAP: Why and How Connectivity Matters
These methods have strong mathematical foundations and are based on the intuition that the topology in low dimensions should be close to that of high dimensions.
Adaptive Mixed Component LDA for Low Resource Topic Modeling
Probabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining word or topic embeddings using neural methods.
CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task
We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR).
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
Topic models are a useful analysis tool to uncover the underlying themes within document collections.
