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Found 16 papers, 7 papers with code
Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment
In this work, we evaluate a simple approach for zero-shot cross-lingual alignment, where a reward model is trained on preference data in one source language and directly applied to other target languages.
A Taxonomy of Ambiguity Types for NLP
Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP.
Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment
Do LMs infer the semantics of text from co-occurrence patterns in their training data?
Universal Deoxidation of Semiconductor Substrates Assisted by Machine-Learning and Real-Time-Feedback-Control
It standardizes deoxidation temperatures across various equipment and substrate materials, advancing the standardization research process in semiconductor preparation, a significant milestone in thin film growth technology.
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
The impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills.
Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots
Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources.
We're Afraid Language Models Aren't Modeling Ambiguity
We find that the task remains extremely challenging, including for GPT-4, whose generated disambiguations are considered correct only 32% of the time in human evaluation, compared to 90% for disambiguations in our dataset.
Continued Pretraining for Better Zero- and Few-Shot Promptability
We demonstrate that a simple recipe, continued pretraining that incorporates a trainable prompt during multi-task learning, leads to improved promptability in both zero- and few-shot settings compared to existing methods, up to 31% relative.
Modeling Context With Linear Attention for Scalable Document-Level Translation
Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations.
Transparency Helps Reveal When Language Models Learn Meaning
Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text.
Learning with Latent Structures in Natural Language Processing: A Survey
While end-to-end learning with fully differentiable models has enabled tremendous success in natural language process (NLP) and machine learning, there have been significant recent interests in learning with latent discrete structures to incorporate better inductive biases for improved end-task performance and better interpretability.
ABC: Attention with Bounded-memory Control
One way to improve the efficiency is to bound the memory size.
Infusing Finetuning with Semantic Dependencies
For natural language processing systems, two kinds of evidence support the use of text representations from neural language models "pretrained" on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia).
Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution
Despite significant recent progress in coreference resolution, the quality of current state-of-the-art systems still considerably trails behind human-level performance.
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource constraints.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings.
