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Found 18 papers, 9 papers with code
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models.
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages.
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries.
Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used to evaluate Large Language Models (LLMs) through "A vs B" paired comparisons.
Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation
Human evaluation is increasingly critical for assessing large language models, capturing linguistic nuances, and reflecting user preferences more accurately than traditional automated metrics.
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
In this work, we take a wider view and explore scalable estimates of data quality that can be used to systematically measure the quality of pretraining data.
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents.
In Defense of Cross-Encoders for Zero-Shot Retrieval
We find that the number of parameters and early query-document interactions of cross-encoders play a significant role in the generalization ability of retrieval models.
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
This has made distilled and dense models, due to latency constraints, the go-to choice for deployment in real-world retrieval applications.
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InPars: Data Augmentation for Information Retrieval using Large Language Models
In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks.
mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
In this work, we present mMARCO, a multilingual version of the MS MARCO passage ranking dataset comprising 13 languages that was created using machine translation.
Understanding and Enhancing the Use of Context for Machine Translation
To understand and infer meaning in language, neural models have to learn complicated nuances.
A New Neural Search and Insights Platform for Navigating and Organizing AI Research
We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
The Unreasonable Volatility of Neural Machine Translation Models
Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered.
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation
In this work, we explore different aspects of back-translation, and show that words with high prediction loss during training benefit most from the addition of synthetic data.
Examining the Tip of the Iceberg: A Data Set for Idiom Translation
Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs.
Learning Topic-Sensitive Word Representations
Distributed word representations are widely used for modeling words in NLP tasks.
Data Augmentation for Low-Resource Neural Machine Translation
The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora.
Data Augmentation
Low-Resource Neural Machine Translation
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