Search Results for author:
Found 23 papers, 10 papers with code
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Understanding In-Context Learning via Supportive Pretraining Data
We observe that a continued pretraining on this small subset significantly improves the model's ICL ability, by up to 18%.
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training
Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems.
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.
Ranked #26 on
Natural Language Inference
on RTE
Improving In-Context Few-Shot Learning via Self-Supervised Training
Self-supervised pretraining has made few-shot learning possible for many NLP tasks.
OPT: Open Pre-trained Transformer Language Models
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning.
Ranked #2 on
Stereotypical Bias Analysis
on CrowS-Pairs
Few-shot Learning with Multilingual Language Models
Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Efficient Large Scale Language Modeling with Mixtures of Experts
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.
SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.
Exposing Paid Opinion Manipulation Trolls
Recently, Web forums have been invaded by opinion manipulation trolls.
EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains.
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings
We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering.
Hunting for Troll Comments in News Community Forums
There are different definitions of what a troll is.
Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension
In this work, we propose to use linguistic annotations as a basis for a \textit{Discourse-Aware Semantic Self-Attention} encoder that we employ for reading comprehension on long narrative texts.
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Our oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts.
Ranked #22 on
Question Answering
on OpenBookQA
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge
We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting.
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved.
Large-Scale Goodness Polarity Lexicons for Community Question Answering
This leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis.
Story Cloze Ending Selection Baselines and Data Examination
This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a).
