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Found 8 papers, 4 papers with code
LIMA: Less Is More for Alignment
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences.
LEVER: Learning to Verify Language-to-Code Generation with Execution
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation.
Ranked #2 on
Semantic Parsing
on spider
Demystifying Prompts in Language Models via Perplexity Estimation
Language models can be prompted to perform a wide variety of zero- and few-shot learning problems.
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
Cultural Vocal Bursts Intensity Prediction
Few-Shot Learning
+1
Improving In-Context Few-Shot Learning via Self-Supervised Training
Self-supervised pretraining has made few-shot learning possible for many NLP tasks.
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
One goal of answer summarization is to produce a summary that reflects the range of answer perspectives.
Multi-Perspective Abstractive Answer Summarization
A major obstacle for multi-perspective, abstractive answer summarization is the absence of a dataset to provide supervision for producing such summaries.
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Ranked #14 on
Question Answering
on HotpotQA
