Search Results for author:
Found 4 papers, 2 papers with code
Long-range Language Modeling with Self-retrieval
We train the retriever component with a semantic objective, where the goal is to retrieve chunks that increase the probability of the next chunk, according to a reference LM.
QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs
Our results highlight the need for developing ODQA models that handle a broad range of question types, including single and multi-answer questions.
Learning To Retrieve Prompts for In-Context Learning
In-context learning is a recent paradigm in natural language understanding, where a large pre-trained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without any update to its parameters.
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2. 2x speed-up in decoding time and a $\sim$5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding.
