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Found 14 papers, 4 papers with code
Functional Interpolation for Relative Positions Improves Long Context Transformers
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models.
MEMORY-VQ: Compression for Tractable Internet-Scale Memory
Retrieval augmentation is a powerful but expensive method to make language models more knowledgeable about the world.
GLIMMER: generalized late-interaction memory reranker
Memory-augmentation is a powerful approach for efficiently incorporating external information into language models, but leads to reduced performance relative to retrieving text.
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference.
CoLT5: Faster Long-Range Transformers with Conditional Computation
Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token.
Ranked #1 on
Long-range modeling
on SCROLLS
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
Retrieval-augmented language models such as Fusion-in-Decoder are powerful, setting the state of the art on a variety of knowledge-intensive tasks.
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction
Large language models have ushered in a golden age of semantic parsing.
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference
Fusion-in-Decoder (FiD) is a powerful retrieval-augmented language model that sets the state-of-the-art on many knowledge-intensive NLP tasks.
Ranked #3 on
Question Answering
on WebQuestions
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation.
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing
Then, it retrieves exemplars with outputs similar to the preliminary prediction which are used to generate a final prediction.
MAVE: A Product Dataset for Multi-source Attribute Value Extraction
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information.
Ranked #1 on
Attribute Value Extraction
on MAVE
ShopTalk: A System for Conversational Faceted Search
The dialog understanding system consists of a deep-learned Contextual Language Understanding module, which interprets user utterances, and a primarily rules-based Dialog-State Tracker (DST), which updates the dialog state and formulates search requests intended for the fulfillment engine.
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach
In particular, we build a question answering model which treats each attribute as a question and identifies the answer span corresponding to the attribute value in the product context.
ETC: Encoding Long and Structured Inputs in Transformers
Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks.
Ranked #3 on
Question Answering
on ConditionalQA
