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Found 9 papers, 0 papers with code
Time-Aware Language Models as Temporal Knowledge Bases
We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum -- those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data.
Graph-Based Decoding for Task Oriented Semantic Parsing
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour
Language models (LMs) trained on raw texts have no direct access to the physical world.
WinoDict: Probing language models for in-context word acquisition
We introduce a new in-context learning paradigm to measure Large Language Models' (LLMs) ability to learn novel words during inference.
DIFFQG: Generating Questions to Summarize Factual Changes
We propose representing factual changes between paired documents as question-answer pairs, where the answer to the same question differs between two versions.
Salient Span Masking for Temporal Understanding
First, we find that SSM alone improves the downstream performance on three temporal tasks by an avg.
Selectively Answering Ambiguous Questions
We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous.
NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders
Neural document rerankers are extremely effective in terms of accuracy.
Gecko: Versatile Text Embeddings Distilled from Large Language Models
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
