Search Results for author:
Found 13 papers, 3 papers with code
Unsupervised Multi-View Post-OCR Error Correction With Language Models
We investigate post-OCR correction in a setting where we have access to different OCR views of the same document.
Orca 2: Teaching Small Language Models How to Reason
Research on training small LMs has often relied on imitation learning to replicate the output of more capable models.
Ranked #1 on
Crass AI
on BIG-bench
Automatic Pair Construction for Contrastive Post-training
Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
While existing token-level early exit methods show promising results for online inference, they cannot be readily applied for batch inferencing and Key-Value caching.
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input.
Facts That Matter
This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts.
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora
Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval.
A Study of the Importance of External Knowledge in the Named Entity Recognition Task
In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER).
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge.
Multilingual Named Entity Recognition
named-entity-recognition
+2
MinIE: Minimizing Facts in Open Information Extraction
The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner.
