Supertagging is an essential task in Categorical grammar parsing and is crucial for dissecting sentence structures. Our research explores the capacity of Large language Models (LLMs) in supertagging for both Combinato...
详细信息
This study explores the inherent limitations of Large language Models (LLMs) from a scaling perspective, focusing on the upper bounds of their cognitive capabilities. We integrate insights from cognitive science to qu...
详细信息
Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted...
详细信息
Large language Models (LLMs) show remarkable performance on a wide variety of tasks. Most LLMs split text into multi-character tokens and process them as atomic units without direct access to individual characters. Th...
详细信息
The study of Differential Privacy (DP) in naturallanguageprocessing often views the task of text privatization as a rewriting task, in which sensitive input texts are rewritten to hide explicit or implicit private i...
详细信息
ISBN:
(纸本)9798400717185
The study of Differential Privacy (DP) in naturallanguageprocessing often views the task of text privatization as a rewriting task, in which sensitive input texts are rewritten to hide explicit or implicit private information. In order to evaluate the privacy-preserving capabilities of a DP text rewriting mechanism, empirical privacy tests are frequently employed. In these tests, an adversary is modeled, who aims to infer sensitive information (e.g., gender) about the author behind a (privatized) text. Looking to improve the empirical protections provided by DP rewriting methods, we propose a simple post-processing method based on the goal of aligning rewritten texts with their original counterparts, where DP rewritten texts are rewritten again. Our results show that such an approach not only produces outputs that are more semantically reminiscent of the original inputs, but also texts which score on average better in empirical privacy evaluations. Therefore, our approach raises the bar for DP rewriting methods in their empirical privacy evaluations, providing an extra layer of protection against malicious adversaries.
Generative language models often struggle with specialized or less-discussed knowledge. A potential solution is found in Retrieval-Augmented Generation (RAG) models which act like retrieving information before generat...
详细信息
We introduce GenerativeDictionary, a novel dictionary system that generates word sense interpretations based on the given context. Our approach involves transforming context sentences to highlight the meaning of targe...
详细信息
While naturallanguage inference (NLI) has emerged as a prominent task for evaluating a model's capability to perform naturallanguage understanding, creating large benchmarks for training deep learning models imp...
详细信息
With the rapid growth of Large language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise. This proliferation has highlighted the need for quick, high-qual...
详细信息
In the rapidly evolving domain of naturallanguage Generation (NLG) evaluation, introducing Large language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and...
详细信息
暂无评论