nlp

Postdoctoral Researcher - NLP

Two Postdoctoral Researcher positions up to 2 years - Deadline May 30th 2024

Classist Tools: Social Class Correlates with Performance in NLP

Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But …

Impoverished Language Technology: The Lack of (Social) Class in NLP

Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-demographic factors and language production and perception. Despite the …

The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising 'Alignment' in Large Language Models

In this paper, we address the concept of 'alignment' in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its parallels to empty signifiers. To establish a shared vocabulary around how …

SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models

The past year has seen rapid acceleration in the development of large language models (LLMs). For many tasks, there is now a wide range of open-source and open-access LLMs that are viable alternatives to proprietary models like ChatGPT. Without …

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful …

The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human …

Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions

Training large language models to follow instructions makes them perform better on a wide range of tasks, generally becoming more helpful. However, a perfectly helpful model will follow even the most malicious instructions and readily generate …

Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation

Large Language Models (LLMs) exhibit remarkable text classification capabilities, excelling in zero- and few-shot learning (ZSL and FSL) scenarios. However, since they are trained on different datasets, performance varies widely across tasks between …

A Multi-dimensional study on Bias in Vision-Language models

In recent years, joint Vision-Language (VL) models have increased in popularity and capability. Very few studies have attempted to investigate bias in VL models, even though it is a well-known issue in both individual modalities.This paper presents …