1 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect
Hazel H. Kim, Andrew M. Bean, Guilherme Affonso Ferreira de Camargo +10
We introduce FramingQA, a benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations. Large language models (LLMs) oft…
Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups
Felix Drinkall, Stefan Zohren, Michael McMahon +1
Macroeconomic fluctuations and the narratives that shape them form a mutually reinforcing cycle: public discourse can spur behavioural changes leading to economic shifts, which the…
When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks
Felix Drinkall, Janet B. Pierrehumbert, Stefan Zohren
Large language models (LLMs) have shown remarkable success in language modelling due to scaling laws found in model size and the hidden dimension of the model's text representation…
Language Models Learn Metadata: Political Stance Detection Case Study
Stanley Cao, Felix Drinkall
Stance detection is a crucial NLP task with numerous applications in social science, from analyzing online discussions to assessing political campaigns. This paper investigates the…
Time Machine GPT
Felix Drinkall, Eghbal Rahimikia, Janet B. Pierrehumbert +1
Large language models (LLMs) are often trained on extensive, temporally indiscriminate text corpora, reflecting the lack of datasets with temporal metadata. This approach is not al…
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts
Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert
We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density cluster…