OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
Zoom: Join Zoom Meeting: zoom.us/j/99057170141?pwd=H6jZR72T3cJPLOU8iq5jSWNxz8YbBV.1
Meeting ID: 990 5717 0141; Passcode: 421752
Abstract: Generative large language models (LLMs) are increasingly used in the social sciences for data generation and text annotation, yet concerns remain about their biases and performance. This talk addresses these issues in two parts. First, we examine political biases in LLM output by analyzing responses to sensitive political questions across languages spoken in politically divergent societies. Focusing on OpenAI’s GPT-3.5 and GPT-4, we find that model outputs are more conservative in languages associated with conservative societies, and that GPT-4 tends to produce more left-leaning responses than GPT-3.5. Second, we evaluate LLM performance on complex annotation tasks using specialized political science texts. We propose a memory-based annotation approach, where the model retains its own prior classifications. This method significantly outperforms few-shot chain-of-thought prompting, suggesting a new direction for improving LLM-based annotation tasks.
