On Zero-Shot Counterspeech Generation by LLMs
Punyajoy Saha Aalok Agrawal Abhik Jana Chris Biemann Animesh Mukherjee
With the emergence of numerous Large Language Models (LLM), the usage of such
models in various Natural Language Processing (NLP) applications is increasing
extensively. Counterspeech generation is one such key task where efforts are
made to develop generative models by fine-tuning LLMs with hatespeech -
counterspeech pairs, but none of these attempts explores the intrinsic
properties of large language models in zero-shot settings. In this work, we
present a comprehensive analysis of the performances of four LLMs namely GPT-2,
DialoGPT, ChatGPT and FlanT5 in zero-shot settings for counterspeech
generation, which is the first of its kind. For GPT-2 and DialoGPT, we further
investigate the deviation in performance with respect to the sizes (small,
medium, large) of the models. On the other hand, we propose three different
prompting strategies for generating different types of counterspeech and
analyse the impact of such strategies on the performance of the models. Our
analysis shows that there is an improvement in generation quality for two
datasets (17%), however the toxicity increase (25%) with increase in model
size. Considering type of model, GPT-2 and FlanT5 models are significantly
better in terms of counterspeech quality but also have high toxicity as
compared to DialoGPT. ChatGPT are much better at generating counter speech than
other models across all metrics. In terms of prompting, we find that our
proposed strategies help in improving counter speech generation across all the
models.