arXiv subjects

Utkarsh Mujumdar

Publications and source records attributed to Utkarsh Mujumdar.

2 recordsLinked to original sources

arXiv2024

Argumentative Experience: Reducing Confirmation Bias on Controversial Issues through LLM-Generated Multi-Persona Debates

Multi-persona debate systems powered by large language models (LLMs) show promise in reducing confirmation bias, which can fuel echo chambers and social polarization. However, empirical evidence remains limited on whether they meaningfully shift user attention toward belief-challenging content, promote belief change, or outperform traditional debiasing strategies. To investigate this, we compare an LLM-based multi-persona debate system with a two-stance retrieval-based system, exposing participants to multiple viewpoints on controversial topics. By collecting eye-tracking data, belief change measures, and qualitative feedback, our results show that while the debate system does not significantly increase attention to opposing views, or make participants shift away from prior beliefs, it does provide a buffering effect against bias caused by individual cognitive tendency. These findings shed light on both the promise and limits of multi-persona debate systems in information seeking, and we offer design insights to guide future work toward more balanced and reflective information engagement.

cs.HC↗

arXiv2024

Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer

The use of Large Language Models (LLMs) for program code generation has gained substantial attention, but their biases and limitations with non-English prompts challenge global inclusivity. This paper investigates the complexities of multilingual prompt-based code generation. Our evaluations of LLMs, including CODELLAMA and CODEGEMMA, reveal significant disparities in code quality for non-English prompts; we also demonstrate the inadequacy of simple approaches like prompt translation, bootstrapped data augmentation, and fine-tuning. To address this, we propose a zero-shot cross-lingual approach using a neural projection technique, integrating a cross-lingual encoder like LASER to map multilingual embeddings from it into the LLM's token space. This method requires training only on English data and scales effectively to other languages. Results on a translated and quality-checked MBPP dataset show substantial improvements in code quality. This research promotes a more inclusive code generation landscape by empowering LLMs with multilingual capabilities to support the diverse linguistic spectrum in programming.

cs.CL↗