LLMs | Tharindu Cyril Weerasooriya

Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala

Tue, 01 Apr 2025 00:00:00 +0000

ARTICLE: Annotator Reliability Through In-Context Learning

Sat, 01 Mar 2025 00:00:00 +0000

When humans label data for AI (like deciding if a tweet is offensive), how do we know who’s doing good work versus who’s careless or biased? Traditional methods struggle because on subjective tasks, sincere people genuinely disagree. We built ARTICLE, which uses large language models to check if an annotator’s labels are internally consistent and align with what the AI predicts. This helps identify reliable annotators without silencing minority perspectives—crucial for building fair AI systems that don’t just reflect the majority view.

Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer

Wed, 01 Jan 2025 00:00:00 +0000

Note

Presenting at NeurIPS (Math-AI Workshop)
Date: December 6, 2025
Time: Sat 3:30 p.m. - 4:15 p.m. Location: NeurIPS 2025 - Workshop Upper Level Ballroom 6A Session Type: Poster Session

ProRefine: Inference-Time Prompt Refinement with Textual Feedback

Wed, 01 Jan 2025 00:00:00 +0000

When multiple AI agents work together (like one agent planning and another executing), they rely heavily on good prompts. But writing perfect prompts is hard, and even small mistakes can cascade through the system, causing failures. ProRefine solves this by creating a feedback loop: one AI agent evaluates how well the prompt worked and suggests improvements, then another agent refines the prompt based on that feedback—all happening automatically at inference time without any training.

We tested ProRefine on mathematical reasoning tasks and it improved accuracy by 3-37% compared to standard methods. Even better, it lets smaller, cheaper AI models perform nearly as well as much larger, expensive ones. This makes powerful AI more accessible and cost-effective, especially for complex multi-agent systems that are becoming critical in commercial applications.

Harnessing Business and Media Insights with Large Language Models

Mon, 01 Jan 2024 00:00:00 +0000