5 papers
Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
Ishaan Watts, Catherine Li, Sachin Goyal +2
Pretraining optimizers are tuned to produce the strongest possible base model, on the assumption that a stronger starting point yields a stronger model after subsequent changes lik…
PARIKSHA: A Large-Scale Investigation of Human-LLM Evaluator Agreement on Multilingual and Multi-Cultural Data
Ishaan Watts, Varun Gumma, Aditya Yadavalli +3
Evaluation of multilingual Large Language Models (LLMs) is challenging due to a variety of factors -- the lack of benchmarks with sufficient linguistic diversity, contamination of…
RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?
Adrian de Wynter, Ishaan Watts, Tua Wongsangaroonsri +30
Large language models (LLMs) and small language models (SLMs) are being adopted at remarkable speed, although their safety still remains a serious concern. With the advent of multi…
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models
Divyanshu Aggarwal, Ashutosh Sathe, Ishaan Watts +1
Parameter Efficient Finetuning (PEFT) has emerged as a viable solution for improving the performance of Large Language Models (LLMs) without requiring massive resources and compute…
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma +8
There has been a surge in LLM evaluation research to understand LLM capabilities and limitations. However, much of this research has been confined to English, leaving LLM building…