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cs.CL2026
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison
Pratuat Amatya, Vinay Setty, V Venktesh +1
Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment steps such as RLHF do not focus o…
cs.LG2026
Data Efficient Sample Selection for In-Context Learning
V Venktesh, Cem levi, Cem Levi +1
The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optimal combination of demonstrat…
cs.IR2026
When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
V Venktesh, Mandeep Rathee, Avishek Anand
Modern retrieval pipelines increasingly rely on query reformulation and neural reranking to improve effectiveness, but this comes at a significant computational cost and introduces…