3 citations · 7 across the 15 of their papers we have counts for
17 papers
Evaluating List Construction and Temporal Understanding capabilities of Large Language Models
Alexandru Dumitru, V Venktesh, Adam Jatowt +1
Large Language Models (LLMs) have demonstrated immense advances in a wide range of natural language tasks. However, these models are susceptible to hallucinations and errors on par…
Sample Efficient Demonstration Selection for In-Context Learning
Kiran Purohit, V Venktesh, Sourangshu Bhattacharya +1
The in-context learning paradigm with LLMs has been instrumental in advancing a wide range of natural language processing tasks. The selection of few-shot examples (exemplars / dem…
Correctness is not Faithfulness in RAG Attributions
Jonas Wallat, Maria Heuss, Maarten de Rijke +1
Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated respon…
DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models
Zijian Zhang, Vinay Setty, Yumeng Wang +1
With the rapid advancement of neural language models, the deployment of over-parameterized models has surged, increasing the need for interpretable explanations comprehensible to h…
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning
Kiran Purohit, Venktesh V, Raghuram Devalla +3
Answering reasoning-based complex questions over text and hybrid sources, including tables, is a challenging task. Recent advances in large language models (LLMs) have enabled in-c…
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Local feature selection in machine learning provides instance-specific explanations by focusing on the most relevant features for each prediction, enhancing the interpretability of…