5 papers · 1 filter
EKLAV: Elicited Key CoT Learning via Answer only superVision
Adarsh Singh, Aman Singh, Kushal Raj Bhandari +4
Reasoning distillation trains a student to reproduce both the teacher's chain-of-thought trace and its final answer. We ask whether the trace is better used as privileged training…
All Claims Are Equal, but Some Claims Are More Equal Than Others: Importance-Sensitive Factuality Evaluation of LLM Generations
Miriam Wanner, Leif Azzopardi, Paul Thomas +3
Existing methods for evaluating the factuality of large language model (LLM) responses treat all claims as equally important. This results in misleading evaluations when vital info…
Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification
Himanshu Beniwal, Youngwoo Kim, Maarten Sap +2
As large language models (LLMs) become increasingly prevalent in global applications, ensuring that they are toxicity-free across diverse linguistic contexts remains a critical cha…
CRAFT: Training-Free Cascaded Retrieval for Tabular QA
Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao +2
Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional dense retrieval models such as DT…
IOLBENCH: Benchmarking LLMs on Linguistic Reasoning
Satyam Goyal, Soham Dan
Despite the remarkable advancements and widespread applications of deep neural networks, their ability to perform reasoning tasks remains limited, particularly in domains requiring…