3 papers
cs.CL2026
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
Lexiang Tang, Weihao Gao, Bingchen Zhao +4
Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However,…
cs.CL2026
Benchmarking Retrieval-Augmented Generation for Chemistry
Xianrui Zhong, Bowen Jin, Siru Ouyang +5
Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that…
cs.CL2025
MedCite: Can Language Models Generate Verifiable Text for Medicine?
Xiao Wang, Mengjue Tan, Qiao Jin +5
Existing LLM-based medical question-answering systems lack citation generation and evaluation capabilities, raising concerns about their adoption in practice. In this work, we intr…