Yixin Nie

Your thoughts tell who you are: Characterize the reasoning patterns of LRMs

2025 · arXiv (Cornell University)

Current comparisons of large reasoning models (LRMs) focus on macro-level statistics such as task accuracy or reasoning length. Whether different LRMs reason differently remains an open question. To address this gap, we introduce the LLM-proposed …

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences …

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

2019

Yixin Nie, Songhe Wang, Mohit Bansal. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

What Can We Learn from Collective Human Opinions on Natural Language Inference Data?

2020

Despite the subjective nature of many NLP tasks, most NLU evaluations have focused on using the majority label with presumably high agreement as the ground truth. Less attention has been paid to the distribution of …

Llama 2: Open Foundation and Fine-Tuned Chat Models

2023 · arXiv (Cornell University)

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, …