Zhehao Zhang

I am a first year Master student in Computer Science at Dartmouth College. Currently, I am a research intern at Stanford SALT Lab under the supervision of Diyi Yang. Previously, I worked as a Research Intern at Microsoft Research Lab – Asia. I received my bachelor's degree in Artificial Intelligence Honor Class at Shanghai Jiao Tong University.

My research interests lie in Natural Language Processing (NLP).

Please feel free to contact me by email!

Mail  /  Résumé  /  Google Scholar  /  Github 

• 2024 June 25th: A new preprint is released! Please check DARG which is a dynamic evaluation framework aiming to augment current reasoning benchmarks from the level of reasoning graphs.
• 2024 Mar 13th: One first-author paper on LLMs for hierarchical table analysis is accepted to NAACL 2024. See you in Mexico City!
• 2024 Mar: Honored to give a talk on Augmented Language Models at TRIP Lab at Dartmouth, hosted by Prof. Yaoqing Yang. Recording and the slide are available.
• 2024 Feb: I will join Adobe Research as Research Intern in this summer. See you in San Jose and bay area!
• 2023 Dec 14th: One first-author papers from my undergraduate is accepted to ICASSP 2024.
• 2023 Oct 27th: The paper titled "Can Large Language Models Transform Computational Social Science?" is accepted to Computational Linguistics.
• 2023 Oct 7th: Two first-author papers from my undergraduate are accepted to EMNLP 2023. See you in Singapore!

My current research interests lie in multiple fields in NLP including:

• Tool-augmented LLMs
• Dynamic evaluation of LLMs
• Computational social science (NLP for social good)
• LLM-based agents
• Vison-Language Models

TL; DR: We propose a dynamic evaluation framework named DARG, aimed at augmenting current reasoning benchmarks from the level of reasoning graphs. We evaluate 15 SOTA LLMs and observe a consistent performance decrease for all LLMs as the complexity level increases, along with increasing biases on some datasets.

TL; DR: We propose a tool-augmented LLM framework named $E^5$ that contains 5 stages to solve the challenging real-life hierarchical table analysis task which achieves an 85.08 exact match score and 93.11 GPT-4-Eval Score. We also introduce $F^3$ which significantly reduces the token length while maintaining useful information to analyze such huge tables.

TL; DR: We systematically evaluate LLMs' reasoning ability on tabular data and establish a comprehensive taxonomy on operation and reasoning types for table analysis. hen, we propose CRT-QA, a dataset of complex reasoning QA over tables. We propose ARC which effectively utilizes table analysis tools to solve table reasoning tasks without manually-annotated exemplars.

TL; DR: We analyze the generation process of HS biases from a causal view and identify two confounders that cause the biases. Propose Multi-Task Intervention and Data-Specific Intervention to mitigate them.

TL; DR: We provide a road map for using LLMs as CSS tools and contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 24 representative CSS benchmarks.

Dartmouth College, Master of Science in Computer Science, 2023 - 2025 (expected)

Shanghai Jiao Tong University, B.Eng. in Artificial Intelligence (Honor Class), 2019 - 2023

Social and Language Technologies (SALT) lab at Stanford, Research Intern

Adobe Research, Research Intern

Data, Knowledge, and Intelligence group at Microsoft Research Lab – Asia, Research Intern