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researcher

Mengnan Du

14 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author10
  • last author3

Across the 13 of 14 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.CL4
  • cs.AI1
  • cs.CR1
  • cs.CV1
  • cs.IR1
ORCID 0000-0002-1614-6069
same name
  • Mengnan Du — 22 papers, h 26
  • Mengnan Du — 10 papers, h 18
  • Mengnan Du — 8 papers, h 3
  • Mengnan Du — 8 papers
  • Mengnan Du — 4 papers, h 3
  • Mengnan Du — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedLawLLM: Law Large Language Model for the US Legal System

40 citations · 98 across the 14 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024★ 40 cited

LawLLM: Law Large Language Model for the US Legal System

Dong Shu, Haoran Zhao, Xukun Liu +3

In the rapidly evolving field of legal analytics, finding relevant cases and accurately predicting judicial outcomes are challenging because of the complexity of legal language, wh…

cs.CL2024★ 8 cited

Towards Uncovering How Large Language Model Works: An Explainability Perspective

Haiyan Zhao, Fan Yang, Bo Shen +2

Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opa…

cs.CL2024★ 3 cited

DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure

Junyi Ye, Mengnan Du, Guiling Wang

This paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analy…

cs.CL2023★ 2 cited

Mitigating Shortcuts in Language Models with Soft Label Encoding

Zirui He, Huiqi Deng, Haiyan Zhao +2

Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. In this work, we aim to answer the fo…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.