activity
20242026
most citedHarnessing Multiple Large Language Models: A Survey on LLM Ensemble

5 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States

Weiyi Kong, Zhuoran Li

The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes, estimands, pathways, or id…

cs.CL2026

Enhancing Multilingual Reasoning via Steerable Model Merging

Zhuoran Li, Rui Xu, Jian Yang +8

Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reaso…

cs.LG2026

NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model

Qianren Mao, Yashuo Luo, Ziqi Qin +12

The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effect…

cs.CL2025

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

Zhijun Chen, Zeyu Ji, Qianren Mao +13

We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…

cs.CV2025

Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI

Chao Ding, Mouxiao Bian, Pengcheng Chen +11

Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiti…

cs.CL2025★ 5 cited

Harnessing Multiple Large Language Models: A Survey on LLM Ensemble

Zhijun Chen, Xiaodong Lu, Jingzheng Li +12

LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…

cs.LG2024

Exploiting Latent Linearity in LLMs Improves Explainable Molecular Representation Learning

Zhuoran Li, Xu Sun, Wanyu Lin +1

Large language models (LLMs) have demonstrated broad utility across molecular domains, spanning drug discovery and materials design. Analyzing LLMs' latent representations is cruci…