most citedBetter Explain Transformers by Illuminating Important Information

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents

Jiaxiang Chen, Aotian Luo, Zhouyi Zheng +3

No single market strategy always wins: momentum, mean reversion, risk control,and event-driven rules can each succeed or fail as market conditions change.Rather than asking large l…

physics.soc-ph2026★ 1 cited

FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution

Mingxi Zou, Jiaxiang Chen, Aotian Luo +4

Conventional financial strategy evaluation relies on isolated backtests in static environments. Such evaluations assess each policy independently, overlook correlations and interac…

cs.CV2024

Investigating the Scaling Effect of Instruction Templates for Training Multimodal Language Model

Shijian Wang, Linxin Song, Jieyu Zhang +9

Current multimodal language model (MLM) training approaches overlook the influence of instruction templates. Previous research deals with this problem by leveraging hand-crafted or…

cs.CL2024

Adaptive In-conversation Team Building for Language Model Agents

Linxin Song, Jiale Liu, Jieyu Zhang +5

Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particula…

cs.CL2024★ 1 cited

Better Explain Transformers by Illuminating Important Information

Linxin Song, Yan Cui, Ao Luo +2

Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers…