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Ebad Shabbir

ACL 2026×2推理代码安全效率与系统评测与基准
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #2/3ACL 2026Poster· ACL Industry Track

FROST : Factual Reasoning via Optimized Stochastic Trajectories in Large Language Models during Inference

Soumedhik Bharati · Ebad Shabbir · Jiechao Gao

摘要

Large language models face a trade-off between factual consistency and reasoningdiversity: deterministic decoding prioritizes reliability but may miss alternativesolution paths, while high-temperature sampling increases exploration at the costof accuracy. We present FROST (Factual Reasoning via O...

推理代码安全OpenReview 项目页
作者位次 #5/8ACL 2026Poster· ACL Industry Track

Are Large Language Models Economically Viable for Industry Deployment?

Abdullah Mohammad · Sushant Kumar Ray · Pushkar Arora · Rafiq Ali · Ebad Shabbir · Gautam Siddharth Kashyap · Jiechao Gao · Usman Naseem

摘要

Generative AI—powered by Large Language Models (LLMs)—is increasingly deployed in industry across healthcare decision support, financial analytics, enterprise retrieval, and conversational automation, where reliability, efficiency, and cost control are critical. In such settings, models must sati...

效率与系统评测与基准检索与知识OpenReview 项目页

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