Papers with MALLM
MALLM: Multi-Agent Large Language Models Framework (2025.emnlp-demos)
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| Challenge: | Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. |
| Approach: | They propose an open-source framework that enables systematic analysis of multi-agent debates. |
| Outcome: | The proposed framework enables systematic analysis of multi-agent debate components. |
Beyond Single-Audio: Advancing Multi-Audio Processing in Audio Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing evaluations of audio large language models focus on single audio inputs, but real-world applications often require processing multiple audio streams simultaneously. |
| Approach: | They propose a multi-audio evaluation benchmark that combines 20 audio inputs from 11 audio tasks to capture audio context. |
| Outcome: | The proposed model outperforms baseline models and achieves high data efficiency without human annotations. |
Generalization or Memorization? Multi-Agent vs. Baseline LLMs and AutoML Models for Tabular Classification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used for structured tabular data. |
| Approach: | They evaluate a representative modular Multi-Agent LLM framework against state-of-the-art AutoML systems and established baselines. |
| Outcome: | The proposed model outperforms AutoML on pre-cutoff and post-cut off datasets. |