Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment
Authors: Souradip Chakraborty, Sujay Bhatt, Udari Sehwag, Soumya Suvra Ghosal, Jiahao Qiu, Mengdi Wang, Dinesh Manocha, Furong Huang, Alec Koppel, Sumitra Ganesh
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a comprehensive empirical analysis of our proposed framework, tested across various open-source datasets and state-of-the-art models (Lambert et al., 2024). Our findings demonstrate Collab s effectiveness in aligning language model outputs with specific target rewards. For implementation, we set the number of tokens sampled (top-p) p = 10 and the decoding alignment parameter α = 1. Reproducibility is ensured through the use of publicly available resources. |
| Researcher Affiliation | Collaboration | 1JPMorgan AI Research 2University of Maryland, College Park 3Princeton University |
| Pseudocode | Yes | Algorithm 1 Mixture of Agents based Controlled Decoding for LLM Alignment |
| Open Source Code | No | Reproducibility is ensured through the use of publicly available resources. This statement refers to resources used for experiments, not the authors' own code. |
| Open Datasets | Yes | 1. Evaluation-1 to Evaluation-4 (Task-I): For this task, we utilize the Berkeley Nectar dataset (Zhu et al., 2023) to test the agent s capacity for multi-turn dialogues and question answering. 2. Evaluation-5 to Evaluation-7 (Task-II): We employ the HH-RLHF dataset (Bai et al., 2022) to assess the agent s helpfulness and ethical alignment in response generation. |
| Dataset Splits | No | For evaluation, we compare the performance of the response generated by the language model corresponding to each prompt in the test dataset. Following (Khanov et al., 2024; Chakraborty et al., 2024b), we limit the maximum length of the prompt and generated continuation to 128 and 2048 tokens, respectively. The paper mentions using a "test dataset" but does not specify the explicit splits (e.g., percentages, counts) for the datasets used. |
| Hardware Specification | Yes | We run all experiments with Python 3.7.4 and Py Torch 1.9.0. For all experimentation, we use two Nvidia RTX A6000 GPUs. |
| Software Dependencies | Yes | We run all experiments with Python 3.7.4 and Py Torch 1.9.0. |
| Experiment Setup | Yes | For implementation, we set the number of tokens sampled (top-p) p = 10 and the decoding alignment parameter α = 1. |