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 | Archive PDF | Plain Text | 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. |