FLAME : Factuality-Aware Alignment for Large Language Models
Authors: Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Scott Yih, Xilun Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our proposed FLAME guides LLMs to output more factual responses while maintaining their instruction-following capability. |
| Researcher Affiliation | Collaboration | University of Waterloo1, Carnegie Mellon University2, Meta AI3 |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | While we do not provide the code to reproduce the main experimental results, we provide all the necessary information and URL links to training and evaluation data. |
| Open Datasets | Yes | At SFT stage, we fine-tune PT on two seed datasets: (1) Instruction-following training (IFT) data from Li et al. [2024], consisting of 3200 instruction response pairs created by humans from Open Assistant dataset [OASST; Köpf et al., 2023]; (2) evaluation following training (EFT) data from Yuan et al. [2024] |
| Dataset Splits | No | For the experiment, we compile training and evaluation datasets comprising 500 and 183 diverse human entities, respectively (further details provided in Appendix A.1). The paper explicitly mentions training and evaluation (test) sets for some experiments but does not explicitly define a separate validation set. |
| Hardware Specification | Yes | We conduct fine-tuning with full parameters on 64 NVIDIA A100 (80GB) GPUs. |
| Software Dependencies | No | The paper mentions several software components and models (e.g., Llama-2 70B, FACTSCORE, DRAGON+, nltk.tokenize) but does not provide specific version numbers for these software dependencies to ensure reproducibility. |
| Experiment Setup | Yes | We fine-tune our models for 500 steps with a batch size of 32 and 64 on respective SFT and DPO stages. The learning rate and maximum sequence length is set to 1e 6 (which decays to 1e 7) and 2048, respectively. At SFT stage, we mix the IFT and EFT while at DPO stage, we set β = 0.1 and uniformly sample between self rewarding (x, y+, y ) and factuality reward (x, ytrue, yfalse) preference data. |