Selective Generation for Controllable Language Models
Authors: Minjae Lee, Kyungmin Kim, Taesoo Kim, Sangdon Park
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the efficacy of the SGen family in achieving a desired FDR-E level with comparable selection efficiency to those from baselines on both open and closed source GLMs. |
| Researcher Affiliation | Academia | Minjae Lee GSAI POSTECH EMAIL Kyungmin Kim GSAI POSTECH EMAIL Taesoo Kim SCS & SCP Ga Tech EMAIL Sangdon Park GSAI & CSE POSTECH EMAIL |
| Pseudocode | Yes | Algorithm 1 Entailment Set Learning with a False Entailment Rate (FER) Guarantee |
| Open Source Code | Yes | Code and datasets are provided at https://github.com/ml-postech/selective-generation. |
| Open Datasets | Yes | We use two GLMs, GPT-3.5-Turbo and Alpaca-7B, alongside the Natural Questions (NQ) dataset to annotate entailment labels for question-answer pairs. [...] we create a dataset on textual entailment using the Natural Questions (NQ) dataset [17] for each GLM. |
| Dataset Splits | Yes | Approximately 7.3k (7,374) and 4.6k (4,595) samples are labeled for Alpaca-7B and GPT-3.5-Turbo, respectively, and both are split into calibration and test data at an 8:2 ratio. |
| Hardware Specification | Yes | Our system environment consists of 4 NVIDIA A100 80GB with 128 CPUs. |
| Software Dependencies | No | The paper mentions models like 'GPT-3.5-Turbo and Alpaca-7B' and 'deberta-v2-xxlarge-mnli' but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | To control an FDR-E, we use two user-specified parameters (ε, δ), where we use (0.25, 0.02) unless specified. For our methods (i.e., SGen Semi, SGen Semi No MS, and SGen Semi-Sup No MS ), we have five control parameters (εS, δS, δE, δW ), where we maps as follows: εS = ε, δS = (δ δW )/2, δE = (δ δW )/2, δW = 10 5. |