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..
LLM Evaluators Recognize and Favor Their Own Generations
Authors: Arjun Panickssery, Samuel Bowman, Shi Feng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main findings are as follows:" and "We base our experiments on 2,000 randomly sampled news articles from two datasets: XSUM (Narayan et al., 2018) and CNN/Daily Mail (Nallapati et al., 2016) |
| Researcher Affiliation | Collaboration | Arjun Panickssery1 Samuel R. Bowman2 Shi Feng3 1MATS 2New York University, Anthropic PBC 3George Washington University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code for evaluating GPT-4, GPT-3.5, and Llama 2, as well as for fine-tuning Llama 2, is available at https://bit.ly/llm_self_recognition. |
| Open Datasets | Yes | We base our experiments on 2,000 randomly sampled news articles from two datasets: XSUM (Narayan et al., 2018) and CNN/Daily Mail (Nallapati et al., 2016) |
| Dataset Splits | No | The paper mentions '500 training articles' and 'The remaining 500 articles and associated summaries are used for evaluation' but does not explicitly define a separate validation split or its purpose. |
| Hardware Specification | No | Section 3.1 mentions 'The Llama models are quantized to 8 bits and fine-tuned for one epoch', but does not specify the type of hardware (e.g., GPU/CPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper mentions using specific LLMs like 'Llama-2-7b-chat' and 'GPT-3.5' and 'GPT-4', and 'Adam optimization', but does not provide specific version numbers for underlying software frameworks or libraries (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The evaluators are trained to predict the final token, representing the correct choice among two options, using supervised learning with cross-entropy loss. ... The Llama models are quantized to 8 bits and fine-tuned for one epoch using Adam optimization and a learning rate of 5.0 × 10−5. |