Shepherd: A Critic for Language Model Generation
By Tianlu Wang, Ping Yu, Xiaoqing Tan, Sean O'Brien, Ramakanth Pasunuru, Jane Dwivedi-Yu, O. Yu. Golovneva, Luke S. Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz. 2023
115 citations recorded by the source corpus.
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shepherd, a language model specifically tuned to critique responses and suggest refinements, extending beyond the capabilities of an untuned model to identify diverse errors and provide suggestions to remedy them. At the core of our approach is a high quality feedback dataset, which we curate from community feedback and human annotations. Even though Shepherd is small (7B parameters), its critiques are either equivalent or preferred to those from established models including ChatGPT. Using GPT-4 for evaluation, Shepherd reaches an average win-rate of 53-87% compared to competitive alternatives. In human evaluation, Shepherd strictly outperforms other models and on average closely ties with ChatGPT.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Safety, Robustness & Interpretability
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Citations in the corpus
- Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, background): “Another area of emphasis is automatically generating feedback (Madaan et al., 2023; Wang et al., 2023) from the model itself or external robust evaluators to guide the safety enhancement.”
- UltraFeedback: Boosting Language Models with High-quality Feedback (2023, unknown): “Compared with two critique models, we find that (the community-trained) Shepherd almost always fails to provide high-quality feedback.”
- ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback (2023, unknown): “Compared with two critique models, we find that (the community-trained) Shepherd almost always fails to provide high-quality feedback.”
- Prometheus: Inducing Fine-grained Evaluation Capability in Language Models (2023, methodology): “Wang et al. (2023b) introduced PandaLM, a fine-tuned LLM to evaluate the generated text and explain its reliability on various preference datasets.”