Attributed Text Generation via Post-hoc Research and Revision

27 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning (2023, background): “Analyzing, evaluating and mitigating these problems pose a promising direction for future work (Gao et al., 2022; Ganguli et al., 2022).”
Galactica: A Large Language Model for Science (2022, background): “Likewise, the question of how to improve the reliability of generation is an active question (Gao et al., 2022).”
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023, methodology): “Prior work in model-based evaluation mainly reports aggregated scores since the goal is a comparison between different systems being evaluated (Zhang et al., 2020; Rashkin et al., 2021; Gao et al., 2022) while we report both to see the relationship between two types of metrics.”
Prompting GPT-3 To Be Reliable (2022, unknown): “…combatting adversarial examples identiﬁed via human or AI red-teaming (Ganguli et al., 2022; Branch et al., 2022; Perez et al., 2022), detecting and handling malicious prompts such as prompt injection 5 , and avoiding toxic and hallucinated generations (Gehman et al., 2020; Gao et al., 2022).”
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing (2023, background): “[69] Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al.”