Towards Tracing Knowledge in Language Models Back to the Training Data
By Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu. 2022
71 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Large Language Models, Safety, Robustness & Interpretability
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In Artificial Intelligence
In Management Science and Operations Research
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Citations in the corpus
- Knowledge Conflicts for LLMs: A Survey (2024, unknown): “While LLMs primarily acquire knowledge during pre-training (Zhou et al., 2023a; Kaddour et al., 2023; Naveed et al., 2023; Akyürek et al., 2022; Singhal et al., 2022), the vast and often unreliable nature of internet-sourced training data (Bender et al., 2021; Weidinger et al., 2021) can lead to…”
- Large Language Model Alignment: A Survey (2023, background): “, 2021), hallucination (Lin et al., 2021; Akyurek et al., 2022; Ji et al., 2023), and inconsistency (Bubeck et al.”
- RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval (2024, unknown): “…Min et al., 2023; Ram et al., 2023), making it easy to provide a system with current knowledge particular to some domain and enabling easy interpretability and provenance tracking, whereas the parametric knowledge of LLMs is opaque and difficult to trace back to its source (Akyurek et al., 2022).”
- How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN (2021, unknown): “Other papers in this direction include Akyurek et al. (2022), Han and Tsvetkov (2022), and Elazar et al. (2022).”