Fetching the paper…
Reading the bibliography…
Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations.
Parameter-Efficient Transfer Learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; de Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 1902
Earlier work this paper cites.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
Earlier work this paper cites.
Fine-Tuning Language Models from Human Preferences
Ziegler, D. M.; Stiennon, N.; Wu, J.; Brown, T. B.; Radford, A.; Amodei, D.; Christiano, P.; and Irving, G. 2020 · 1909
Earlier work this paper cites.
On the limited memory method for large scale optimization: Mathematical Programming B
Liu, D.; and Nocedal, J. 1989 · 1989
Earlier work this paper cites.
Language Models are Open Knowledge Graphs
Wang, C.; Liu, X.; and Song, D. 2020 · 2010
Earlier work this paper cites.
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Nguyen, A.; Clune, J.; Bengio, Y.; Dosovitskiy, A.; and Yosinski, J. 2017 · 2017
Earlier work this paper cites.
Learning to generate reviews and discovering sentiment
Radford, A.; Jozefowicz, R.; and Sutskever, I. 2017 · 2017
Earlier work this paper cites.
Natural Questions: A Benchmark for Question Answering Research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; Toutanova, K.; Jones, L.; Kelcey, M.; Chang, M.-W.; Dai, A. M.; Uszkoreit, J.; Le, Q.; and Petrov, S. 2019 · 2019
Earlier work this paper cites.
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Bai, Y.; Jones, A.; Ndousse, K.; Askell, A.; Chen, A.; DasSarma, N.; Drain, D.; Fort, S.; Ganguli, D.; Henighan, T.; Joseph, N.; Kadavath, S.; Kernion, J.; Conerly, T.; El-Showk, S.; Elhage, N.; Hatfield-Dodds, Z.; Hernandez, D.; Hume, T.; Johnston, S.; Kravec, S.; Lovitt, L.; Nanda, N.; Olsson, C.; Amodei, D.; Brown, T.; Clark, J.; McCandlish, S.; Olah, C.; Mann, B.; and Kaplan, J. 2022 · 2022
Cited alongside, same era.
Discovering Latent Knowledge in Language Models Without Supervision
Burns, C.; Ye, H.; Klein, D.; and Steinhardt, J. 2022 · 2022
Cited alongside, same era.
Self-critiquing models for assisting human evaluators
Saunders, W.; Yeh, C.; Wu, J.; Bills, S.; Ouyang, L.; Ward, J.; and Leike, J. 2022 · 2022
Later among the works it cites.
Do Language Models Know When They’re Hallucinating References?
Agrawal, A.; Mackey, L.; and Kalai, A. T. 2023 · 2023
Closest in time.
The Internal State of an LLM Knows When its Lying
Azaria, A.; and Mitchell, T. 2023 · 2023
Closest in time.
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Manakul, P.; Liusie, A.; and Gales, M. J. F. 2023 · 2023
Closest in time.
Sources of Hallucination by Large Language Models on Inference Tasks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S.; Hilton, J.; and Evans, O. 2022 · 2022
Cited alongside, same era.
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
Liu, X.; Park, D. H.; Azadi, S.; Zhang, G.; Chopikyan, A.; Hu, Y.; Shi, H.; Rohrbach, A.; and Darrell, T. 2022 · 2022
Cited alongside, same era.
Teaching language models to support answers with verified quotes
Menick, J.; Trebacz, M.; Mikulik, V.; Aslanides, J.; Song, F.; Chadwick, M.; Glaese, M.; Young, S.; Campbell-Gillingham, L.; Irving, G.; and McAleese, N. 2022 · 2022
Cited alongside, same era.
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
Li, J.; Cheng, X.; Zhao, W. X.; Nie, J.-Y.; and Wen, J.-R. 2023a
Cited in the paper.
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Li, K.; Patel, O.; Viégas, F.; Pfister, H.; and Wattenberg, M. 2023b
Cited in the paper.
LLaMA: Open and Efficient Foundation Language Models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; Rodriguez, A.; Joulin, A.; Grave, E.; and Lample, G. 2023a
Cited in the paper.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; Bikel, D.; Blecher, L.; Ferrer, C. C.; Chen, M.; Cucurull, G.; Esiobu, D.; Fernandes, J.; Fu, J.; Fu, W.; Fuller, B.; Gao, C.; Goswami, V.; Goyal, N.; Hartshorn, A.; Hosseini, S.; Hou, R.; Inan, H.; Kardas, M.; Kerkez, V.; Khabsa, M.; Kloumann, I.; Korenev, A.; Koura, P. S.; Lachaux, M.-A.; Lavril, T.; Lee, J.; Liskovich, D.; Lu, Y.; Mao, Y.; Martinet, X.; Mihaylov, T.; Mishra, P.; Molybog, I.; Nie, Y.; Poulton, A.; Reizenstein, J.; Rungta, R.; Saladi, K.; Schelten, A.; Silva, R.; Smith, E. M.; Subramanian, R.; Tan, X. E.; Tang, B.; Taylor, R.; Williams, A.; Kuan, J. X.; Xu, P.; Yan, Z.; Zarov, I.; Zhang, Y.; Fan, A.; Kambadur, M.; Narang, S.; Rodriguez, A.; Stojnic, R.; Edunov, S.; and Scialom, T. 2023b
Cited in the paper.
McKenna, N.; Li, T.; Cheng, L.; Hosseini, M. J.; Johnson, M.; and Steedman, M. 2023 · 2023
Closest in time.
Stanford Alpaca: An Instruction-following LLaMA model
Taori, R.; Gulrajani, I.; Zhang, T.; Dubois, Y.; Li, X.; Guestrin, C.; Liang, P.; and Hashimoto, T. B. 2023 · 2023
Closest in time.
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Zheng, L.; Chiang, W.-L.; Sheng, Y.; Zhuang, S.; Wu, Z.; Zhuang, Y.; Lin, Z.; Li, Z.; Li, D.; Xing, E. P.; Zhang, H.; Gonzalez, J. E.; and Stoica, I. 2023 · 2023
Closest in time.