Fetching the paper…
Reading the bibliography…
The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning, enhancing LLMs with excellent applicability and effectiveness in a wide range of contexts.
T. Brown et al., “Language models are few-shot learners,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2020, pp. 1877–1901
1901
Earlier work this paper cites.
D. M. Ziegler et al., “Fine-tuning language models from human preferences,” 2020, arXiv:1909.08593
1909
Earlier work this paper cites.
R. Likert, “A technique for the measurement of attitudes,” Arch. Psychol. , vol. 22, no. 140, pp. 55–55, 1932
1932
Earlier work this paper cites.
H. Everett, “Generalized lagrange multiplier method for solving problems of optimum allocation of resources,” Oper. Res. , vol. 11, no. 3, pp. 399–417, 1963
1963
Earlier work this paper cites.
L. R. Bahl, F. Jelinek, and R. L. Mercer, “A maximum likelihood approach to continuous speech recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. IEEE Trans. Pattern Anal. Mach. Intell., no. 2, pp. 179–190, 1983
1983
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Mach. Learn. , vol. 8, no. 3, pp. 229–256, 1992
1992
Earlier work this paper cites.
S. F. Chen and J. Goodman, “An empirical study of smoothing techniques for language modeling,” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 1996, pp. 310–318
1996
Earlier work this paper cites.
R. Maclin and J. W. Shavlik, “Creating advice-taking reinforcement learners,” Mach. Learn. , vol. 22, no. 1, pp. 251–281, 1996
1996
Earlier work this paper cites.
S. F. Chen and J. Goodman, “An empirical study of smoothing techniques for language modeling,” Comput. Speech Lang. , vol. 13, no. 4, pp. 359–394, 1999
1999
Earlier work this paper cites.
R. Rosenfeld, “Two decades of statistical language modeling: where do we go from here?” Proc. IEEE , vol. 88, no. 8, pp. 1270–1278, 2000
2000
Earlier work this paper cites.
Y. Bengio, R. Ducharme, and P. Vincent, “A neural probabilistic language model,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2000
2000
Earlier work this paper cites.
C. L. Isbell, M. Kearns, D. Kormann, S. Singh, and P. Stone, “Cobot in lambdamoo: A social statistics agent,” in Proc. Natl. Conf. Artif. Intell. Conf. Innov. Appl. Artif. Intell. , 2000, pp. 36 – 41
2000
Earlier work this paper cites.
C. L. Isbell, C. R. Shelton, M. Kearns, S. Singh, and P. Stone, “Cobot: A social reinforcement learning agent,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2001
2001
Earlier work this paper cites.
C. Isbell, C. R. Shelton, M. Kearns, S. Singh, and P. Stone, “A social reinforcement learning agent,” in Proc. Int. Conf. Auton. Agents , 2001, p. 377–384
2001
Earlier work this paper cites.
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, “A neural probabilistic language model,” J. Mach. Learn. Res. , vol. 3, p. 1137–1155, 2003
2003
Earlier work this paper cites.
J. Gao and C.-Y. Lin, “Introduction to the special issue on statistical language modeling,” ACM Trans. Asian Lang. Inf. Process. , vol. 3, no. 2, p. 87–93, 2004
2004
Earlier work this paper cites.
C. Zhai and J. Lafferty, “A study of smoothing methods for language models applied to information retrieval,” ACM Trans. Inf. Syst. , vol. 22, no. 2, pp. 179–214, 2004
2004
Earlier work this paper cites.
C. L. Isbell, M. Kearns, S. Singh, C. R. Shelton, P. Stone, and D. Kormann, “Cobot in lambdamoo: An adaptive social statistics agent,” Auton. Agents Multi-Agent Syst. , vol. 13, no. 3, pp. 327–354, 2006
2006
Earlier work this paper cites.
W. B. Knox and P. Stone, “Tamer: Training an agent manually via evaluative reinforcement,” in Proc. IEEE Int. Conf. Dev. Learn. , 2008, pp. 292–297
2008
Earlier work this paper cites.
W. B. Knox and P. Stone, “Interactively shaping agents via human reinforcement: the tamer framework,” in Proc. Int. Conf. Knowl. Capture , 2009, p. 9–16
2009
Earlier work this paper cites.
T.-Y. Liu, “Learning to rank for information retrieval,” Found. Trends Inf. Retr. , vol. 3, no. 3, pp. 225–331, 2009
2009
Earlier work this paper cites.
W. B. Knox and P. Stone, “Combining manual feedback with subsequent mdp reward signals for reinforcement learning,” in Proc. Int. Joint Conf. Auton. Agents Multiagent Syst. , 2010, p. 5–12
2010
Earlier work this paper cites.
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” J. Mach. Learn. Res. , vol. 12, no. 76, pp. 2493–2537, 2011
2011
Earlier work this paper cites.
J. Fürnkranz and E. Hüllermeier, Preference Learning: An Introduction . Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 1–17
2011
Earlier work this paper cites.
P. M. Pilarski, M. R. Dawson, T. Degris, F. Fahimi, J. P. Carey, and R. S. Sutton, “Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning,” in Proc. IEEE Int. Conf. Rehabil. Rob. , 2011, pp. 1–7
2011
Earlier work this paper cites.
H. B. Suay and S. Chernova, “Effect of human guidance and state space size on interactive reinforcement learning,” in Proc. IEEE Int. Symp. Robot Human Interact. Commun. , 2011, pp. 1–6
2011
Earlier work this paper cites.
R. Akrour, M. Schoenauer, and M. Sebag, “Preference-based policy learning,” in Proc. Eur. Conf. Mach. Learn. Princ. Pract. Knowl. Discovery Databases , 2011
2011
Earlier work this paper cites.
W. Cheng, J. Fürnkranz, E. Hüllermeier, and S.-H. Park, “Preference-based policy iteration: Leveraging preference learning for reinforcement learning,” in Proc. Eur. Conf. Mach. Learn. Princ. Pract. Knowl. Discovery Databases , 2011, pp. 312–327
2011
Earlier work this paper cites.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 2011, pp. 142–150
2011
Earlier work this paper cites.
W. B. Knox, “Learning from human-generated reward,” Ph.D. dissertation, 2012
2012
Earlier work this paper cites.
W. B. Knox and P. Stone, “Reinforcement learning from simultaneous human and mdp reward,” in Proc. Int. Joint Conf. Auton. Agents Multiagent Syst. , 2012, p. 475–482
2012
Earlier work this paper cites.
J. Fürnkranz, E. Hüllermeier, W. Cheng, and S.-H. Park, “Preference-based reinforcement learning: a formal framework and a policy iteration algorithm,” Mach. Learn. , vol. 89, no. 1, pp. 123–156, 2012
2012
Earlier work this paper cites.
R. Akrour, M. Schoenauer, and M. Sebag, “April: Active preference learning-based reinforcement learning,” in Proc. Eur. Conf. Mach. Learn. Princ. Pract. Knowl. Discovery Databases , 2012, pp. 116–131
2012
Earlier work this paper cites.
A. Wilson, A. Fern, and P. Tadepalli, “A bayesian approach for policy learning from trajectory preference queries,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2012
2012
Earlier work this paper cites.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. Int. Conf. Learn. Represent. Workshop Track , 2013
2013
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2013
2013
Earlier work this paper cites.
W. B. Knox and P. Stone, “Learning non-myopically from human-generated reward,” in Proc. Int. Conf. Intell. User Interfaces , 2013, p. 191–202
2013
Earlier work this paper cites.
C. Wirth and J. Fürnkranz, “A policy iteration algorithm for learning from preference-based feedback,” in Proc. Int. Symp. Intell. Data Anal. , 2013, pp. 427–437
2013
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proc. Conf. Empir. Methods Nat. Lang. Process. , 2014, pp. 1532–1543
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Proc. Int. Conf. Neural Inf. Process. Syst. , 2014
2014
Earlier work this paper cites.
R. Akrour, M. Schoenauer, J.-C. Souplet, and M. Sebag, “Programming by feedback,” in Proc. Int. Conf. Mach. Learn. , 2014, pp. 3385 – 3393
2014
Earlier work this paper cites.
I. Goodfellow et al., “Generative adversarial nets,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2014
2014
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proc. Int. Conf. Learn. Represent. , 2015
2015
Earlier work this paper cites.
C. Daniel, O. Kroemer, M. Viering, J. Metz, and J. Peters, “Active reward learning with a novel acquisition function,” Auton. Robot. , vol. 39, no. 3, pp. 389–405, 2015
2015
Earlier work this paper cites.
L. El Asri, B. Piot, M. Geist, R. Laroche, and O. Pietquin, “Score-based inverse reinforcement learning,” in Proc. Int. Joint Conf. Auton. Agents Multiagent Syst. , 2016, p. 457–465
2016
Earlier work this paper cites.
C. Wirth, J. Fürnkranz, and G. Neumann, “Model-free preference-based reinforcement learning,” in Proc. AAAI Conf. Artif. Intell. , 2016, p. 2222–2228
2016
Earlier work this paper cites.
S. I. Wang, P. Liang, and C. D. Manning, “Learning language games through interaction,” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 2016, pp. 2368–2378
2016
Earlier work this paper cites.
R. Nallapati, B. Zhou, C. dos Santos, Ç. Gu̇lçehre, and B. Xiang, “Abstractive text summarization using sequence-to-sequence RNNs and beyond,” in Proc. Conf. Comput. Nat. Lang. Learn. , 2016, pp. 280–290
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2017
2017
Earlier work this paper cites.
C. Wirth, R. Akrour, G. Neumann, and J. Fürnkranz, “A survey of preference-based reinforcement learning methods,” J. Mach. Learn. Res. , vol. 18, no. 136, pp. 1–46, 2017
2017
Earlier work this paper cites.
A. Vaswani et al., “Attention is all you need,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Völske, M. Potthast et al., “TL;DR: Mining Reddit to learn automatic summarization,” in Proc. Workshop New Front. Summ. , 2017, pp. 59 – 63
2017
Earlier work this paper cites.
M. E. Peters et al., “Deep contextualized word representations,” in Proc. Conf. N. Am. Chapter Assoc. Comput. Linguist. , 2018, pp. 2227–2237
2018
Earlier work this paper cites.
M. Barlier, R. Laroche, and O. Pietquin, “Training dialogue systems with human advice,” in Proc. Int. Joint Conf. Auton. Agents Multiagent Syst. , 2018, p. 999–1007
2018
Earlier work this paper cites.
B. Ibarz, J. Leike, T. Pohlen, G. Irving, S. Legg, and D. Amodei, “Reward learning from human preferences and demonstrations in atari,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2018
2018
Earlier work this paper cites.
J. Kreutzer, J. Uyheng, and S. Riezler, “Reliability and learnability of human bandit feedback for sequence-to-sequence reinforcement learning,” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 2018, pp. 1777–1788
2018
Earlier work this paper cites.
G. Irving, P. Christiano, and D. Amodei, “Ai safety via debate,” 2018, arXiv:1805.00899
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. Conf. N. Am. Chapter Assoc. Comput. Linguist. , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019
2019
Earlier work this paper cites.
W. S. Cho et al., “Towards coherent and cohesive long-form text generation,” in Proc. Workshop Narrative Understanding , 2019, pp. 1–11
2019
Earlier work this paper cites.
F. Böhm, Y. Gao, C. M. Meyer, O. Shapira, I. Dagan, and I. Gurevych, “Better rewards yield better summaries: Learning to summarise without references,” in Proc. Conf. Empir. Methods Nat. Lang. Process. Int. Jt. Conf. Nat. Lang. Process. , 2019, pp. 3110–3120
2019
Earlier work this paper cites.
B. Hancock, A. Bordes, P.-E. Mazare, and J. Weston, “Learning from dialogue after deployment: Feed yourself, chatbot!” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 2019, pp. 3667–3684
2019
Earlier work this paper cites.
S. Yi et al., “Towards coherent and engaging spoken dialog response generation using automatic conversation evaluators,” in Proc. Int. Conf. Nat. Lang. Gener. , 2019, pp. 65–75
2019
Earlier work this paper cites.
D. Brown, W. Goo, P. Nagarajan, and S. Niekum, “Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations,” in Proc. Int. Conf. Mach. Learn. , 2019, pp. 783–792
2019
Earlier work this paper cites.
N. Stiennon et al., “Learning to summarize with human feedback,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2020, pp. 3008–3021
2020
Cited alongside, same era.
S. Gehman, S. Gururangan, M. Sap, Y. Choi, and N. A. Smith, “RealToxicityPrompts: Evaluating neural toxic degeneration in language models,” in Proc. Findings Annu. Meet. Assoc. Comput. Linguist. , 2020, pp. 3356–3369
2020
Cited alongside, same era.
C. Raffel et al., “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res. , vol. 21, no. 1, 2020
2020
Cited alongside, same era.
W. Zhou and K. Xu, “Learning to compare for better training and evaluation of open domain natural language generation models,” in Proc. AAAI Conf. Artif. Intell. , 2020, pp. 9717–9724
2020
Cited alongside, same era.
Y. Wang et al., “Self-instruct: Aligning language models with self-generated instructions,” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 2023, pp. 13 484–13 508
2023
Later among the works it cites.
Z. Sun et al., “Principle-driven self-alignment of language models from scratch with minimal human supervision,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2023, pp. 2511–2565
2023
Later among the works it cites.
G. Mukobi et al., “SuperHF: Supervised iterative learning from human feedback,” in Proc. Int. Conf. Neural Inf. Process. Syst. Workshop , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
S. Welleck, I. Kulikov, S. Roller, E. Dinan, K. Cho, and J. Weston, “Neural text generation with unlikelihood training,” in Proc. Int. Conf. Learn. Represent. , 2020
2020
Cited alongside, same era.
2021
Cited alongside, same era.
L. Weidinger et al., “Ethical and social risks of harm from language models,” 2021, arXiv:2112.04359
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
D. Hendrycks et al., “Measuring massive multitask language understanding,” in Proc. Int. Conf. Learn. Represent. , 2021
2021
Cited alongside, same era.
M. Chen et al., “Evaluating large language models trained on code,” 2021, arXiv:2107.03374
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Zhang, F. Liu, J. Wong, P. Abbeel, and J. E. Gonzalez, “The wisdom of hindsight makes language models better instruction followers,” in Proc. Int. Conf. Mach. Learn. , 2023, pp. 41 414–41 428
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Zheng, W.-L. Chiang et al., “Judging LLM-as-a-judge with MT-bench and chatbot arena,” Proc. Int. Conf. Neural Inf. Process. Syst. Datasets Benchmarks , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Suzgun et al., “Challenging BIG-bench tasks and whether chain-of-thought can solve them,” in Proc. Findings Annu. Meet. Assoc. Comput. Linguist. , 2023, pp. 13 003–13 051
2023
Later among the works it cites.
P. Wang et al., “Large language models are not fair evaluators,” 2023, arXiv:2305.17926
2023
Later among the works it cites.
R. Ramamurthy et al., “Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization,” in Proc. Int. Conf. Learn. Represent. , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
N. Muennighoff et al., “Crosslingual generalization through multitask finetuning,” in Proc. Annu. Meet. Assoc. Comput. Linguist. , 2023, pp. 15 991–16 111
2023
Later among the works it cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2023, pp. 34 892–34 916
2023
Later among the works it cites.
W. Dai et al., “InstructBLIP: Towards general-purpose vision-language models with instruction tuning,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2023, pp. 49 250–49 267
2023
Later among the works it cites.
2023
Later among the works it cites.
OpenAI, “Gpt-4 technical report,” 2024, arXiv:2303.08774
2024
Closest in time.
A. Q. Jiang et al., “Mixtral of experts,” 2024, arXiv:2401.04088
2024
Closest in time.
J. Ji et al., “Ai alignment: A comprehensive survey,” 2024, arXiv:2310.19852
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Shanahan, “Talking about large language models,” Commun. ACM , vol. 67, no. 2, p. 68–79, 2024
2024
Closest in time.
J. Dai et al., “Safe RLHF: Safe reinforcement learning from human feedback,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
Z. Sun et al., “SALMON: Self-alignment with principle-following reward models,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
G. Guo, R. Zhao, T. Tang, X. Zhao, and J.-R. Wen, “Beyond imitation: Leveraging fine-grained quality signals for alignment,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.
K. Yang, D. Klein, A. Celikyilmaz, N. Peng, and Y. Tian, “RLCD: Reinforcement learning from contrastive distillation for LM alignment,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
G. Wang, S. Cheng, X. Zhan, X. Li, S. Song, and Y. Liu, “Openchat: Advancing open-source language models with mixed-quality data,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
P. Cheng et al., “Adversarial preference optimization,” 2024, arXiv:2311.08045
2024
Closest in time.
2024
Closest in time.
L. Li et al., “Tool-augmented reward modeling,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
A. Sinha, A. Balashankar, A. Beirami, T. Avrahami, J. Chen, and A. Beutel, “Break it, imitate it, fix it: Robustness by generating human-like attacks,” Trans. Mach. Learn. Res. , 2024
2024
Closest in time.
F. Song et al., “Preference ranking optimization for human alignment,” in Proc. AAAI Conf. Artif. Intell. , 2024, pp. 18 990–18 998
2024
Closest in time.
2024
Closest in time.
K. Ethayarajh, W. Xu, N. Muennighoff, D. Jurafsky, and D. Kiela, “Model alignment as prospect theoretic optimization,” in Proc. Int. Conf. Mach. Learn. , 2024
2024
Closest in time.
R. Zheng et al., “Improving generalization of alignment with human preferences through group invariant learning,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
H. Lightman et al., “Let’s verify step by step,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
T. Coste, U. Anwar, R. Kirk, and D. Krueger, “Reward model ensembles help mitigate overoptimization,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.
X. Li et al., “Self-alignment with instruction backtranslation,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
T. Moskovitz et al., “Confronting reward model overoptimization with constrained RLHF,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
H. Zhang et al., “CPPO: Continual learning for reinforcement learning with human feedback,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
Z. Li et al., “ReMax: A simple, effective, and efficient reinforcement learning method for aligning large language models,” in Proc. Int. Conf. Mach. Learn. , 2024
2024
Closest in time.
2024
Closest in time.
L. Chen et al., “Alpagasus: Training a better alpaca model with fewer data,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.
H. Liu, C. Sferrazza, and P. Abbeel, “Chain of hindsight aligns language models with feedback,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
R. Liu, R. Yang, C. Jia, G. Zhang, D. Yang, and S. Vosoughi, “Training socially aligned language models on simulated social interactions,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
C. Wang, Y. Jiang, C. Yang, H. Liu, and Y. Chen, “Beyond reverse KL: Generalizing direct preference optimization with diverse divergence constraints,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
T. Liu et al., “Statistical rejection sampling improves preference optimization,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.
Y. Wang et al., “PandaLM: An automatic evaluation benchmark for LLM instruction tuning optimization,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
S. Kim et al., “Prometheus: Inducing evaluation capability in language models,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
J. Li, S. Sun, W. Yuan, R.-Z. Fan, hai zhao, and P. Liu, “Generative judge for evaluating alignment,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
T. Sorensen et al., “A roadmap to pluralistic alignment,” 2024, arXiv:2402.05070
2024
Closest in time.
S. Zhao, J. Dang, and A. Grover, “Group preference optimization: Few-shot alignment of large language models,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
Closest in time.
2024
Closest in time.