REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models (2404.11782v1)
Abstract: The extensive scope of LLMs across various domains underscores the critical importance of responsibility in their application, beyond natural language processing. In particular, the randomized nature of LLMs, coupled with inherent biases and historical stereotypes in data, raises critical concerns regarding reliability and equity. Addressing these challenges are necessary before using LLMs for applications with societal impact. Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable LLM outputs through aggregation. Specifically, we develop a Monte Carlo method based on repeated sampling to find a reliable output close to the mean of the underlying distribution of possible outputs. We formally define the terms such as reliability and bias, and design an equity-aware aggregation to minimize harmful bias while finding a highly reliable output. REQUAL-LM does not require specialized hardware, does not impose a significant computing load, and uses LLMs as a blackbox. This design choice enables seamless scalability alongside the rapid advancement of LLM technologies. Our system does not require retraining the LLMs, which makes it deployment ready and easy to adapt. Our comprehensive experiments using various tasks and datasets demonstrate that REQUAL- LM effectively mitigates bias and selects a more equitable response, specifically the outputs that properly represents minority groups.
- Machine bias. In Ethics of data and analytics, pages 254–264. Auerbach Publications.
- RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1941–1955, Online. Association for Computational Linguistics.
- Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc.
- Shikha Bordia and Samuel R. Bowman. 2019. Identifying and reducing gender bias in word-level language models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 7–15, Minneapolis, Minnesota. Association for Computational Linguistics.
- Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183–186.
- Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173–8188, Online. Association for Computational Linguistics.
- Rick Durrett. 2010. Probability: theory and examples. Cambridge university press.
- Double equivariance for inductive link prediction for both new nodes and new relation types.
- Debiasing pre-trained language models via efficient fine-tuning. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 59–69, Dublin, Ireland. Association for Computational Linguistics.
- Mithraranking: A system for responsible ranking design. In Proceedings of the 2019 International Conference on Management of Data, pages 1913–1916.
- John Hammersley. 2013. Monte carlo methods. Springer Science & Business Media.
- LLM-blender: Ensembling large language models with pairwise ranking and generative fusion. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14165–14178, Toronto, Canada. Association for Computational Linguistics.
- Masahiro Kaneko and Danushka Bollegala. 2021. Debiasing pre-trained contextualised embeddings. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1256–1266, Online. Association for Computational Linguistics.
- Milo: Model-agnostic subset selection framework for efficient model training and tuning.
- On measuring social biases in sentence encoders. pages 622–628.
- An empirical survey of the effectiveness of debiasing techniques for pre-trained language models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland. Association for Computational Linguistics.
- StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5356–5371, Online. Association for Computational Linguistics.
- One embedder, any task: Instruction-finetuned text embeddings. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1102–1121, Toronto, Canada. Association for Computational Linguistics.
- Mitigating voter attribute bias for fair opinion aggregation. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’23, page 170–180, New York, NY, USA. Association for Computing Machinery.
- Farewell to aimless large-scale pretraining: Influential subset selection for language model. In Findings of the Association for Computational Linguistics: ACL 2023, pages 555–568, Toronto, Canada. Association for Computational Linguistics.
- Measuring and reducing gendered correlations in pre-trained models. Technical report.
- How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humanizing llms. arXiv preprint arXiv:2401.06373.
- Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers).
- Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1651–1661, Florence, Italy. Association for Computational Linguistics.
- Sana Ebrahimi (5 papers)
- Nima Shahbazi (9 papers)
- Abolfazl Asudeh (46 papers)