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Stay on topic with Classifier-Free Guidance (2306.17806v1)

Published 30 Jun 2023 in cs.CL, cs.CV, and cs.LG

Abstract: Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure LLMing. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75\% preference for GPT4All using CFG over baseline.

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References (81)
  1. How does negative prompt work? https://stable-diffusion-art.com/how-negative-prompt-work/.
  2. Falcon-40B: an open large language model with state-of-the-art performance. 2023.
  3. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all, 2023.
  4. A general language assistant as a laboratory for alignment. arXiv preprint arXiv:2112.00861, 2021.
  5. The sciqa scientific question answering benchmark for scholarly knowledge. Scientific Reports, 13(1):7240, 2023.
  6. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022.
  7. R. Barzilay and M. Lapata. Modeling local coherence: An entity-based approach. Computational Linguistics, 34(1):1–34, 2008.
  8. Aqua: Asp-based visual question answering. In Practical Aspects of Declarative Languages: 22nd International Symposium, PADL 2020, New Orleans, LA, USA, January 20–21, 2020, Proceedings 22, pages 57–72. Springer, 2020.
  9. Leace: Perfect linear concept erasure in closed form. arXiv preprint arXiv:2306.03819, 2023.
  10. S. Biderman and E. Raff. Fooling moss detection with pretrained language models. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 2933–2943, 2022.
  11. Pythia: A suite for analyzing large language models across training and scaling, 2023.
  12. Piqa: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 7432–7439, 2020.
  13. Findings of the 2014 workshop on statistical machine translation. In Proceedings of the Ninth Workshop on Statistical Machine Translation, pages 12–58, Baltimore, Maryland, USA, June 2014. Association for Computational Linguistics.
  14. Neural photo editing with introspective adversarial networks. In International Conference on Learning Representations.
  15. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  16. Evaluating large language models trained on code. 2021.
  17. J. Chorowski and N. Jaitly. Towards better decoding and language model integration in sequence to sequence models. arXiv preprint arXiv:1612.02695, 2016.
  18. Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019.
  19. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv:1803.05457v1, 2018.
  20. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021.
  21. Vqgan-clip: Open domain image generation and editing with natural language guidance. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVII, pages 88–105. Springer, 2022.
  22. Plug and play language models: A simple approach to controlled text generation. arXiv preprint arXiv:1912.02164, 2019.
  23. Qlora: Efficient finetuning of quantized llms, 2023.
  24. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.
  25. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2019.
  26. P. Dhariwal and A. Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780–8794, 2021.
  27. Compositional visual generation with energy based models. Advances in Neural Information Processing Systems, 33:6637–6647, 2020.
  28. Towards winoqueer: Developing a benchmark for anti-queer bias in large language models. arXiv preprint arXiv:2206.11484, 2022.
  29. A theoretical analysis of the repetition problem in text generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12848–12856, 2021.
  30. Stylegan-nada: Clip-guided domain adaptation of image generators. arXiv preprint arXiv:2108.00946, 2021.
  31. A framework for few-shot language model evaluation, Sept. 2021.
  32. Realtoxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009.11462, 2020.
  33. Learning to forget: Continual prediction with lstm. Neural computation, 12(10):2451–2471, 2000.
  34. More than you’ve asked for: A comprehensive analysis of novel prompt injection threats to application-integrated large language models. arXiv preprint arXiv:2302.12173, 2023.
  35. J. Ho and T. Salimans. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021.
  36. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751, 2019.
  37. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551, 2017.
  38. Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858, 2019.
  39. Diffusionclip: Text-guided diffusion models for robust image manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2426–2435, 2022.
  40. Openassistant conversations–democratizing large language model alignment. arXiv preprint arXiv:2304.07327, 2023.
  41. Gedi: Generative discriminator guided sequence generation. arXiv preprint arXiv:2009.06367, 2020.
  42. Diffusion-lm improves controllable text generation. Advances in Neural Information Processing Systems, 35:4328–4343, 2022.
  43. Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097, 2022.
  44. Common diffusion noise schedules and sample steps are flawed, 2023.
  45. Editgan: High-precision semantic image editing. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
  46. Program induction by rationale generation: Learning to solve and explain algebraic word problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 158–167, Vancouver, Canada, July 2017. Association for Computational Linguistics.
  47. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. arXiv preprint arXiv:2303.08896, 2023.
  48. Controllable text generation with neurally-decomposed oracle. arXiv preprint arXiv:2205.14219, 2022.
  49. Efficient estimation of word representations in vector space. In International Conference on Learning Representations, 2013.
  50. Crosslingual generalization through multitask finetuning. ArXiv, abs/2211.01786, 2022.
  51. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pages 16784–16804. PMLR, 2022.
  52. Codegen: An open large language model for code with multi-turn program synthesis. In The Eleventh International Conference on Learning Representations, 2023.
  53. Show your work: Scratchpads for intermediate computation with language models. In Deep Learning for Code Workshop, 2022.
  54. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022.
  55. The lambada dataset: Word prediction requiring a broad discourse context. arXiv preprint arXiv:1606.06031, 2016.
  56. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277, 2023.
  57. Glove: Global vectors for word representation. In Conference on Empirical Methods in Natural Language Processing, 2014.
  58. Improving language understanding by generative pre-training. 2018.
  59. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
  60. Scaling language models: Methods, analysis & insights from training gopher, 2021.
  61. L. Reynolds and K. McDonell. Prompt programming for large language models: Beyond the few-shot paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–7, 2021.
  62. High-resolution image synthesis with latent diffusion models, 2021.
  63. A. Rutherford. ANOVA and ANCOVA: a GLM approach. John Wiley & Sons, 2011.
  64. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  65. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106, 2021.
  66. Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations.
  67. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100, 2022.
  68. Bloom: A 176b-parameter open-access multilingual language model. ArXiv, abs/2211.05100, 2022.
  69. Trusting your evidence: Hallucinate less with context-aware decoding. arXiv preprint arXiv:2305.14739, 2023.
  70. Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203, 2019.
  71. Denoising diffusion implicit models. In International Conference on Learning Representations.
  72. Sequentially controlled text generation. arXiv preprint arXiv:2301.02299, 2023.
  73. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca, 2023.
  74. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  75. B. Wang and A. Komatsuzaki. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax, May 2021.
  76. Self-consistency improves chain of thought reasoning in language models. In ICLR 2023, 2023.
  77. Finetuned language models are zero-shot learners. In International Conference on Learning Representations.
  78. Chain-of-thought prompting elicits reasoning in large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 24824–24837. Curran Associates, Inc., 2022.
  79. Wizardlm: Empowering large language models to follow complex instructions, 2023.
  80. K. Yang and D. Klein. Fudge: Controlled text generation with future discriminators. arXiv preprint arXiv:2104.05218, 2021.
  81. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019.
Citations (37)
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Summary

  • The paper demonstrates that adapting classifier-free guidance (CFG) to language models improves prompt adherence by adjusting guidance strength.
  • It reports significant benchmark improvements, with models like LLaMA-7B achieving state-of-the-art performance on the LAMBADA dataset.
  • CFG provides efficiency gains akin to doubling model size and integrates smoothly with techniques like Chain-of-Thought for enhanced reasoning.

Stay on Topic with Classifier-Free Guidance: An In-Depth Evaluation

The research paper titled "Stay on Topic with Classifier-Free Guidance" presents an extensive evaluation and application of Classifier-Free Guidance (CFG) as an inference-time methodology to improve prompt adherence in LLM applications. Initially successful in text-to-image generation within diffusion models, CFG has been effectively adapted here for LLMs to solve diverse language-related tasks.

Core Contributions and Methodology:

  1. Adaptation of CFG for LLMs: The authors adapt CFG, originally used in text-to-image generation, to enhance autoregressive LLMs. By adjusting the guidance strength, denoted as γ\gamma, they show how CFG can modulate models to adhere more closely to provided prompts, facilitating better alignment between the input prompt and generated content.
  2. Benchmark Performance: The research demonstrates significant improvements in various benchmarks, including zero-shot tasks across multiple model families such as Pythia, GPT-2, and LLaMA. Notably, the LLaMA-7B model achieves state-of-the-art (SOTA) performance on the LAMBADA dataset, surpassing the previous leader PaLM-540B.
  3. Efficiency Gains: CFG is shown to provide performance gains that mimic those of models with twice the parameter count, suggesting that CFG effectively amplifies model capacity at the inference stage without increasing model size.
  4. Stacking with Other Techniques: The methodology coexists with other inference-time techniques like Chain-of-Thought and Self-Consistency, providing compounded improvements in complex reasoning tasks.
  5. Human Evaluations: Human assessments reveal a 75% preference for outputs using CFG over baseline responses, reinforcing its practical effectiveness in enhancing content adherence and coherence.

Implications and Future Directions:

The implications of these findings are profound both theoretically and practically. Theoretically, CFG offers a simple yet powerful approach to enhance generation tasks by increasing the weighting of prompt information throughout the decoding process. Practically, the application of CFG can lead to more efficient deployment of smaller LLMs in environments where compute resources are constrained, as it can reliably mimic larger models' performance. This demonstrates potential for reducing computing costs without sacrificing performance, making robust LLMs more accessible across varied platforms and applications.

Moreover, the exploration of negative prompting within CFG introduces nuanced control over undesired content, which could refine chatbot interactions and mitigate unintended biases present in model outputs. This adds a layer of flexibility and targeted modulation that could prove beneficial in domains requiring high precision and context awareness, such as automated customer service or therapeutic chatbots.

In the context of natural language processing and artificial intelligence, this paper paves the way for extended research into dynamically adjustable guidance applications, enhancing interaction models by optimizing for context retention and accuracy. Future research could explore CFG’s efficacy across different languages and tasks beyond those tested, and potentially involve combined CFP and further fine-tuning to explore the interaction of training-time and inference-time interventions.

In conclusion, this paper underscores the potency of CFG as a readily applicable tool for elevating LLM output quality. Its out-of-the-box integration capability with existing models exemplifies a smart utilization of available technological frameworks, setting a precedent for forthcoming improvements in AI alignment and fidelity to human intent.

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