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CodecLM: Aligning Language Models with Tailored Synthetic Data (2404.05875v1)

Published 8 Apr 2024 in cs.CL, cs.AI, and cs.LG
CodecLM: Aligning Language Models with Tailored Synthetic Data

Abstract: Instruction tuning has emerged as the key in aligning LLMs with specific task instructions, thereby mitigating the discrepancy between the next-token prediction objective and users' actual goals. To reduce the labor and time cost to collect or annotate data by humans, researchers start to explore the use of LLMs to generate instruction-aligned synthetic data. Recent works focus on generating diverse instructions and applying LLM to increase instruction complexity, often neglecting downstream use cases. It remains unclear how to tailor high-quality data to elicit better instruction-following abilities in different target instruction distributions and LLMs. To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. Drawing on the Encode-Decode principles, we use LLMs as codecs to guide the data generation process. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution, and then decode metadata to create tailored instructions. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples. Extensive experiments on four open-domain instruction following benchmarks validate the effectiveness of CodecLM over the current state-of-the-arts.

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Introducing CodecLM: A Framework for Tailoring High-Quality Synthetic Data for LLM Alignment

Overview of CodecLM

Recent advances in LLMs have highlighted the importance of instruction tuning to align LLMs with specific task instructions. A pivotal challenge in this line of work is generating high-quality synthetic data that closely matches the target instruction distributions and LLMs, a task that CodecLM approaches with a novel methodology. CodecLM operates on the principles of Encode-Decode, using LLMs as codecs to navigate the data generation process, starting from encoding seed instructions into metadata and subsequently decoding this metadata to produce tailored instructions. Enhanced by Self-Rubrics and Contrastive Filtering, CodecLM systematically refines the generated data, ensuring it is both diverse and aligned with the designated tasks.

Encoding Seed Instructions into Metadata

CodecLM introduces an innovative step of encoding seed instructions into concise keywords that encapsulate the target instruction distribution. This metadata, focusing on use cases and required skills, enables a generalizable yet precise formulation of the instruction's intent and complexity level. Such a method not only streamlines the generation process but also sidesteps the labor-intensive requirement for vast annotated datasets.

Decoding Metadata to Generate Tailored Instructions

With the metadata in place, CodecLM decodes it to craft basic instructions, which are further refined through the Self-Rubrics process. This allows for adapting the instruction complexity based on the metadata, ensuring the synthetic instructions are both challenging and relevant to the targeted downstream task. The iterative nature of this process, informed by the generated rubrics and actions, lends a dynamic adaptability to the system, allowing for the generation of instructions that are finely tuned to the model's needs.

Self-Rubrics and Contrastive Filtering

The Self-Rubrics mechanism in CodecLM empowers the system to evaluate and adjust the complexity of instructions dynamically, catering to a wide range of downstream tasks. Following this, Contrastive Filtering helps select the most effective instruction-response pairs by estimating the target LLM's performance gap compared to a stronger LLM model. This not only identifies areas where the target LLM could improve but also maximizes the instructional value of each data point used in tuning.

Empirical Validation and Implications

Extensive experiments across four open-domain instruction-following benchmarks demonstrate CodecLM's superiority over existing state-of-the-art methods. By setting new benchmarks, CodecLM not only underscores the significance of custom-tailored synthetic data but also opens new avenues in instruction tuning for LLMs across different sizes and capabilities.

The implications of CodecLM extend beyond immediate practical applications in LLM tuning. Theoretically, it presents a refined understanding of how LLMs can be tailored for specific tasks through targeted synthetic data generation. This adaptability foretells a future where LLMs can be more efficiently and effectively specialized, reducing reliance on extensive human-annotated datasets—a notable advance in the pursuit of more autonomous and agile AI systems.

Future Directions

CodecLM's architecture invites further exploration into enhancing the quality and applicability of synthetic data for LLM alignment. Future work might include refining the metadata definition to encompass broader or more nuanced aspects of instructions, developing more sophisticated mechanisms for Self-Rubrics and Contrastive Filtering, and integrating CodecLM with other alignment techniques for synergistic effects. As LLMs continue to evolve, frameworks like CodecLM will play a crucial role in harnessing their potential for a wide array of applications, marking a significant step forward in the field of generative AI and machine learning.

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References (57)
  1. Palm 2 technical report. arXiv preprint arXiv:2305.10403.
  2. Ext5: Towards extreme multi-task scaling for transfer learning. arXiv preprint arXiv:2111.10952.
  3. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862.
  4. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 610–623.
  5. Knowledge distillation: A good teacher is patient and consistent. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10925–10934.
  6. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
  7. Mixture of soft prompts for controllable data generation. arXiv preprint arXiv:2303.01580.
  8. Alpagasus: Training a better alpaca with fewer data. arXiv preprint arXiv:2307.08701.
  9. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.
  10. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416.
  11. Steerlm: Attribute conditioned sft as an (user-steerable) alternative to rlhf. arXiv preprint arXiv:2310.05344.
  12. Alpacafarm: A simulation framework for methods that learn from human feedback. arXiv preprint arXiv:2305.14387.
  13. Avia Efrat and Omer Levy. 2020. The turking test: Can language models understand instructions? arXiv preprint arXiv:2010.11982.
  14. Promptbreeder: Self-referential self-improvement via prompt evolution. arXiv preprint arXiv:2309.16797.
  15. Bias and fairness in large language models: A survey. arXiv preprint arXiv:2309.00770.
  16. Koala: A dialogue model for academic research. Blog post.
  17. Laura Hanu and Unitary team. 2020. Detoxify. Github. https://github.com/unitaryai/detoxify.
  18. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300.
  19. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
  20. Unnatural instructions: Tuning language models with (almost) no human labor. arXiv preprint arXiv:2212.09689.
  21. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. arXiv preprint arXiv:2305.02301.
  22. Baseline defenses for adversarial attacks against aligned language models. arXiv preprint arXiv:2309.00614.
  23. Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
  24. Openassistant conversations–democratizing large language model alignment. arXiv preprint arXiv:2304.07327.
  25. Mark A Kramer. 1991. Nonlinear principal component analysis using autoassociative neural networks. AIChE journal, 37(2):233–243.
  26. Applying large language models and chain-of-thought for automatic scoring. arXiv preprint arXiv:2312.03748.
  27. Self-alignment with instruction backtranslation. arXiv preprint arXiv:2308.06259.
  28. Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097.
  29. Less is more: Task-aware layer-wise distillation for language model compression. In International Conference on Machine Learning, pages 20852–20867. PMLR.
  30. Wanli: Worker and ai collaboration for natural language inference dataset creation. arXiv preprint arXiv:2201.05955.
  31. Prompt injection attack against llm-integrated applications. arXiv preprint arXiv:2306.05499.
  32. Self-refine: Iterative refinement with self-feedback. arXiv preprint arXiv:2303.17651.
  33. Tuning language models as training data generators for augmentation-enhanced few-shot learning. In International Conference on Machine Learning, pages 24457–24477. PMLR.
  34. OpenAI. 2023a. Gpt-4 technical report. ArXiv, abs/2303.08774.
  35. OpenAI. 2023b. Introducing gpts. https://openai.com/blog/introducing-gpts.
  36. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744.
  37. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277.
  38. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551.
  39. Timo Schick and Hinrich Schütze. 2021. Generating datasets with pretrained language models. arXiv preprint arXiv:2104.07540.
  40. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261.
  41. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca.
  42. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805.
  43. Language models get a gender makeover: Mitigating gender bias with few-shot data interventions. arXiv preprint arXiv:2306.04597.
  44. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  45. Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations.
  46. How far can camels go? exploring the state of instruction tuning on open resources. arXiv preprint arXiv:2306.04751.
  47. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560.
  48. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652.
  49. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837.
  50. Wizardlm: Empowering large language models to follow complex instructions. arXiv preprint arXiv:2304.12244.
  51. Large language models as optimizers. arXiv preprint arXiv:2309.03409.
  52. Large language model as attributed training data generator: A tale of diversity and bias. arXiv preprint arXiv:2306.15895.
  53. A preliminary study of the intrinsic relationship between complexity and alignment. arXiv preprint arXiv:2308.05696.
  54. Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685.
  55. Lima: Less is more for alignment. arXiv preprint arXiv:2305.11206.
  56. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911.
  57. Universal and transferable adversarial attacks on aligned language models.
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Authors (8)
  1. Zifeng Wang (78 papers)
  2. Chun-Liang Li (60 papers)
  3. Vincent Perot (14 papers)
  4. Long T. Le (7 papers)
  5. Jin Miao (7 papers)
  6. Zizhao Zhang (44 papers)
  7. Chen-Yu Lee (48 papers)
  8. Tomas Pfister (89 papers)
Citations (11)
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