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From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning

Paper · 2023 · Qian Liu, Fan Zhou, Zhengbao Jiang et al.

Summary

Instruction tuning helps language models handle tasks they never saw during training. Its training data, however, usually comes from human crowdsourcing, which is costly and limited in size, or from LLM generation, which is noisy because outputs may not match inputs. The authors propose adding symbolic tasks. These can be produced automatically at any scale, and an executor labels them correctly. Their case study is SQL execution: queries built from SQUALL templates are run over tables. This yields about 200K examples, which are mixed with roughly 600K FLAN examples to further fine-tune FLAN-T5. They also test a training-free variant that inserts synthetic SQL examples as demonstrations into GPT-3 prompts. The resulting 3B model never sees table reasoning data. Even so, in zero-shot mode it beats GPT-3 (code-davinci-002) and ChatGPT on WTQ, SQA, WikiSQL-Weak and TabFact. It also improves on SVAMP math problems and keeps its scores on BBH and MMLU. The work suggests that cheap, verifiable synthetic data can enlarge instruction-tuning collections without reducing general ability.

Key points

  • Training data: about 200K synthetic SQL-execution examples, built on TAPEX-style synthesis with SQUALL templates, were merged with about 600K FLAN mixture examples. The models are initialized from FLAN-T5 (Large and XL) and called TAPEX-Zero.
  • Zero-shot table reasoning (TAPEX-Zero XL vs. FLAN-T5 XL):
    • WTQ: 50.2 vs. 39.5 (GPT-3 40.4, ChatGPT 42.9)
    • SQA: 34.1 vs. 16.8
    • WikiSQL-Weak: 70.5 vs. 38.2
    • TabFact: 72.3 vs. 66.3 (ChatGPT 68.8)
  • Training-free use: synthetic SQL demonstrations raise GPT-3 accuracy on WTQ from 40.4% to 52.9%, with no real WTQ examples involved.
  • Transfer beyond tables: the SVAMP math score rises from 21.4% to 26.5%. Results stay intact across the 27 tasks of BBH and the 57 tasks of MMLU.
  • Code: released at github.com/sail-sg/symbolic-instruction-tuning (Sea AI Lab and co-authors).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Qian Liu, Fan Zhou, Zhengbao Jiang, Longxu Dou and Min Lin. 2023. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning. arXiv:2304.07995.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2304.07995 · arXiv 2304.07995

@misc{liu2023zero,
  title = {{From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning}},
  author = {Qian Liu, Fan Zhou, Zhengbao Jiang, Longxu Dou and Min Lin},
  year = {2023},
  doi = {10.48550/arxiv.2304.07995},
  eprint = {2304.07995},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2304.07995}
}

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