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RA-DIT: Retrieval-Augmented Dual Instruction Tuning

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2310.01352

Summary

Large language models store knowledge only in their weights, so they handle rare facts poorly and are costly to update. Retrieval-augmented language models (RALMs) address this, but earlier systems either needed expensive retrieval-aware pre-training (REALM, RETRO, Atlas) or attached a retriever to a frozen LLM (REPLUG, In-Context RALM) that was never trained to use retrieved text. RA-DIT is a cheaper middle path. It adds retrieval to an existing LLM through two separate instruction-tuning stages. Starting from LLaMA and the DRAGON+ dense retriever, the first stage (LM-ft) fine-tunes the LLM on prompts with a retrieved "background" passage added in front. This teaches the model to use helpful evidence and ignore irrelevant passages. The second stage (R-ft) updates only the retriever's query encoder so that its rankings match the passages the LLM finds most useful. Each stage improves results by itself, and combining them gives further gains. The largest model, RA-DIT 65B, reaches state-of-the-art results on knowledge-intensive zero- and few-shot benchmarks. This shows that lightweight fine-tuning can compete with costly continued pre-training.

Key points

  • Training data: LM-ft uses 20 Hugging Face datasets in five groups: dialogue (oasst1), open-domain QA, reading comprehension (including SQuAD v2), summarization (CNN/DailyMail) and chain-of-thought reasoning (e.g. GSM8K, StrategyQA). R-ft uses the QA subsets plus FreebaseQA and MS MARCO. Both stages also mix in unsupervised text.
  • Inference: each of the top-k retrieved chunks is paired with the prompt separately. The model's outputs for these pairs are then averaged, weighted by the retriever's scores. R-ft trains the query encoder with a generalized LM-Supervised Retrieval (LSR) objective, which minimizes the KL divergence between the retriever's scores and the LLM's preferences.
  • Results vs. un-tuned in-context RALM: on average +8.9% at 0-shot and +1.4% at 5-shot. On MMLU the gains are +8.2% (0-shot) and +0.7% (5-shot); on Natural Questions they are +22% (0-shot) and +3.8% (5-shot).
  • Results vs. Atlas 11B: RA-DIT 65B scores 7.2% higher on average across 8 knowledge-intensive tasks in the 64-shot fine-tuning setting.
  • Side effect and release: training on SQuAD v2 makes the model more likely to answer "I don't know" when a retrieved passage is off-topic. The authors released their Common Crawl indexing scripts and prompt templates at github.com/facebookresearch/RA-DIT.

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: Natural language processing · AI & machine learning

Cited by

132 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 124 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2310.01352 · arXiv 2310.01352

@misc{lin2023ra,
  title = {{RA-DIT: Retrieval-Augmented Dual Instruction Tuning}},
  author = {Lin, Xi Victoria and Chen, Xilun and Chen, Mingda and Shi, Weijia and Lomeli, Maria and James, Rich and Rodriguez, Pedro and Kahn, Jacob and Szilvasy, Gergely and Lewis, Mike and Zettlemoyer, Luke and Yih, Scott},
  year = {2023},
  doi = {10.48550/arxiv.2310.01352},
  eprint = {2310.01352},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.01352}
}

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