Stanford CS25: V3 I Retrieval Augmented Language Models

Stanford CS25: V3 I Retrieval Augmented Language Models

Retrieval Augmentation in Language Models

  • Retrieval augmentation involves using an external memory (retriever) to retrieve relevant information and provide it as context to a language model (generator).
  • Different variations of retrieval augmentation include updating the query encoder, updating both the query and document encoders, and using in-context retrieval.
  • Instruction tuning and training the retriever and generator together are important in retrieval augmentation.
  • Open questions and areas for future research include pre-training of retrieval augmented systems, scaling laws, and measuring the effectiveness of retrieval.
  • There is potential for multimodal retrieval augmentation and the use of retrieval augmentation in other domains beyond text.
  • Optimizing the entire retrieval augmentation system is emphasized over optimizing the language model alone.

Tuning Transformer architecture like convolution layers

  • Can we optimize Transformer architecture similar to how we optimize convolution layers?
  • Paper on light convolutions suggests the computational model is slightly better than the Transformer for GPU computation.

Two-stage process for retrieval

  • Use bm25 as the first stage to cast a wide net for retrieval.
  • Use a dense model as the second stage to narrow down the results.

Adapting models to domain-specific areas

  • Two potential ways: instrumental tuning or meta-tuning.
  • All approaches will likely come together in the end, with fine-tuning on the specific use case.

Hardware for efficient retrieval

  • There are dedicated retrieval hardware solutions in development or already available.
  • Efficient dense retrieval is a significant market.

Hallucination in language models

  • Hallucination refers to when the language model produces output that does not correspond to the retrieved information.
  • Often misinterpreted as a mistake or incorrectness, but it's more specific to counterfactual ground truth.

Defining ground truth and controlling hallucination

  • Ground truth can be defined differently based on the index used.
  • Architecture can be designed to control the level of hallucination and the reliance on the ground truth.

Tuning the temperature for sampling

  • Temperature affects sampling by controlling how flat the distribution is.
  • Even with a low temperature, random outputs can still occur.
  • More sophisticated methods are needed to control sampling.

Many interesting questions and thanks for the discussion.

Summarize and save any online content

Save this summary