title: TART: A plug-and-play Transformer module for task-agnostic reasoning
summary: Large language models (LLMs) exhibit in-context learning abilities which
  enable the same model to perform several tasks without any task-specific
  training. In contrast, traditional adaptation approaches, such as fine-tuning,
  modify the underlying models for each specific task. In-context learning,
  however, consistently underperforms task-specific tuning approaches even when
  presented with the same examples. While most existing approaches (e.g., prompt
  engineering) focus on the LLM's learned representations to patch this
  performance gap, our analysis actually reveal that LLM representations contain
  sufficient information to make good predictions. As such, we focus on the LLM's
  reasoning abilities and demonstrate that this performance gap exists due to
  their inability to perform simple probabilistic reasoning tasks. This raises an
  intriguing question: Are LLMs actually capable of learning how to reason in a
  task-agnostic manner? We answer this in the affirmative and propose TART which
  generically improves an LLM's reasoning abilities using a synthetically trained
  Transformer-based reasoning module. TART trains this reasoning module in a
  task-agnostic manner using only synthetic logistic regression tasks and
  composes it with an arbitrary real-world pre-trained model without any
  additional training. With a single inference module, TART improves performance
  across different model families (GPT-Neo, Pythia, BLOOM), model sizes (100M -
  6B), tasks (14 NLP binary classification tasks), and even across different
  modalities (audio and vision). Additionally, on the RAFT Benchmark, TART
  improves GPT-Neo (125M)'s performance such that it outperforms BLOOM (176B),
  and is within 4% of GPT-3 (175B). Our code and models are available at
  https://github.com/HazyResearch/TART .
paper_authors._id: 64891e04ef2989c5dfe6a276
paper_authors.name: Kush Bhatia
paper_authors._id: 64891e04ef2989c5dfe6a277
paper_authors.name: Avanika Narayan
paper_authors._id: 64891e04ef2989c5dfe6a278
paper_authors.name: Christopher De Sa
paper_authors._id: 64891e04ef2989c5dfe6a279
paper_authors.name: Christopher Ré
paper_title: TART: A plug-and-play Transformer module for task-agnostic reasoning
paper_summary: Large language models (LLMs) exhibit in-context learning abilities which
  enable the same model to perform several tasks without any task-specific
  training. In contrast, traditional adaptation approaches, such as fine-tuning,
  modify the underlying models for each specific task. In-context learning,
  however, consistently underperforms task-specific tuning approaches even when
  presented with the same examples. While most existing approaches (e.g., prompt
  engineering) focus on the LLM's learned representations to patch this
  performance gap, our analysis actually reveal that LLM representations contain
  sufficient information to make good predictions. As such, we focus on the LLM's
  reasoning abilities and demonstrate that this performance gap exists due to
  their inability to perform simple probabilistic reasoning tasks. This raises an
  intriguing question: Are LLMs actually capable of learning how to reason in a
  task-agnostic manner? We answer this in the affirmative and propose TART which
  generically improves an LLM's reasoning abilities using a synthetically trained
  Transformer-based reasoning module. TART trains this reasoning module in a
  task-agnostic manner using only synthetic logistic regression tasks and
  composes it with an arbitrary real-world pre-trained model without any
  additional training. With a single inference module, TART improves performance
  across different model families (GPT-Neo, Pythia, BLOOM), model sizes (100M -
  6B), tasks (14 NLP binary classification tasks), and even across different
  modalities (audio and vision). Additionally, on the RAFT Benchmark, TART
  improves GPT-Neo (125M)'s performance such that it outperforms BLOOM (176B),
  and is within 4% of GPT-3 (175B). Our code and models are available at
  https://github.com/HazyResearch/TART .
paper_ai_keywords: large language models (LLMs) in-context learning task-specific training fine-tuning prompt engineering probabilistic reasoning TART Transformer-based reasoning module synthetic logistic regression tasks NLP binary classification tasks RAFT Benchmark GPT-Neo Pythia BLOOM GPT-3