Inductive Out-of-Context Reasoning (OOCR) in Large Language Models (LLMs): Its Capabilities, Challenges, and Implications for Artificial Intelligence (AI) Safety

Large Language Models (LLMs) are the so far greatest advancement in the field of Artificial Intelligence (AI). However, since these models are trained on extensive and varied corpora, they can unintentionally contain harmful information. This can sometimes also include instructions on how to make biological pathogens. It is necessary to eliminate every instance of this information from the training data in order to shield LLMs from acquiring such detrimental details. But even if explicit mentions of a dangerous fact are removed, the model can still detect implied and dispersed hints across the data. The worry is that an LLM might deduce the dangerous fact by piecing together these faint clues from several papers. 

This gives rise to the question of whether LLMs, like in Chain of Thought or Retrieval-Augmented Generation, can infer such information without explicit reasoning procedures. To address this,  a team of researchers from UC Berkeley, the University of Toronto, Vector Institute, Constellation, Northeastern University, and Anthropic have looked into a phenomenon called inductive out-of-context reasoning (OOCR). OOCR is the ability of LLMs to apply their inferred knowledge to new tasks without depending on in-context learning by deducing hidden information from fragmented evidence in the training data.

The study has shown that advanced LLMs are able to conduct OOCR using five different tasks. One famous experiment is fine-tuning an LLM on a dataset including only the distances between multiple known cities and an unknown city. With no formal reasoning methods such as Chain of Thought or in-context examples, the LLM is able to correctly identify the unfamiliar city as Paris. It then applies this understanding to respond to further inquiries concerning the city.

Additional tests have demonstrated the range of OOCR capabilities in LLMs. An LLM, for example, that has only been trained on the results of specific coin flips can identify and explain whether the coin is biassed. An additional experiment demonstrates that an LLM can construct the function and compute its inverses even in the absence of explicit examples or explanations when it is trained on pairs.

The team has also shared the limitations that accompany OOCR. When working with complex structures or smaller models, OOCR’s performance can be variable. This discrepancy emphasizes how difficult it is to guarantee trustworthy conclusions from LLMs.

The team has summarized their primary contributions as follows. 

The team has introduced OOCR, a new non-transparent way for LLMs to learn and reason, whereby the models deduce latent information from scattered evidence in training data.

To ensure a thorough assessment of this innovative reasoning approach, the team has developed a comprehensive suite of five demanding tests that are specifically intended to evaluate the inductive OOCR capabilities of LLMs.

The tests have shown that GPT-3.5 and GPT-4 are able to complete all five tasks with OOCR success. Furthermore, Llama 3 has been used to repeat these results for a single job, confirming the applicability of the findings.

The team has demonstrated that inductive OOCR performance can exceed that of in-context learning. GPT-4 exhibits superior inductive OOCR capabilities compared to GPT-3.5, highlighting advancements in model performance.

The robust OOCR capabilities of LLMs have important consequences for AI safety. Concerns regarding possible deception by misaligned models are raised by the fact that these models can learn and use knowledge in ways that are difficult for humans to monitor because the inferred information is not expressed clearly.

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