The Nonlinear Library AF - Connecting the Dots: LLMs can Infer & Verbalize Latent Structure from Training Data by Johannes Treutlein
Jun 21, 2024
14:59
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Connecting the Dots: LLMs can Infer & Verbalize Latent Structure from Training Data, published by Johannes Treutlein on June 21, 2024 on The AI Alignment Forum.
TL;DR: We published a new paper on out-of-context reasoning in LLMs. We show that LLMs can infer latent information from training data and use this information for downstream tasks, without any in-context learning or CoT. For instance, we finetune GPT-3.5 on pairs (x,f(x)) for some unknown function f. We find that the LLM can (a) define f in Python, (b) invert f, (c) compose f with other functions, for simple functions such as x+14, x // 3, 1.75x, and 3x+2.
Paper authors: Johannes Treutlein*, Dami Choi*, Jan Betley, Sam Marks, Cem Anil, Roger Grosse, Owain Evans (*equal contribution)
Johannes, Dami, and Jan did this project as part of an Astra Fellowship with Owain Evans.
Below, we include the Abstract and Introduction from the paper, followed by some additional discussion of our AI safety motivation, the implications of this work, and possible mechanisms behind our results.
Abstract
One way to address safety risks from large language models (LLMs) is to censor dangerous knowledge from their training data. While this removes the explicit information, implicit information can remain scattered across various training documents.
Could an LLM infer the censored knowledge by piecing together these implicit hints? As a step towards answering this question, we study inductive out-of-context reasoning (OOCR), a type of generalization in which LLMs infer latent information from evidence distributed across training documents and apply it to downstream tasks without in-context learning. Using a suite of five tasks, we demonstrate that frontier LLMs can perform inductive OOCR.
In one experiment we finetune an LLM on a corpus consisting only of distances between an unknown city and other known cities. Remarkably, without in-context examples or Chain of Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.
Further experiments show that LLMs trained only on individual coin flip outcomes can verbalize whether the coin is biased, and those trained only on pairs (x,f(x)) can articulate a definition of f and compute inverses. While OOCR succeeds in a range of cases, we also show that it is unreliable, particularly for smaller LLMs learning complex structures.
Overall, the ability of LLMs to "connect the dots" without explicit in-context learning poses a potential obstacle to monitoring and controlling the knowledge acquired by LLMs.
Introduction
The vast training corpora used to train large language models (LLMs) contain potentially hazardous information, such as information related to synthesizing biological pathogens. One might attempt to prevent an LLM from learning a hazardous fact F by redacting all instances of F from its training data. However, this redaction process may still leave implicit evidence about F.
Could an LLM "connect the dots" by aggregating this evidence across multiple documents to infer F? Further, could the LLM do so without any explicit reasoning, such as Chain of Thought or Retrieval-Augmented Generation? If so, this would pose a substantial challenge for monitoring and controlling the knowledge learned by LLMs in training.
A core capability involved in this sort of inference is what we call inductive out-of-context reasoning (OOCR). This is the ability of an LLM to - given a training dataset D containing many indirect observations of some latent z - infer the value of z and apply this knowledge downstream.
Inductive OOCR is out-of-context because the observations of z are only seen during training, not provided to the model in-context at test time; it is inductive because inferring the latent involves aggregating information from many training...
TL;DR: We published a new paper on out-of-context reasoning in LLMs. We show that LLMs can infer latent information from training data and use this information for downstream tasks, without any in-context learning or CoT. For instance, we finetune GPT-3.5 on pairs (x,f(x)) for some unknown function f. We find that the LLM can (a) define f in Python, (b) invert f, (c) compose f with other functions, for simple functions such as x+14, x // 3, 1.75x, and 3x+2.
Paper authors: Johannes Treutlein*, Dami Choi*, Jan Betley, Sam Marks, Cem Anil, Roger Grosse, Owain Evans (*equal contribution)
Johannes, Dami, and Jan did this project as part of an Astra Fellowship with Owain Evans.
Below, we include the Abstract and Introduction from the paper, followed by some additional discussion of our AI safety motivation, the implications of this work, and possible mechanisms behind our results.
Abstract
One way to address safety risks from large language models (LLMs) is to censor dangerous knowledge from their training data. While this removes the explicit information, implicit information can remain scattered across various training documents.
Could an LLM infer the censored knowledge by piecing together these implicit hints? As a step towards answering this question, we study inductive out-of-context reasoning (OOCR), a type of generalization in which LLMs infer latent information from evidence distributed across training documents and apply it to downstream tasks without in-context learning. Using a suite of five tasks, we demonstrate that frontier LLMs can perform inductive OOCR.
In one experiment we finetune an LLM on a corpus consisting only of distances between an unknown city and other known cities. Remarkably, without in-context examples or Chain of Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.
Further experiments show that LLMs trained only on individual coin flip outcomes can verbalize whether the coin is biased, and those trained only on pairs (x,f(x)) can articulate a definition of f and compute inverses. While OOCR succeeds in a range of cases, we also show that it is unreliable, particularly for smaller LLMs learning complex structures.
Overall, the ability of LLMs to "connect the dots" without explicit in-context learning poses a potential obstacle to monitoring and controlling the knowledge acquired by LLMs.
Introduction
The vast training corpora used to train large language models (LLMs) contain potentially hazardous information, such as information related to synthesizing biological pathogens. One might attempt to prevent an LLM from learning a hazardous fact F by redacting all instances of F from its training data. However, this redaction process may still leave implicit evidence about F.
Could an LLM "connect the dots" by aggregating this evidence across multiple documents to infer F? Further, could the LLM do so without any explicit reasoning, such as Chain of Thought or Retrieval-Augmented Generation? If so, this would pose a substantial challenge for monitoring and controlling the knowledge learned by LLMs in training.
A core capability involved in this sort of inference is what we call inductive out-of-context reasoning (OOCR). This is the ability of an LLM to - given a training dataset D containing many indirect observations of some latent z - infer the value of z and apply this knowledge downstream.
Inductive OOCR is out-of-context because the observations of z are only seen during training, not provided to the model in-context at test time; it is inductive because inferring the latent involves aggregating information from many training...