The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs

Despite widespread use of LLMs as conversational agents, evaluations of performance fail to capture a crucial aspect of communication: interpreting language in context---incorporating its pragmatics. Humans interpret language using beliefs and prior knowledge about the world. For example, we intuitively understand the response "I wore gloves" to the question "Did you leave fingerprints?" as meaning "No". To investigate whether LLMs have the ability to make this type of inference, known as an implicature, we design a simple task and evaluate four categories of widely used state-of-the-art models. We find that, despite only evaluating on utterances that require a binary inference (yes or no), models in three of these categories perform close to random. However, LLMs instruction-tuned at the example-level perform significantly better. These results suggest that certain fine-tuning strategies are far better at inducing pragmatic understanding in models. We present our findings as the starting point for further research into evaluating how LLMs interpret language in context and to drive the development of more pragmatic and useful models of human discourse.

Do LLMs selectively encode the goal of an agent's reach?

In this work, we investigate whether large language models (LLMs) exhibit one of the earliest Theory of Mind-like behaviors: selectively encoding the goal object of an actor's reach (Woodward, 1998). We prompt state-of-the-art LLMs with ambiguous examples that can be explained both by an object or a location being the goal of an actor's reach, and evaluate the model's bias. We compare the magnitude of the bias in three situations: i) an agent is acting purposefully, ii) an inanimate object is acted upon, and iii) an agent is acting accidentally. We find that two models show a selective bias for agents acting purposefully, but are biased differently than humans. Additionally, the encoding is not robust to semantically equivalent prompt variations. We discuss how this bias compares to the bias infants show and provide a cautionary tale of evaluating machine Theory of Mind (ToM). We release our dataset and code.

Role-Play with Large Language Models

As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. In this paper, we foreground the concept of role-play. Casting dialogue agent behaviour in terms of role-play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models they in fact lack. Two important cases of dialogue agent behaviour are addressed this way, namely (apparent) deception and (apparent) self-awareness.