Reasoning about Ambiguous Definite Descriptions (2023)

Abstract: Natural language reasoning plays an increasingly important role in improving language models’ ability to solve complex language understanding tasks. An interesting use case for reasoning is the resolution of context-dependent ambiguity. But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in language. We propose to use ambiguous definite descriptions for this purpose and create and publish the first benchmark dataset consisting of such phrases. Our method includes all information required to resolve the ambiguity in the prompt, which means a model does not require anything but reasoning to do well. We find this to be a challenging task for recent LLMs. Code and data available at: https://github.com/sfschouten/exploiting-ambiguity

## introduction natural language understanding and reasoning are interdependent skills: reasoning with natural language presupposes a level of understanding; but full understanding may require the resolution of ambiguities through reasoning. complex ambiguity in particular could benefit from explicit 'out loud' reasoning, such as the reasoning that is produced with chain-of-thought prompts. existing resources used to evaluate reasoning are not well suited to investigate the capability of resolving ambiguity by explicit reasoning. some existing benchmarks require the resolution of ambiguity, but focus only on ambiguity that humans can resolve intuitively (e.g. winograd schemas, @xcite . the ability to reason with natural language is often evaluated with tasks considered complex enough to require it. these may or may not include ambiguities of various types, making them poorly suited to evaluate when models are able to resolve ambiguity and which types. ## related work reasoning is a broad term and the tasks related to it are varied. we focus on the type of reasoning that is explicit and is done with natural language, which has recently been surveyed extensively @xcite @xcite . this paradigm became possible with the advent of large language models (llms) such as chat-gpt and gpt4 (openai, 2023). these models can be prompted to perform 'chain-of-thought' reasoning @xcite ; even in a zero-shot setting @xcite . this has significantly improved performance for many tasks @xcite . using a new dataset of ambiguous definite descriptions, our experiments evaluate to what extent llms can use (chain-of-thought) reasoning to resolve ambiguity in language. recent work on ambiguity includes the construction of both curated @xcite and synthetic datasets @xcite . such datasets investigate ambiguity in a variety of tasks such as: natural language inference, opendomain question answering, etc. our dataset is synthetically generated (but factual), and focuses specifically on de dicto / de re ambiguities, forming a binary classification task. the term 'logical reasoning over natural language' (lrnl) was coined by @xcite to talk about a new trend where natural language is used to represent knowledge and llms are used to reason over that knowledge. one challenge this paradigm could have to overcome is the potential for ambiguity inherent in natural language. our work intentionally introduces ambiguity, and evaluates how well llms can resolve it by reasoning. previously, winograd schemas @xcite have been used in various benchmarks to test for commonsense reasoning. these schemas consist of sentences with ambiguous pronouns which must be resolved in one of two ways. however, existing schemas focus on ambiguities that humans resolve intuitively (without the need for explicit reasoning). creating new schemas that do require explicit reasoning does not appear straightforward, especially since creating them has proved difficult in general @xcite . our method also involves ambiguous noun phrases, but requires explicit reasoning about definite descriptions rather than implicit reasoning about pronouns. ## ambiguous definite descriptions definite descriptions are noun phrases that denote entities by describing the properties or roles that are unique to them within the relevant context. examples of such phrases in english include among others: "the pope", "john's mother", and "our king". ## radd-wikidata-5-en to demonstrate the value of reasoning about ambiguous definite descriptions we create a (semi-)automatically generated dataset based on wikidata. this dataset is based on 5 wikidata property-pairs, and is in english. we focus on creating ambiguous sentences with a temporal operator. wikidata contains many triples qualified with the 'start time' and 'end time' properties to indicate the period in which they were true. in we prioritized finding a small diverse set of property-pairs. besides requiring a property whose value changes over time and a property whose value does not, the combination of properties also must yield sufficient results on wikidata (we required at least 50 to create the 100 samples). we also filtered out property-pairs where the results were dominated by a single entity, for example, had the data for the 'person-military_unit-size_class' pair consisted of only a handful of people changing units many different times, we would not have included it. see we include all facts relevant to the main entity in each prompt whether they are necessary to resolve the ambiguity or not, this allows us to measure if models can work around distractor facts. ## experiments we evaluate three dialogue-oriented large language models (llms) on our benchmark in a zero-shot setup. the models we include are: the evaluation is performed on 500 instances (100 per property-pair). to prompt the language models, we start with instances such as the one from the 'example' column in ## conclusion we have introduced reasoning about ambiguous definite descriptions as a way to evaluate how well systems can use natural language reasoning to resolve ambiguous language and have created the first benchmark dataset to demonstrate the value of this task. our findings show that recent llms are not yet capable of solving this task reliably, and that they particularly struggle with de re instances. our error analysis suggests that models-besides not always 'thinking step-by-step' as instructeddo not properly consider the de re options, instead hallucinating extra conditions for their correctness. in future work, we will expand our work by: (1) testing how changing the provided information (e.g. removing regularities) affects the performance; (2) embedding this problem in other tasks such that the ability to solve it depends on the resolution of the ambiguity; (3) constructing multi-lingual versions to evaluate if the ability to resolve ambiguity through reasoning is independent of the language; and (4) extending our method to other extensional operators, and other types of ambiguities.