We’re Afraid Language Models Aren’t Modeling Ambiguity (2023)

Abstract: Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators and revise our interpretations as listeners. As language models are increasingly employed as dialogue interfaces and writing aids, handling ambiguous language is critical to their success. We capture ambiguity in a sentence through its effect on entailment relations with another sentence, and collect AmbiEnt, a linguist-annotated benchmark of 1,645 examples with diverse kinds of ambiguity. We design a suite of tests based on AmbiEnt, presenting the first evaluation of pretrained LMs to recognize ambiguity and disentangle possible meanings. We find that the task remains extremely challenging, including for GPT-4, whose generated disambiguations are considered correct only 32% of the time in crowdworker evaluation, compared to 90% for disambiguations in our dataset. Finally, to illustrate the value of ambiguity-sensitive tools, we show that a multilabel NLI model can flag political claims in the wild that are misleading due to ambiguity. We encourage the field to rediscover the importance of ambiguity for NLP.

## introduction ambiguity seems to be an essential, indispensable element for the transfer of information from one place to another by words. @xcite , as referenced in the epilogue of @xcite ambiguity is an intrinsic feature of language, allowing speakers to balance efficiency and clarity in communication @xcite . language understanding thus requires recognizing the presence of multiple interpretations: as communicators, we anticipate the possibility of misunderstanding; as listeners, we ask clarifying questions, disambiguate meanings on the basis of a wide range of contextual factors, and backtrack and revise our earlier interpretations as needed. beyond unintended miscommunication, ambiguity is also an effective tool for sending covert messages, e.g., out of politeness or to mislead one's listeners while avoiding accountability (see figure 1 ). as language models (lms) are increasingly employed to act as dialogue agents @xcite or to aid human communication as writing aids @xcite , being able to work with ambiguous language will make them more effective. this skill would support adaptation to different contexts, clearer communication, and identification of misleading or deceptive language. yet, the ability of pretrained lms to recognize ambiguity and disentangle possible meanings remains unstudied, partly because ambiguous instances are systematically excluded in the curation of benchmarks @xcite . we present ambient, ambiguity in entailment, an english benchmark of 1,645 examples covering a variety of lexical, syntactic, and pragmatic ambiguities, and more broadly sentences which can be plausibly read as conveying one of multiple different messages. formally characterizing ambiguity requires a choice of meaning representation to distinguish between possible interpretations, and enumerating the full set of interpretations can be tricky or impractical. each ambient example consists of a premise and hypothesis pair, assigned a set of labels (among entailment, neutral, and contradiction), along with disambiguating rewrites corresponding to each label when multiple are plausible (see we design a suite of tests based on ambient to investigate the extent to which understanding of ambiguity is acquired during pretraining of large lms ( §4). these tests evaluate whether lms can directly produce relevant disambiguations, recognize possible interpretations, and model different interpretations in their continuation distributions. we find that these tasks remain extremely challenging, including for the recent @xcite . therefore, we additionally investigate whether lms can be finetuned on existing nli data for the less demanding task of ambiguity recognition, without explicit disambiguation ( §5). we adapt several finetuned nli models to a multilabel setting, and find that the best model predicts the exact label set in only 43.6% of instances, suggesting that the nli task is much more challenging when formulated to account for ambiguity. finally, to illustrate the value of ambiguitysensitive tools, we present a case study of how a multilabel nli model can be used to detect misleading political claims in the wild. we find that the strongest model from §5, despite its limitations, can not only recover claims flagged by fact-checkers as ambiguous, but highlight previously unidentified ambiguous claims, indicating the promise of such tools to aid real-world communication. the simplifying assumption that text has only one interpretation has facilitated the development of large-scale benchmarks, yet limits the depth of what these benchmarks can evaluate. in this work we show that sensitivity to ambiguity-a fundamental aspect of human language understandingis lacking in our ever-larger models, and illustrate the value such understanding could bring. ## ambient traditionally, the nli task requires predicting whether a premise entails, contradicts, or is neutral with respect to a hypothesis. yet, ambiguities in the premise and/or hypothesis (as in we present ambient, a dataset of 1,645 nli examples, each annotated with a set of labels, reflecting potentially multiple readings of the premise and/or hypothesis. ambiguous examples, i.e., those having more than one label, make up 35.2% of the dataset and include a disambiguating rewrite corresponding to each label; unambiguous examples have a single label. the inclusion of unambiguous examples facilitates evaluating model abilities to first detect the presence of relevant ambiguity, and then resolve it to distinct interpretations. we use two approaches to collect source examples: manual curation and automatic generation. manual curation ( §2.1) involves crafting a small set of examples targeting specific types of ambiguity. further, to cover more diverse forms of ambiguity, we produce a larger collection of examples via text generation and heuristic filtering ( §2.2), followed by expert manual annotation ( §2.3), forming the bulk of ambient. details are in §a. ## evaluating pretrained language models in our experiments, we investigate the extent to which understanding of ambiguity is acquired during the course of pretraining. our three tests evaluate if lms can directly generate relevant disambiguations ( §4.1), recognize the validity of plausible interpretations ( §4.2), and finally, model openended continuations reflecting different interpretations ( §4.3). for these tests, we consider only the ambiguous instances in ambient. as our set of lms, we evaluate llama (65b; touvron et al., 2023) and gpt-3 (davinci), as well as instruction-tuned models flan-t5 (xxl; chung @xcite , instructgpt (text-davinci-003), chatgpt (gpt-3.5-turbo), and the recent gpt-4. ## evaluating multilabel nli models given that language models still struggle to process ambiguity in §4, we next investigate the effectiveness of finetuning them on existing nli data collected in the line of work on underspecification and subjectivity in nli. 10 here, we consider the discriminative task of multilabel nli prediction, across both ambiguous and unambiguous examples in ambient. experimental details are in §d. ## case study: detecting misleading political claims we illustrate the value of ambiguity-sensitive models via a case study in detecting misleading political claims in the wild. here, we use the key insight that for ambiguous sentences, some paraphrases are naturally disambiguating, as paraphrases must either preserve the ambiguity or paraphrase a particular interpretation. therefore, if we cast a given sentence as the premise and a paraphrase as the hypothesis, a multilabel nli model predicting two or more labels should indicate the presence of ambiguity. moreover, the paraphrase resulting in this prediction should reveal the source of ambiguity. we experimentally evaluate this idea on the development set of claimdecomp @xcite , which contains 200 claims with their poli-tifact fact-checks. the authors read each instance and mark whether the fact-check describes an issue of ambiguity or factuality (regardless of whether we perceive ambiguity ourselves). then we paraphrase each claim 5 times with instructgpt zeroshot, and apply the multilabel wanli model from §5, which achieved the highest f1 score, on each resulting nli example. a claim is considered ambiguous if the model predicts more than one label for any paraphrase. examples in this method recalls 88.8% of ambiguous claims. while precision is lower at 12.4%, qualitative inspection of false positives reveals many ambiguities that were left unmentioned in the fact-check, illustrating the potential of these tools to anticipate sources of misunderstanding. ultimately, our analysis suggests that fact-checking as a more general problem may need refinement, due to the possible presence of both true and false interpretations. this case study shows only one use case of ambiguitysensitive models, and we hope for ambient for benchmark further progress on this front. ## related work ambiguity ambiguity is a longstanding and well-studied issue for nlp tasks involving symbolic analyses of sentences, such as syntactic and semantic parsing @xcite or coreference resolution @xcite . however, as the field has recently shifted toward higher-level understanding and reasoning problems, ambiguity in language has been largely overlooked. ## conclusion ambiguity in language will become increasingly conspicuous as we push the limits of lm capabilities and build tools that engage with the nuances of natural language communication. we develop the first benchmark to evaluate whether language models recognize different readings of ambiguous text, and demonstrate that the task remains extremely challenging. we encourage future work to study the sensitivity of lms to context and emphasis, investigate the presence of systematic biases in interpretation, and explore the promising space of real-world applications enabled by ambiguity-sensitive tools.