Selectively Answering Ambiguous Questions (2023)

Abstract: Trustworthy language models should abstain from answering questions when they do not know the answer. However, the answer to a question can be unknown for a variety of reasons. Prior research has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown. However, the answer to a question can also be unclear due to uncertainty of the questioner’s intent or context. We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous. In this setting, we find that the most reliable approach to calibration involves quantifying repetition within a set of sampled model outputs, rather than the model’s likelihood or self-verification as used in prior work. We find this to be the case across different types of uncertainty, varying model scales and both with or without instruction tuning. Our results suggest that sampling-based confidence scores help calibrate answers to relatively unambiguous questions, with more dramatic improvements on ambiguous questions.

## introduction any practical question answering (qa) system must be able to assess its own confidence so that it can (1) avoid making up incorrect, incomplete, or misleading answers, and (2) request clarification to resolve ambiguity arising from unclear questions or missing context. in recognition of this desirable property, recent work has addressed confidence estimation of large language models (llms) by evaluating qa tasks in unambiguous settings @xcite . in these experiments, questions are posed to the model with multiple-choice answers so the probability of each answer can be computed. here we extend the study of confidence to practical scenarios, requiring freeform text answers to arbitrary questions which may be underspecified or ambiguous. we describe a range of experiments to help disentangle uncertainty about the world from uncertainty about the question. while we focus on the question answering capabilities of large pretrained language models, these two forms of uncertainty can be more clearly distinguished in an idealized scenario in which a symbolic question-answering system first converts the question into a formal denotation (such as an sql query or lambda expression), which is then applied to a knowledge base (e.g., @xcite . the system may be uncertain about the meaning of the question -denotational uncertainty -if the question is underspecified because of assumed background knowledge or context. for any given denotation, the system may also be uncertain about the correct answer -epistemic uncertainty -if the knowledge base is incomplete foot_0 . in figure 1 , denotational uncertainty is shown in the upper fork and epistemic uncertainty is shown in the lower set of forks. the most competitive systems today do not construct explicit denotations of questions. as a result, it can be difficult to clearly separate these two forms of uncertainty. nonetheless, our general approach is to attempt to first approximately disambiguate the question (in natural language) and then selectively answer user questions with sufficiently high confidence. this allows the model to more effectively represent ambiguous user inputs. this scheme is represented in figure 1 . further, we argue that repeated sampling within our disambiguate-then-answer framework provides reliable confidence estimates of the model. we summarize our contributions as follows: who won the gold medal in the 2020 olympics in the men's foil? who won the gold medal in the 2016 olympics in fencing? who won the gold medal in the 2020 olympics in the men's sabre? cheung ka long áron szilágyi inna deriglazova emese szász 0.5 0.3 0.2 <@xmath0.4 0.6 0.6 0.4 0.3 0.3 0.4 uncertainty over interpretations denotational uncertainty uncertainty over answers given an interpretation epistemic uncertainty user query interpretation: who won the gold medal in the 2020 olympics in the men's foil? answer: cheung ka long model output figure 1 : uncertainty in question answering systems may arise in various ways. we propose a scheme called disambiguate then answer where the model first attempts to pose an unambiguous interpretation of the user question (yellow), then selectively produces an answer to this question, alternatively abstaining ("unknown") (green). the log likelihood of the model is shown above each generation. in addition, we find that sampling multiple times from the model generally allows for more robust confidence estimates. our experiments suggest that the answer probabilities given by current llms are somewhat helpful for assessing epistemic uncertainty but cannot detect denotational uncertainty. ## calibration for question answering a predictor is said to be well-calibrated if its predictions are accompanied by confidence scores and those scores are informative of the likelihood that the prediction is correct. such confidence scores can be used to support applications such as selective prediction, in which the model can abstain from predicting when its confidence is low @xcite . in selective question answering (e.g., @xcite , we assign each question-answer pair (q, a) a confidence score s(q, a). the system's output is then parameterized by a threshold τ , with ∅ representing abstention. given a probabilistic model p (a | q), a natural choice for the confidence score s(q, a) is the conditional probability of the answer. this and other scoring functions are discussed in section 3. for information-seeking queries such as those found in natural questions @xcite , the askers have no answer in mind, which makes it more likely that the questions are accidentally ambiguous. the task of answering an ambiguous question requires solving at least two subtasks: determining the meaning of the question (its denotation), and then identifying the answer. this pipeline is shown in figure 1 ; in a probabilistic model, this can be represented mathematically as ## confidence scores before proposing confidence scores for question answering, it will be helpful to review how these answers are generated in practical settings. a language model over a finite vocabulary σ defines a probability distribution x over sequences of to-kens in σ * , by modeling the marginal probability of a sequence p(σ 1 , • • • , σ n ) using the conditional probabilities of individual tokens via the chain rule for qa, answers are obtained by conditioning on the question q and first sampling from x (or a version of x adjusted with modifications such as temperature) and then applying a normalization or extraction process to it in order to map it to an answer space a. this function f : σ * → a can be as simple as punctuation removal and lower-casing, or as complex as code execution or extracting the answer from a chain-of-thought @xcite . using this background, we can describe various methods for estimating the scores of predictions from a language model (figure 2 ). ## evaluation setup our evaluations test the utility of the confidence scoring methods described in section 3. we focus mainly on the pretrained language model palm, which obtains high accuracy on several question answering datasets using few-shot in-context learning @xcite . ## unambiguous questions to start, we explore the case of unambiguous questions with a single answer. in this scenario, all uncertainty should be of the epistemic form; the denotation of the questions should be clear. previous work with this scenario has focused on the domain shift setting @xcite where the goal is to avoid answering questions from other datasets. other work has looked at reading comprehension with relatively small models @xcite . open-domain question answering is substantially more difficult than reading comprehension, which likewise makes calibration more challenging. ## ambiguous questions questions can be ambiguous for a variety of reasons. language is highly context-sensitive and meaning can be lost when context is missing. often, interlocutors fail to clearly translate their intent into unambiguous language. alternatively, ambiguity occurs due to a lack of alignment between interlocutors at a higher level of abstraction. regardless of its origin, ambiguous questions give rise to denotational uncertainty, which we can conceptualize as a latent distribution over interpretations. realistic scenarios such as search and chat frequently contain ambiguous questions, which makes calibration more difficult because uncertainty may be epistemic, denotational, or both. our approach will again involve sampling: this time over both interpretations and answers. note that in this case, the approach has some similarity to selfconsistency @xcite , as we compute sampling-based confidence scores over the final answers regardless of the exact interpretation. ## instuction tuning and scaling pretrained language models are typically fine-tuned before being used in applications. however, finetuning and other alignment techniques may disturb the model's calibration by emphasizing high performance on specific sets of tasks rather than the full pretraining distribution. 3 we examine the effect of model scale on calibration, finding that in general, accuracy declines substantially in closed book question answering, but calibration stays roughly constant. see appendix d for full results. as sampling-based approaches require a linear increase in compute for the number of samples, we also examine how calibration scales with the number. in particular, we test three, five, and eight samples and compare that to the original results containing ten samples. results can be found in appendix e. unsurprisingly, more samples seems to improve calibration, though it seems on the unambiguous natural questions slice, sampling diversity with a small number of samples works relatively well for the cost. ## related work this paper investigates calibration, selective question answering, and ambiguity within a single model. it builds on work across these topics. while ambigqa @xcite , asqa @xcite and situatedqa @xcite introduce new annotations for question answering datasets, neither these papers nor ## conclusion we investigate the calibration of large language models, extending prior work by distinguishing uncertainty about the answer (epistemic uncertainty) from uncertainty about the meaning of the question (denotational uncertainty). we propose a disambiguate-and-answer paradigm, where the model first attempts to rephrase the question before providing its answer. this paradigm enables straightforward techniques for quantifying model confidence by counting the frequency of answers within repeated samples from the language model. these sample-based confidence metrics are particularly effective for ambiguous questions, and are significantly better calibrated than traditional likelihood-based measures and self-verification. for ambiguous questions, the proposed method is similar to calibration through self-consistency.