Selective “Selective Prediction”: Reducing Unnecessary Abstention in Vision-Language Reasoning (2024)

Abstract: Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain. However, when deploying a vision-language system with low tolerance for inaccurate predictions, selective prediction may be over-cautious and abstain too frequently, even on many correct predictions. We introduce ReCoVERR, an inference-time algorithm to reduce the over-abstention of a selective vision-language system without increasing the error rate of the system’s predictions. When the VLM makes a low-confidence prediction, instead of abstaining ReCoVERR tries to find relevant clues in the image that provide additional evidence for the prediction. ReCoVERR uses an LLM to pose related questions to the VLM, collects high-confidence evidences, and if enough evidence confirms the prediction the system makes a prediction instead of abstaining. ReCoVERR enables three VLMs (BLIP2, InstructBLIP and LLaVA-1.5) to answer up to 20% more questions on the VQAv2 and A-OKVQA tasks without decreasing system accuracy, thus improving overall system reliability. Our code is available at https://github.com/tejas1995/ReCoVERR.

## introduction instruction-tuned vision-and-language models (vlms) @xcite @xcite have achieved strong accuracy on reasoning benchmarks, which typically require vlms to produce an answer for each instance. for downstream use, however, these systems should abstain from answering when uncertain (e.g., by saying "i don't know") @xcite . selective prediction systems @xcite aim to balance the number of predictions made (coverage) and the error rate on predicted instances (risk). however, a vanilla selective prediction system with low tolerance for incorrect predictions will abstain too frequently to be practical, even when the model answer may be correct @xcite . for example, if a user specifies that the blip2 @xcite predictions should be right at least 90% of the time, vanilla selective prediction will make a prediction for just 4% of a-okvqa @xcite questions, with 94% of the correct predictions being abstained on. we introduce recoverr (reason by collecting visual evidences that are reliable and relevant), an algorithm that increases the number of questions that a selective vlm system can answer confidently while adhering to a specified risk tolerance, without any additional training. when the vlm is uncertain about its prediction for a given question, instead of abstaining outright, recoverr tries to verify the prediction by recovering reliable supporting (or contradicting) evidence. this ability is predicated on two characteristics of vlms. first, vlms can produce well-calibrated confidence estimates ( § 4.2.1). second, vlms can often correctly and confidently recover information in the image that entails a low-confidence initial prediction. for instance, in figure 1 , when asked about the number of floor tile colors, a vlm (here, blip2) correctly answers "two" but with low confidence: simple threshold-based abstention would abstain on this instance. but, when asked to identify the colors of the floor tiles, the model confidently answers "red and white". while it is obvious that "red and white" entails "two colors", the vlm was unable to confidently make that inference. based on these two insights, recoverr searches for additional visual evidence by iteratively posing relevant questions to the vlm, and collecting answers as visual evidence if they are: a) reliable, i.e, the vlm is highly confident in its answer, and b) relevant to the question the vlm is trying to answer in the first place. we experiment on the vqav2 and a-okvqa visual reasoning tasks using three vlms: blip2, instructblip and llava-1.5. for all three vlms, recoverr substantially increases the number of questions answered by the selective vlm system, while keeping system risk under the specified risk tolerance (section 5). recoverr is particularly helpful for blip2, which has not been trained on the target task, by improving coverage by 20% and recall by 25-30%. our analysis reveals that the ability to give accurate confidence estimates, and high estimates for correct predictions, is crucial to recoverr's performance. further experiments demonstrate the importance of ensuring evidences are both reliable and relevant (section 5.1), and that recoverr tuned for a single task can be directly applied to new tasks without further tuning (section 5.3). our findings suggest that recoverr is a promising solution towards building more reliable multimodal reasoning systems. ## multimodal selective prediction we consider the task of answering a textual question about an image by drawing inferences from what is visually observed. a vision-language model (vlm) is given an input @xmath0, q) ∈ x consisting of an image i and a question q, and aims to produce an answer @xmath1. different from popular benchmarks of this form which require models to make a prediction for each instance, we evaluate models on the selective pre-diction setting, where abstention on individual instances is allowed @xcite . a decision function g, which has access to the image, question, vlm prediction, and associated confidence foot_0 determines whether the system produces an answer or abstains (denoted by ∅). the selective vlm system s vlm : @xmath2: prior work @xcite ) employs confidence-based selection, where instead of assuming access to a vlm which has been trained to abstain with explicit supervision, we assume a vlm that can produce both an answer, as well as a confidence score for that answer π vlm : @xmath3, 1] that estimates π vlm (a) = p (a|q, i; m vlm ). confidence-based selection relies on the confidence π vlm (a) being higher for correct answers than incorrect ones, on average. the decision function g is a simple thresholding function, with a threshold γ: g(a; γ) = 1{π vlm (a) ≥ γ}. we refer to this method as vanilla selective prediction. ## experiments we instantiate a selective prediction task using the a-okvqa benchmark and compare recoverr to simple threshold-based selective prediction with two different backbone vlms with calibrated confidence estimates, using metrics that capture aspects and tradeoffs of risk and coverage. ## results and analysis we evaluate selective prediction methods on a-okvqa at two specified risk tolerances: 10% and 20% risk. t3&4 can be understood by examining the confidence distribution of the vlms' predictions (figure 4 ). we see that off-the-shelf blip2 has more predictions with greater than 80% confidence compared to calibrated blip2, with the trend reversed for instructblip. this means that recoverr finds more reliable evidences (π vlm (a) ≥ 1r) with off-the-shelf blip2 and calibrated instructblip, resulting in higher recall for recoverr with those two vlms, thus explaining t3&4. these findings indicate that recoverr works best when the vlm is strongly confident on correct answers. we also experiment on vqav2, at 10% risk tolerance. 4 in ## conclusion and related works we introduce recoverr, an algorithm to improve the coverage of a selective vlm system while respecting a user-specified risk tolerance level. recoverr verifies low-confidence vlm predictions by recovering high-confidence evidences in the image that support the prediction. we instantiate a selective prediction task on the a-okvqa reasoning benchmark and demonstrate the quantitative advantages of recoverr for inference-time selective prediction that holds across different back-evidences err collected by recoverr image initial prediction {llm question qj} {vlm answer aj} [πvlm(aj)] question: what kind of a person usually eats food like this? g.t. answers: vegetarian, vegan, healthy vlm: vegetarian (@xmath4.57) does the image depict a variety of plant-based ingredients? yes [0.95] does the food predominantly consist of fruits and vegetables? yes [0.80] are there any meat items in the image? no [0.86] result: correctly predicted question: the colors on the bus match the colors on what flag? g.t. answers: sweden, ukraine vlm: u.k. (@xmath5.53) is there any blue color on the bus? yes [0.95] what is the color of the bus? yellow and blue [0.93] result: correctly abstained a large breadth of prior work has studied the ability of models to abstain from answering @xcite @xcite . this ability is especially important when the model's prediction cannot be trusted @xcite , particularly for ood @xcite and adversarial @xcite inputs. similar to our work, low coverage caused by low risk tolerance has also been observed and mitigated in text-only selective prediction systems @xcite . in the multimodal domain, previous works have explored abstention for error recovery @xcite . @xcite establish the reliablevqa framework, wherein vqa models have the option to abstain from answering. @xcite evaluates vqa models on their ability to abstain when a question does not apply to the image. @xcite trains vqa models to abstain for outof-distribution inputs. in contrast to learning to abstain, we reduce abstention of multimodal selective prediction while maintaining reliability. using llms to query a pool of vision experts, either through program generation @xcite @xcite , or iterative querying @xcite @xcite , can decompose vision-language reasoning. we use llms to query an image for visual evidence, similar to repare @xcite which extracts additional visual context to rephrase underspecified vqa questions.