Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses (2023)

Abstract: A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases in human decision-making, it is worth considering a broad differential diagnosis, going beyond the most likely options. We introduce a new task, “less likely brainstorming,” that asks a model to generate outputs that humans think are relevant but less likely to happen. We explore the task in two settings: a brain MRI interpretation generation setting and an everyday commonsense reasoning setting. We found that a baseline approach of training with less likely hypotheses as targets generates outputs that humans evaluate as either likely or irrelevant nearly half of the time; standard MLE training is not effective. To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans. We compare our method with several state-of-the-art controlled text generation models via automatic and human evaluations and show that our models’ capability of generating less likely outputs is improved.

## introduction cognitive errors occur when an abnormality is identified, but its importance is incorrectly understood, resulting in an incorrect final diagnosis @xcite . for example, radiologists may look for confirmatory evidence to support a diagnostic hypothesis and ignore or discount evidence that refutes the hypothesis (confirmation bias; @xcite ; @xcite ). one 1 code is available at https://github.com/ liyan06/brainstorm . what is less likely to happen after that? ## problem setting the problem we tackle in this work can be viewed as a controllable text generation task. let x be a premise or a brain mri report findings, we want a model to generate a likely/less likely hypothesis or interpretation y given an indicator i by drawing from the distribution p (y | x, i). the indicator i can take two values: + to indicate generating likely outputs and ∼ to generate less likely outputs. for example, given a premise @xmath0." in figure 1 from the e-care dataset (more details in section 5), we want a model to generate a hypothesis y ∼ that is less likely to happen (@xmath1, such as "he gets a promotion from his manager who saw him in the gym.". although this hypothesis fits into the same scenario as the premise as it directly connects to the premise involving tom's daily gym attendance, it is less likely to happen since the causal relationship between going to the gym and receiving a promotion is not common. the understanding of what is "less likely" can be based on the concept of bounded rationality @xcite , where likely hypotheses are those that are likely given known premises, but less likely hypotheses may stem from additional unknown premises. it is important to note that when we refer to an output as "less likely/likely", we mean that it is less likely/likely based on human understanding of x. all models we experiment with in this work generate outputs that have high probability according to the model, regardless of whether they are likely or less likely to happen according to humans. ## methodology in this section, we present our method as well as baseline models we compare against. requirements for these models can be found in ## experimental settings we investigate our methods in both brain mri settings and everyday commonsense reasoning settings ( ## evaluation on commonsense reasoning 6.1 automatic evaluation our first evaluation relies on automatically assessing whether system outputs are likely or less likely according to humans. we fine-tune deberta models @xcite for our automatic evaluation on two everyday commonsense datasets. they take the pair of (x, y) as input and predict whether y is a likely or less likely hypothesis. in our settings, can we just train on (x, y ∼ )? interestingly, the baseline model mle-ll that only trained on (x, y ∼ ) pairs generates "likely" hypotheses approximately half of the time. this is possibly an effect of the pre-training regimen; furthermore, generating likely hypotheses may be easier and past work has shown that seq2seq models can amplify behaviors like copying that are easy to learn @xcite . are the proposed two loss objectives effective? we see that compared to mle-ll, our proposed brainstorm method achieves substantially higher fractions of less likely hypotheses with no cost to quality in terms of perplexity. at the bottom of ## human evaluation on mriinterpret to evaluate the models' performance on the radiological interpretation generation setting, we select 30 findings from our validation set that ask for less likely interpretation. for each finding, we select the human reference and generate the top 5 less likely interpretations from 2 baselines (mle-ll and mle) and brainstorm ′ , resulting in 30 × (5 × 3 + 1) = 480 interpretations. we randomized the order of these interpretations before evaluation. due to the structure of the indicators in this dataset, methods that require examples to have both y + and y ∼ for the same data (see "pair" in for each generated interpretation, we evaluate its (1) relevancy to the findings and (2) whether it contains any hallucinations about findings (appendix e.2). for relevancy, we asked a neurologist to classify each interpretation into: (1) relevant and likely; (2) relevant and less likely; and (3) irrelevant. further, for those classified as "relevant and less likely", we further evaluate how well the interpretation fits into the context of the findings by grading them on three levels: high, medium and low, ranging from high matches that represent the most obvious less likely interpretations to low matches that represent relevant but exceedingly rare diagnosis. we provide detailed definitions for these model likely less likely irrel. high med. low mle-ll 6.7 40.7 21.2 14.7 16.7 mle 7.3 50.0 22.1 13.3 7.3 brainstorm ′ 6.7 42.0 32.6 8.7 10.0 reference 3.3 76.7 13.4 3.3 3.3 results are shown in by comparing the outcomes between human reference and brainstorm, we find that brain-storm tends to shift the distribution of generated interpretations towards generating lower matched interpretations, which effectively extends the beam of potential diagnoses that meet the criteria of "relevant but less likely" based on refuting findings. anecdotally, interpretations in this medium category reflect the sort of alternative hypotheses and "outside-the-box" suggestions that represent the original goal of our approach. ## conclusion in this work, we propose a new text generation task "less likely brainstorming" for reducing cognitive errors in interpreting findings of mri reports. we found that simply training on less likely data does not help with generating less likely interpretations and hence propose a novel cl method to tackle the problem. in two settings, we show that our proposed training technique can effectively generate more "less likely" hypotheses, producing interpre-tations that radiologists may not think of, outperforming past training-and decode-time modifications to generation models.