A hallmark of learning is generalization to novel instances. In speech, exposure to atypical pronunciation drives perceptual adjustment that can generalize to unheard tokens. Prior work has attributed constraints on generalization primarily to acoustic similarity between exposure and test contexts. We propose that generalization can also be understood as an inference problem: listeners must determine whether, and how strongly, a learned phonetic mapping should apply in a new context. We test this proposal using data from a recent experiment in which listeners were exposed to shifted vowel pronunciations and then tested on minimal pairs varying in lexical frequency. Learning effects appeared strongest when the exposure direction aligned with a high-frequency alternative in mixed-frequency pairs, and were absent for low-frequency pairs. The observed pattern could reflect token-level acoustic similarity, reliance on prior expectations, or frequency-dependent constraints in applying the learned mapping. We formalized these alternatives within a Bayesian belief-updating framework: a talker-specific model assuming full transfer, a mixture-of-expectations model that interpolates between the updated representation and the listener’s prior, and a hierarchical Bayesian model that deploys the updated representation with uncertainty. The talker-specific model captured most generalization patterns through its sensitivity to token-level acoustic properties, but overpredicted learning for low-frequency pairs. The hierarchical model best recovered the theoretically central exposure-control contrast pattern, suggesting that lexical frequency may constrain how learned representations are applied. Our results provide a computationally explicit framework for studying how contextual factors shape generalization in speech perception.

Countless decisions shape our lives, and it is crucial to understand the how and why behind them. In this paper, we introduce a new LLM decision-making framework called STRUX, which enhances LLM decision-making by providing structured explanations. These include favorable and adverse facts related to the decision, along with their respective strengths. STRUX begins by distilling lengthy information into a concise table of key facts. It then employs a series of self-reflection steps to determine which of these facts are pivotal, categorizing them as either favorable or adverse in relation to a specific decision. Lastly, we fine-tune an LLM to identify and prioritize these key facts to optimize decision-making. STRUX has been evaluated on the challenging task of forecasting stock investment decisions based on earnings call transcripts and demonstrated superior performance against strong baselines. It enhances decision transparency by allowing users to understand the impact of different factors, representing a meaningful step towards practical decision-making with LLMs.

LLMs are ideal for decision-making thanks to their ability to reason over long contexts. However, challenges arise when processing speech transcripts that describe complex scenarios, as they are verbose and include repetition, hedging, and vagueness. E.g., during a company’s earnings call, an executive might project a positive revenue outlook to reassure investors, despite uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a modular framework that constructs probabilistic factor profiles from complex scenarios. It then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in new situations. Our framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. This approach is particularly useful in areas such as consulting and financial deliberation, where making decisions under uncertainty is vital.