Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models (2024)

Abstract: Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. But even the best human conversationalist cannot perfectly anticipate the trajectory of a dialogue. How well can language models represent inherent uncertainty in conversations? We propose FortUne Dial, an expansion of the long-standing “conversation forecasting” task: instead of just accuracy, evaluation is conducted with uncertainty-aware metrics, effectively enabling abstention on individual instances. We study two ways in which language models potentially represent outcome uncertainty (internally, using scores and directly, using tokens) and propose fine-tuning strategies to improve calibration of both representations. Experiments on eight difficult negotiation corpora demonstrate that our proposed fine-tuning strategies (a traditional supervision strategy and an off-policy reinforcement learning strategy) can calibrate smaller open-source models to compete with pre-trained models 10x their size.

## introduction dialogue models are increasingly fluent, topical, and informative conversationalists, capable of predicting plausible next-utterances given a partial conversation. yet, the capacity to generate a single, plausible utterance is not the same as modeling the uncertainty about all possible next-utterances in a calibrated way -that is, assigning an appropriate probability to potential conversation outcomes, reflective of the randomness we observe in the real world. for example, in negotiations, "sounds good!" or "no thanks" may be equally fluent/topical/informative next-utterances, but one choice may be more likely if the goals, beliefs, and emotions of the interlocutors are taken into account. while even the best conversationalists cannot perfectly predict the trajectory of a dialogue, humans often manage uncertainty about social cues appropriately @xcite , and ## experiments data & splits we consider 8 modeling tasks spanning both traditional (distributive) negotiations and collaborative negotiations @xcite ; dataset situation outcome # speak # turn # char aff. distr. zhang et al. wikipedia editing personal @xmath4.2 2.5k yes no he et al. craigslist best deal for @xmath5.8 720 no yes chawla et al. camp provisions both camps @xmath6.4 1.2k yes yes chang et al. reddit personal @xmath7.3 3.2k yes no wang et al. charity donation @xmath8.6 2.2k yes no lewis et al. item allocation deal @xmath9.0 253 no yes mayfield et al. wikipedia editing article @xmath10.6 2.2k no no chang et al. a courtroom petitioner @xmath11: forecasting tasks. we list setting, outcome of interest, number of speakers, average turn/character count, and whether the setting is distributive. we also note if affective reasoning (about emotions) is useful in forecasting. data are grouped into 3 train-test splits (easy, med., hard) to simulate generalization difficulty (see tables 6, 7). a see also danescu-niculescu-mizil et al. (2012) for courtroom data. precise outcome definitions are in § b.1. post-hoc scaling no scaling combined @xmath0 @xmath1 prior data prior @xmath2 @xmath3 prior data prior bad prior neg all model bs ↓ bs ↓ bss ↑ bs ↓ bs ↓ bss ↑ bs ↓ bss ↑ bi bi gpt-4 df 21.1 20.7 8.5 23.8 21.8 3.8 22.7 9.0 -11 -1.1 70b if 22.9 23.1 -2.0 66.1 66.1 -182 66.1 -160 3.6 -49.4 70b df 22 22.1 2.3 25.0 22.0 2.7 23.6 5.0 -5.6 -1.3 to simulate varying degrees of distribution shift, we group these datasets into different train/test splits, categorized as easy, medium, or hard. to make the forecasting task more difficult, each of these three splits hold out full datasets for testing. conceptually, the splits are designed to create different degrees of train/test imbalance for important properties like the topic, the length of the conversation, the type of outcome, and the number of speakers; tables 6+7 and § b.1 provide more detail on train/val/test splits. sometimes, we restrict inference to data with affective conflict as outcome (neg) like, a personal attack or unhappy speaker. here, to compute some metrics, we swap the positive and negative classes as needed, e.g., the positive class becomes 1 -o to study "both camps unhappy" instead of "both camps happy." ## related works negotiation & conversation forecasting negotiation and dispute modeling has a long history @xcite @xcite with early works hand-crafting models of interlocutor behavior by logical or discourse structures. reinforcement learning in simulated environments offers improvement @xcite with most recent advances modeling opponents' dialogue acts @xcite , word choices @xcite , and mental states @xcite . instead of full simulation, we focus on efficient and interpretable outcome models @xcite . outcome models, or forecasts, are also common in broader dialogue for proactive moderation of social media @xcite as well as predicting task-success @xcite , mental health codes @xcite , emotions @xcite ; matero and schwartz, 2020), situated actions @xcite , and financials @xcite . among these, ours is first to propose and evaluate probabilistic methodology, modeling dynamic uncertainty for the first time. our proposal is also uniquely general, operating independent of setting or outcome of interest. indeed, we evaluate on general negotiations, looking beyond distributive applications (zero-sum games in specific markets) to include common collaborative negotiations @xcite , like planning, where parties share some goals, but conflicts arise from competing sub-goals or beliefs. language models & uncertainty modern large language models fine-tuned to human preferences @xcite are increasingly general, "unsupervised" multi-taskers. when queried on factual information, these models also represent uncertainty about their solutions with little to no supervision @xcite . uncertainty (about correctness) has also been studied in smaller models without preference tuning @xcite @xcite with many algorithms for improvement @xcite @xcite . albeit similarly operationalized, modeling uncertainty about factual correctness is distinct from our focus in negotiations, which elicits modeling of social dynamics and mental states. fewer works study uncertainty in social reasoning @xcite @xcite . these look at smaller models without instruction-tuning, and lack focus on the interactive, temporal aspects of conversations that cultivate an inherent uncertainty about future outcomes. ours is also one of few works that study how models communicate uncertainty directly via output tokens @xcite , and fewer that propose tuning algorithms for this @xcite . ## conclusion we show language models represent uncertainty about conversational outcomes quite well, depending on their size, inference strategy, training strategy, and access to prior knowledge. we design a task to evaluate this ability and show: