Recently, the self-consistency decoding strategy has shown the ability to improve performance for complex reasoning tasks with large language models (LLMs). However, the costs may be high because the sampling process of the strategy generates some low-probability text, resulting in low-quality reasoning paths. As a consequence, it requires a relatively large sampling number to obtain good aggregation performance. In this paper, we propose an alternative strategy, \emph{self-para-consistency}. It first generates multiple paraphrases for each test question, then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding, and finally selects the most consistent answer. Since all the candidate paths have relatively high probabilities, the sampling number could be much smaller than the self-consistency strategy. Extensive experiments on complex reasoning datasets demonstrate the effectiveness of our method in reducing the sampling number.

Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models

Unsupervised text-graph alignment (UTGA) is a fundamental task that bidirectionally generates texts and graphs without parallel data. Most available models of UTGA suffer from information asymmetry, a common phenomenon that texts and graphs include additional information invisible to each other. On the one hand, these models fail to supplement asymmetric information effectively due to the lack of ground truths. On the other hand, it is challenging to indicate asymmetric information with explicit indicators because it cannot be decoupled from the data directly. To address the challenge posed by information asymmetry, we propose the assumption that asymmetric information is encoded in unobservable latent variables and only affects the one-way generation processes. These latent variables corresponding to asymmetric information should obey prior distributions recovered approximately from original data. Therefore, we first propose a taxonomy of the latent variable that classifies the latent variable into transferrable (TV) and non-transferable (NTV) variables and further distinguish NTV as the dependent variable (DV) and the independent variable (IV). Next, we propose three latent VAE-based regularizations on TV, DV, and IV to constrain their distributions to well-designed prior distributions to introduce asymmetric information into models and enhance the preservation of shared contents. Finally, we impose the three proposed constraints on a cycle-consistent learning framework, back-translation (BT), named ConstrainedBT. Experimental results on three UTGA tasks demonstrate the effectiveness of ConstrainedBT on the information-asymmetric challenge.

Latent Constraints on Unsupervised Text-Graph Alignment with Information Asymmetry

Conditional text generation is to generate text sequences conditioning on linguistic or non-linguistic data. The main line of existing work proposed deterministic models to improve the fidelity of the generated text but often ignored the diversity. Another line relied on conditional variational auto-encoders (CVAEs), which increased the diversity over their deterministic backbones. However, CVAEs regard diversity as an implicit objective and may not be optimal. In this paper, we raise two questions: 1) Can diversity be further improved with an explicit objective? 2) Since fidelity and diversity are two conflicting objectives, how can we obtain different multi-objective optimal solutions according to user preferences? To answer question 1), we propose a multi-objective reinforcement learning (MORL) method which explicitly takes CIDEr and Self-CIDEr scores as the fidelity-oriented and diversity-oriented rewards respectively. To answer question 2), we propose a preference-controlled MORL method, which can obtain infinite multi-objective optimal solutions by tuning the preference variable. We conduct extensive experiments on paraphrasing and image captioning tasks, which show that our model outperforms both deterministic and CVAE-based baselines in the fidelity-diversity trade-off space.

Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Wenqing Chen

3

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Presentations

Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models

Latent Constraints on Unsupervised Text-Graph Alignment with Information Asymmetry

Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation

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