Hanjun Dai
My research focuses on deep learning with structured data, including generative modeling and combinatorial optimization, and the applications for domains including chemical engineering and programming languages.
Please see my Google Scholar page for the full list of publications.
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Universal Self-adaptive Prompting
Empirical Methods in Natural Language Processing (EMNLP) (2023)
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A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks when ground-truth labels are unavailable. In this study, we address this by presenting Universal Self-Adaptive Prompting (USP), an automatic prompt design approach specifically tailored for zero-shot learning (while compatible with few-shot). Requiring only a small amount of unlabeled data and an inference-only LLM, USP is highly versatile: to achieve universal prompting, USP categorizes a possible NLP task into one of the three possible task types and then uses a corresponding selector to select the most suitable queries and zero-shot model-generated responses as pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a fully automated way. We evaluate USP with PaLM and PaLM 2 models and demonstrate performances that are considerably stronger than standard zero-shot baselines and often comparable to or even superior to few-shot baselines across more than 40 natural language understanding, natural language generation, and reasoning tasks.
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Document understanding tasks, in particular, Visually-rich Document Entity Retrieval (VDER), have gained significant attention in recent years thanks to their broad applications in enterprise AI. However, publicly available data have been scarce for these tasks due to strict privacy constraints and high annotation costs. To make things worse, the non-overlapping entity spaces from different datasets hinder the knowledge transfer between document types. In this paper, we propose a method to collect massive-scale and weakly labeled data from the web to benefit the training of VDER models. The collected dataset, named DocumentNet, does not depend on specific document types or entity sets, making it universally applicable to all VDER tasks. The current DocumentNet consists of 30M documents spanning nearly 400 document types organized in a four-level ontology. Experiments on a set of broadly adopted VDER tasks show significant improvements when DocumentNet is incorporated into the pre-training for both classic and few-shot learning settings. With the recent emergence of large language models (LLMs), DocumentNet provides a large data source to extend their multimodal capabilities for VDER.
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Better Zero-Shot Reasoning with Self-Adaptive Prompting
Findings of the Association for Computational Linguistics: ACL 2023 (2023)
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Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few-shot and zero shot abilities: they either learn from a handful of handcrafted, completed responses (“in context examples”), or are prompted to reason spontaneously through specially designed triggers. Nonetheless, few-shot performance is sensitive to the choice of the examples, for which artisanal hand-crafted selection would require extensive effort, and in some cases, it might not even be possible to obtain relevant examples a-priori without expertise about the downstream tasks. On the other hand, most general and handcrafting-free, zero-shot performance is limited by the lack of guidance to the LLM. To address this, we propose Consistency-based Self-adaptive Prompting (COSP), a novel prompt design method for LLMs. Requiring neither handcrafted responses nor ground-truth labels, COSP selects & builds the set of examples from the LLM’s own zero-shot outputs via carefully designed criteria combining consistency, diversity and repetition. In zero-shot setting, with only LLM predictions, COSP significantly improves performance (up to 2× compared to zero-shot baselines and matching or exceeding few-shot baselines) in a range of reasoning tasks in 3 LLMs. Moreover, COSP can be generalized to few-shot setting and can take advantage of few labeled examples in an efficient way
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SQLPrompt: Improved In-context Learning for Few-shot Text-to-SQL
Findings of Conference on Empirical Methods in Natural Language Processing (EMNLP) (2023)
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Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. In this work, we propose "SQLPrompt", tailored to improve the few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs). Our methods
include innovative prompt design, execution based consistency decoding strategy which selects the SQL with the most consistent execution outcome among other SQL proposals, and a method that aims to improve performance by diversifying the SQL proposals during consistency selection with different prompt designs ("MixPrompt") and foundation models ("MixLLMs"). We show that SQLPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin, closing the gap with finetuning state-of the-art with thousands of labeled data.
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Designing a suitable representation for code-reasoning tasks is challenging in
aspects such as the kinds of program information to model, how to combine them,
and how much context to consider. We propose CodeTrek, a deep learning approach
that addresses these challenges by representing codebases as databases that conform
to rich relational schemas. The relational representation not only allows CodeTrek
to uniformly represent diverse kinds of program information, but also to leverage
program-analysis queries to derive new semantic relations, which can be readily
incorporated without further architectural engineering. CodeTrek embeds this
relational representation using a set of walks that can traverse different relations
in an unconstrained fashion, and incorporates all relevant attributes along the way.
We evaluate CodeTrek on four diverse and challenging Python tasks: variable
misuse, exception prediction, unused definition, and variable shadowing. CodeTrek
achieves an accuracy of 91%, 63%, 98%, and 94% on these tasks respectively, and
outperforms state-of-the-art neural models by 2--19% points.
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Extracting informative representations of molecules using Graph neural networks (GNNs) is crucial in AI-driven drug discovery. Recently, the graph research community has been trying to replicate the success of self-supervised pretraining in natural language processing, with several successes claimed. However, we find the benefit brought by self-supervised pretraining on small molecular data can be negligible in many cases. We conduct thorough ablation studies on the key components of GNN pretraining, including pretraining objectives, data splitting methods, input features, pretraining dataset scales, and GNN architectures, to see how they affect the accuracy of the downstream tasks. Our first important finding is, self-supervised graph pretraining do not always have statistically significant advantages over non-pretraining methods in many settings. Secondly, although noticeable improvement can be observed with additional supervised pretraining, the improvement may diminish with richer features or more balanced data splits. Thirdly, hyper-parameters could have larger impacts on accuracy of downstream tasks than the choice of pretraining tasks, especially when the scales of downstream tasks are small. Finally, we provide our conjectures where the complexity of some pretraining methods on small molecules might be insufficient, followed by empirical evidences on different pretraining datasets.
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Stochastic dual dynamic programming~(SDDP) is one of the state-of-the-art algorithm for multi-stage stochastic optimization, yet its cost exponentially increases w.r.t. the size of decision variables, therefore, quickly becomes inapplicable for high-dimension problems. We introduce a neuralized component into SDDP, which outputs a \emph{piece-wise linear function} in a \emph{low-dimension} space to approximate the value function, based on the \emph{context of the problem instances}. The neuralized component will consistently evolve to abstract effective low-dimension action space and improve the quality of value function approximation for each problem based on prior successful experiences. It is seamlessly integrated with SDDP, formed our neural enhanced solver,~\AlgName~(\algshort), which achieves the optimality \emph{without loss of accuracy} in \emph{faster speed} for high-dimension and long-horizon multi-stage stochastic optimizations. We conduct thorough empirical experiments to demonstrate the benefits of \algshort from transferability on scalability.~\algshort significantly outperforms the competitors, including SDDP and variants of RL algorithms, in terms of solution quality and feasibility, and computational speed.
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Learning to Walk over Relational Graphs of Source Code
Pardis Pashakhanloo
Aaditya Naik
Mayur Naik
Deep Learning for Code (DL4C) Workshop @ ICLR 2022 (2022)
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Information-rich relational graphs have shown great potential in designing effective representations of code for program-understanding tasks. However, the
wealth of structural and semantic information in such graphs can overwhelm models, because of their limited input size. A promising approach for overcoming this
challenge is to gather presumed-relevant but smaller context from a larger graph,
and random walks over graphs was one of the first such approaches discovered.
We propose a deep-learning approach that improves upon random walks by learning task-specific walk policies that guide the traversal of the graph towards the
most relevant context. In the setting of relational graphs representing programs
and their semantic properties, we observe that models that employ learned policies for guiding walks are 6--36% points more accurate than models that employ
uniform random walks, and 0.2--3.5% points more accurate than models that employ expert knowledge for guiding the walks.
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Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it non-trivial to adapt the score-based modeling to categorical data. In this paper, we extend diffusion models to discrete variables by introducing a stochastic jump process where the reverse process denoises via a continuous-time Markov chain. This formulation admits an analytical simulation during backward sampling. To learn the reverse process, we extend score matching to general categorical data, and show that an unbiased estimator can be obtained via simple matching of the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
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CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Kevin Ellis
International Conference on Learning Representations (ICLR) (2022) (to appear)
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Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches still explore a huge portion of the search space and quickly become intractable as the size of the desired program increases. To tame the search space blowup, we propose training a neural model to learn a hands-on search policy for bottom-up synthesis, instead of relying on a combinatorial search algorithm. Our approach, called CrossBeam, uses the neural model to choose how to combine previously-explored programs into new programs, taking into account the search history and partial program executions. Motivated by work in structured prediction on learning to search, CrossBeam is trained on-policy using data extracted from its own bottom-up searches on training tasks. We evaluate CrossBeam in two very different domains, string manipulation and logic programming. We observe that CrossBeam learns to search efficiently, exploring much smaller portions of the program space compared to the state-of-the-art.
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