Sercan O. Arik
Sercan Arik is a Research Scientist at Google Cloud AI. Motivated by the mission of democratizing AI and bringing it to the most impactful use cases (from Healthcare, Finance, Retail, Media, Education, Communications and many other industries), he works on making AI high-performance for the most-demanded data types, interpretable, fair, data-efficient, robust and reliable.
Before joining Google, he was a Research Scientist at Baidu Silicon Valley AI Lab. At Baidu, he focused on deep learning research, particularly for applications in human-technology interfaces. He co-developed state-of-the-art speech synthesis, keyword spotting, voice cloning, and neural architecture search systems. Prior to Baidu, he completed a PhD degree in Electrical Engineering at Stanford University in 2016. He has co-authored more than 50 journal and conference publications.
Authored Publications
Sort By
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Ke Jiang
International Conference on Learning Representations (ICLR) (2025) (to appear)
Preview abstract
Recent advances in long-context large language models (LLMs) have led to the emerging paradigm of many-shot in-context learning (ICL), where it is observed that scaling many more demonstrating examples beyond the conventional few-shot setup in the context can lead to performance benefits. However, despite its promise, it is unclear what aspects dominate the benefits and whether simply scaling to more examples is the most effective way of improving many-shot ICL. In this work, we first provide an analysis of the factors driving many-shot ICL, and we find that 1) many-shot performance can still be attributed to often a few disproportionately influential examples and 2) identifying such influential examples ("optimize") and using them as demonstrations to regenerate new examples ("generate") can lead to further improvements. Inspired by the findings, we propose BRIDGE, an algorithm that alternates between the optimize step with Bayesian optimization to discover the influential sets of examples and the generate step to reuse this set to expand the reasoning paths of the examples back to the many-shot regime automatically. On Gemini, Claude, and Mistral LLMs of different sizes, we show that BRIDGE to significant improvements across a diverse set of tasks, including symbolic reasoning, numerical reasoning, and code generation.
View details
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
Advances in Neural Information Processing Systems (NeurIPS) (2024)
Preview abstract
Large language models have demonstrated remarkable capabilities, but their performance is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) methods are designed to automate this and can be broadly categorized into those targeting instructions (instruction optimization, IO) vs. those targeting exemplars (exemplar selection, ES). Despite their shared objective, these have evolved rather independently, with IO recently receiving more research attention. This paper seeks to bridge this gap by comprehensively comparing the performance of representative IO and ES techniques, both isolation and combination, on a diverse set of challenging tasks. Our findings reveal that intelligently reusing model-generated input-output pairs obtained from evaluating prompts on the validation set as exemplars consistently improves performance over IO methods but is currently under-investigated. We also find that despite the recent focus on IO, how we select exemplars can outweigh how we optimize instructions, with ES strategies as simple as random search outperforming state-of-the-art IO methods with seed instructions without any optimization. Moreover, we observe synergy between ES and IO, with optimal combinations surpassing individual contributions. We conclude that studying exemplar selection as a standalone method and its optimal combination with instruction optimization remains a crucial aspect of APO and deserves greater consideration in future research, even in the era of highly capable instruction-following models.
View details
Preview abstract
With development of Large Language Models (LLMs), collaboration between LLMs to solve complex tasks has attracted more and more attention. An important challenging task is reasoning from long text that cannot be input into LLMs. Thus far, limited research has explored how to solve long context tasks via pure multi-agent collaboration.
In this paper, we propose Chain-of-Agents (CoA), a novel framework that leverages the multi-agent collaboration via natural language to solve complex tasks. In CoA, the long text is split into chunks to be processed by agents repeatedly with appending the information from preceding agents. A manager model is finally employed to obtain the final answer utilizing the output of the last agent.
On wide range of datasets for long context question answering, summarization, and code completion and with many LLMs (including PaLM 2, Claude, and Gemini), we show that CoA framework outperforms strong baselines, including the commonly-used retrieval augmented generation (RAG) systems, by a large margin. For instance, text-bison obtains 13.30\% performance gain on NarrativeQA, and 10.22\% on MuSiQue dataset.
View details
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL
Satya Gundabathula
Hanjun Dai
TMLR (2024)
Preview abstract
Text-to-SQL, the process of translating natural language into Structured Query Language
(SQL), represents a transformative application of large language models (LLMs), potentially
revolutionizing how humans interact with data. This paper introduces the SQL-PaLM
framework, a comprehensive solution for understanding and enhancing Text-to-SQL using
LLMs, using in the learning regimes of few-shot prompting and instruction fine-tuning. With
few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error filtering. With instruction fine-tuning, we delve deep in understanding the critical
paradigms that influence the performance of tuned LLMs. In particular, we investigate
how performance can be improved through expanded training data coverage and diversity,
synthetic data augmentation, and integrating query-specific database content. We propose
a test-time selection method to further refine accuracy by integrating SQL outputs from
multiple paradigms with execution feedback as guidance. Additionally, we tackle the
practical challenge of navigating intricate databases with a significant number of tables and
columns, proposing efficient techniques for accurately selecting relevant database elements to
enhance Text-to-SQL performance. Our holistic approach yields substantial advancements
in Text-to-SQL, as demonstrated on two key public benchmarks, Spider and BIRD. Through
comprehensive ablations and error analyses, we shed light on the strengths and weaknesses
of our framework, offering valuable insights into Text-to-SQL’s future work.
View details
Preview abstract
Large language models (LLMs) have achieved remarkable advancements in natural language understanding, generation, and manipulation of text-based data. However, one major issue towards their widespread deployment in the real world is that they can generate "hallucinated" answers that are not factual. Towards this end, this paper focuses on improving grounding from a holistic perspective with a novel framework, AGREE. We start with the design of a test time adaptation capability that takes into account the support information generated in self-grounded responses. To effectively enable this capability, we propose that the model tuning needs to be redesigned with a novel tuning objective mimicking the test time adaptation setup for grounding. This tuning on top of the pre-trained LLMs requires small amount of data that need to be constructed in a particular way to learn the grounding information, for which we introduce a data construction method. Our results show that AGREE pushes the state-of-the-art in grounding, demonstrated across many datasets.
View details
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Somesh Jha
Transactions on Machine Learning Research (TMLR) (2024)
Preview abstract
Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. These predictions can then be deferred to humans for further evaluation. As an everlasting challenge for machine learning, in many real-world scenarios, the distribution of test data is different from the training data. This results in more inaccurate predictions, and often increased dependence on humans, which can be difficult and expensive. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. Selective prediction and active learning have been approached from different angles, with the connection between them missing. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain while increasing accuracy and coverage. For this new paradigm, we propose a simple yet effective approach, ASPEST, that utilizes ensembles of model snapshots with self-training with their aggregated outputs as pseudo labels. Extensive experiments on numerous image, text and structured datasets, which suffer from domain shifts, demonstrate that ASPEST can significantly outperform prior work on selective prediction and active learning (e.g. on the MNIST→SVHN benchmark with the labeling budget of 100, ASPEST improves the AUACC metric from 79.36% to 88.84%) and achieves more optimal utilization of humans in the loop.
View details
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Chun-Liang Li
Kihyuk Sohn
Transactions on Machine Learning Research (TMLR) (2023)
Preview abstract
Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that isn't limited by the assumption that labeled and unlabeled data come from the same distribution. Indeed, the assumption is often violated in many applications -- for example, the labeled data may contain only anomalies unlike unlabeled data, or unlabeled data may contain different types of anomalies, or labeled data may contain only `easy-to-label' samples. SPADE utilizes an ensemble of one class classifiers as the pseudo-labeler to improve the robustness of pseudo-labeling with distribution mismatch. Partial matching is proposed to automatically select the critical hyper-parameters for pseudo-labeling without validation data, which is crucial with limited labeled data. SPADE shows state-of-the-art semi-supervised anomaly detection performance across a wide range of scenarios with distribution mismatch in both tabular and image domains. In some common real-world settings such as model facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art alternatives by 5% AUC in average.
View details
Preview abstract
Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning models have become popular. However, recent work demonstrates that simple univariate linear models can outperform such deep learning models on several commonly used academic benchmarks. Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons (MLPs). TSMixer is based on mixing operations along both the time and feature dimensions to extract information efficiently. On popular academic benchmarks, the simple-to-implement TSMixer is comparable to specialized state-of-the-art models that leverage the inductive biases of specific benchmarks. On the challenging and large scale M5 benchmark, a real-world retail dataset, TSMixer demonstrates superior performance compared to the state-of-the-art alternatives. Our results underline the importance of efficiently utilizing cross-variate and auxiliary information for improving the performance of time series forecasting. We present various analyses to shed light into the capabilities of TSMixer. The design paradigms utilized in TSMixer are expected to open new horizons for deep learning-based time series forecasting. The implementation
is available at: https://github.com/google-research/google-research/tree/master/
tsmixer .
View details
Preview abstract
We propose a canonical approach for feature selection, sparse learnable masks (SLM). SLM integrates learnable sparse masks into end-to-end training. For the fundamental non-differentiability challenge of selecting a desired number of features, we propose duo mechanisms for automatic mask scaling to achieve the desired feature sparsity, and gradually tempering this sparsity for effective learning.
In addition, SLM employs a novel objective that maximizes the mutual information (MI) between the selected features and the labels, in an efficient and scalable way. Empirically, SLM achieves state-of-the-art results on several benchmark datasets, often by a significant margin, especially on real-world challenging datasets.
View details
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Somesh Jha
Findings of the Association for Computational Linguistics: EMNLP (2023)
Preview abstract
Large language models (LLMs) have recently shown great advances in a variety of tasks, including natural language understanding and generation. However, their use in high-stakes
decision-making scenarios is still limited due to the potential for errors. Selective prediction
is a technique that can be used to improve the reliability of the LLMs by allowing them to abstain from making predictions when they are unsure of the answer. In this work, we propose a novel framework for adaptation with self-evaluation to improve the selective prediction performance of LLMs. Our framework is based on the idea of using parameter-efficient tuning to adapt the LLM to the specific task at hand while improving its ability to perform self-evaluation. We evaluate our method on a variety of question-answering (QA) datasets and show that it outperforms state-of-the-art selective prediction methods. For example, on the CoQA benchmark, our method improves the AUACC from 91.23% to 92.63% and improves the AUROC from 74.61% to 80.25%.
View details
