arXiv subjects

Shalin Shah

Publications and source records attributed to Shalin Shah.

16 recordsLinked to original sources

arXiv2026

SRAG: RAG with Structured Data Improves Vector Retrieval

Retrieval Augmented Generation (RAG) provides the necessary informational grounding to LLMs in the form of chunks retrieved from a vector database or through web search. RAG could also use knowledge graph triples as a means of providing factual information to an LLM. However, the retrieval is only based on representational similarity between a question and the contents. The performance of RAG depends on the numeric vector representations of the query and the chunks. To improve these representations, we propose Structured RAG (SRAG), which adds structured information to a query as well as the chunks in the form of topics, sentiments, query and chunk types (e.g., informational, quantitative), knowledge graph triples and semantic tags. Experiments indicate that this method significantly improves the retrieval process. Using GPT-5 as an LLM-as-a-judge, results show that the method improves the score given to answers in a question answering system by 30% (p-value = 2e-13) (with tighter bounds). The strongest improvement is in comparative, analytical and predictive questions. The results suggest that our method enables broader, more diverse, and episodic-style retrieval. Tail risk analysis shows that SRAG attains very large gains more often, with losses remaining minor in magnitude.

cs.IR↗

arXiv2024

Multi-Document Financial Question Answering using LLMs

We propose two new methods for multi-document financial question answering. First, a method that uses semantic tagging, and then, queries the index to get the context (RAG_SEM). And second, a Knowledge Graph (KG_RAG) based method that uses semantic tagging, and, retrieves knowledge graph triples from a graph database, as context. KG_RAG uses knowledge graphs constructed using a small model that is fine-tuned using knowledge distillation using a large teacher model. The data consists of 18 10K reports of Apple, Microsoft, Alphabet, NVIDIA, Amazon and Tesla for the years 2021, 2022 and 2023. The list of questions in the data consists of 111 complex questions including many esoteric questions that are difficult to answer and the answers are not completely obvious. As evaluation metrics, we use overall scores as well as segmented scores for measurement including the faithfulness, relevance, correctness, similarity, an LLM based overall score and the rouge scores as well as a similarity of embeddings. We find that both methods outperform plain RAG significantly. KG_RAG outperforms RAG_SEM in four out of nine metrics.

cs.IR↗

arXiv2024

Enhancing Question Answering on Charts Through Effective Pre-training Tasks

To completely understand a document, the use of textual information is not enough. Understanding visual cues, such as layouts and charts, is also required. While the current state-of-the-art approaches for document understanding (both OCR-based and OCR-free) work well, a thorough analysis of their capabilities and limitations has not yet been performed. Therefore, in this work, we addresses the limitation of current VisualQA models when applied to charts and plots. To investigate shortcomings of the state-of-the-art models, we conduct a comprehensive behavioral analysis, using ChartQA as a case study. Our findings indicate that existing models particularly underperform in answering questions related to the chart's structural and visual context, as well as numerical information. To address these issues, we propose three simple pre-training tasks that enforce the existing model in terms of both structural-visual knowledge, as well as its understanding of numerical questions. We evaluate our pre-trained model (called MatCha-v2) on three chart datasets - both extractive and abstractive question datasets - and observe that it achieves an average improvement of 1.7% over the baseline model.

cs.CL↗

arXiv2024

ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement

Structural extraction of events within discourse is critical since it avails a deeper understanding of communication patterns and behavior trends. Event argument extraction (EAE), at the core of event-centric understanding, is the task of identifying role-specific text spans (i.e., arguments) for a given event. Document-level EAE (DocEAE) focuses on arguments that are scattered across an entire document. In this work, we explore open-source Large Language Models (LLMs) for DocEAE, and propose ULTRA, a hierarchical framework that extracts event arguments more cost-effectively. Further, it alleviates the positional bias issue intrinsic to LLMs. ULTRA sequentially reads text chunks of a document to generate a candidate argument set, upon which non-pertinent candidates are dropped through self-refinement. We introduce LEAFER to address the challenge LLMs face in locating the exact boundary of an argument. ULTRA outperforms strong baselines, including strong supervised models and ChatGPT, by 9.8% when evaluated by Exact Match (EM).

cs.CL↗

arXiv2023

Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text

Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reveal that ATINTER is effective at providing better adversarial robustness than existing defense approaches, without compromising task accuracy. For example, on sentiment classification using the SST-2 dataset, our method improves the adversarial accuracy over the best existing defense approach by more than 4% with a smaller decrease in task accuracy (0.5% vs 2.5%). Moreover, we show that ATINTER generalizes across multiple downstream tasks and classifiers without having to explicitly retrain it for those settings. Specifically, we find that when ATINTER is trained to remove adversarial perturbations for the sentiment classification task on the SST-2 dataset, it even transfers to a semantically different task of news classification (on AGNews) and improves the adversarial robustness by more than 10%.

cs.CL↗

arXiv2021

Multi-Task Triplet Loss for Named Entity Recognition using Supplementary Text

Retail item data contains many different forms of text like the title of an item, the description of an item, item name and reviews. It is of interest to identify the item name in the other forms of text using a named entity tagger. However, the title of an item and its description are syntactically different (but semantically similar) in that the title is not necessarily a well formed sentence while the description is made up of well formed sentences. In this work, we use a triplet loss to contrast the embeddings of the item title with the description to establish a proof of concept. We find that using the triplet loss in a multi-task NER algorithm improves both the precision and recall by a small percentage. While the improvement is small, we think it is a step in the right direction of using various forms of text in a multi-task algorithm. In addition to precision and recall, the multi task triplet loss method is also found to significantly improve the exact match accuracy i.e. the accuracy of tagging the entire set of tokens in the text with correct tags.

cs.CL↗

arXiv2021

Multi-Task Learning of Query Intent and Named Entities using Transfer Learning

Named entity recognition (NER) has been studied extensively and the earlier algorithms were based on sequence labeling like Hidden Markov Models (HMM) and conditional random fields (CRF). These were followed by neural network based deep learning models. Recently, BERT has shown new state of the art accuracy in sequence labeling tasks like NER. In this short article, we study various approaches to task specific NER. Task specific NER has two components - identifying the intent of a piece of text (like search queries), and then labeling the query with task specific named entities. For example, we consider the task of labeling Target store locations in a search query (which could be entered in a search box or spoken in a device like Alexa or Google Home). Store locations are highly ambiguous and sometimes it is difficult to differentiate between say a location and a non-location. For example, "pickup my order at orange store" has "orange" as the store location, while "buy orange at target" has "orange" as a fruit. We explore this difficulty by doing multi-task learning which we call global to local transfer of information. We jointly learn the query intent (i.e. store lookup) and the named entities by using multiple loss functions in our BERT based model and find interesting results.

cs.CL↗

arXiv2020

Analysis of Greenhouse Gases

Climate change is a result of a complex system of interactions of greenhouse gases (GHG), the ocean, land, ice, and clouds. Large climate change models use several computers and solve several equations to predict the future climate. The equations may include simple polynomials to partial differential equations. Because of the uptake mechanism of the land and ocean, greenhouse gas emissions can take a while to affect the climate. The IPCC has published reports on how greenhouse gas emissions may affect the average temperature of the troposphere and the predictions show that by the end of the century, we can expect a temperature increase from 0.8 C to 5 C. In this article, I use Linear Regression (LM), Quadratic Regression and Gaussian Process Regression (GPR) on monthly GHG data going back several years and try to predict the temperature anomalies based on extrapolation. The results are quite similar to the IPCC reports.

physics.ao-ph↗

arXiv2020

Hebbian Graph Embeddings

Representation learning has recently been successfully used to create vector representations of entities in language learning, recommender systems and in similarity learning. Graph embeddings exploit the locality structure of a graph and generate embeddings for nodes which could be words in a language, products of a retail website; and the nodes are connected based on a context window. In this paper, we consider graph embeddings with an error-free associative learning update rule, which models the embedding vector of node as a non-convex Gaussian mixture of the embeddings of the nodes in its immediate vicinity with some constant variance that is reduced as iterations progress. It is very easy to parallelize our algorithm without any form of shared memory, which makes it possible to use it on very large graphs with a much higher dimensionality of the embeddings. We study the efficacy of proposed method on several benchmark data sets and favorably compare with state of the art methods. Further, proposed method is applied to generate relevant recommendations for a large retailer.

cs.LG↗

arXiv2020

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

The 0/1 multidimensional knapsack problem is the 0/1 knapsack problem with m constraints which makes it difficult to solve using traditional methods like dynamic programming or branch and bound algorithms. We present a genetic algorithm for the multidimensional knapsack problem with Java and C++ code that is able to solve publicly available instances in a very short computational duration. Our algorithm uses iteratively computed Lagrangian multipliers as constraint weights to augment the greedy algorithm for the multidimensional knapsack problem and uses that information in a greedy crossover in a genetic algorithm. The algorithm uses several other hyperparameters which can be set in the code to control convergence. Our algorithm improves upon the algorithm by Chu and Beasley in that it converges to optimum or near optimum solutions much faster.

cs.NE↗

arXiv2019

Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. The tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but its performance drops significantly on the open test sets due to the instability of trees. Several methods are explored to improve the robustness of the algorithm, with limited success.

cs.LG↗

arXiv2019

Genetic Algorithm for a class of Knapsack Problems

The 0/1 knapsack problem is weakly NP-hard in that there exist pseudo-polynomial time algorithms based on dynamic programming that can solve it exactly. There are also the core branch and bound algorithms that can solve large randomly generated instances in a very short amount of time. However, as the correlation between the variables is increased, the difficulty of the problem increases. Recently a new class of knapsack problems was introduced by D. Pisinger called the spanner knapsack instances. These instances are unsolvable by the core branch and bound instances; and as the size of the coefficients and the capacity constraint increase, the spanner instances are unsolvable even by dynamic programming based algorithms. In this paper, a genetic algorithm is presented for spanner knapsack instances. Results show that the algorithm is capable of delivering optimum solutions within a reasonable amount of computational duration.

cs.NE↗

arXiv2018

DNA-based chemical compiler

Marcello, in 1997, formally proved that chemical kinetics can make a universal computer i.e they can replicate any digital circuit. Recently, Soloveichik et al. showed that chemical kinetics can perform fast and reliable Turing Universal computations. To simulate the behavior of chemical reactions, Sean et al. developed a software called CAIN which represents chemical reactions in XML format. In this work, we have made an attempt to create trans-compiler which can take python like code as input and gives CAIN supported chemical reactions file as output. This can be compared to generating assembly code from a high-level programming language. Additionally, Soloveichik et al. also showed DNA as a universal primer for implementing CRN's and Andrews Phillips developed Visual DSD programming language for simulating all the possible DSD reactions. CRN2DSD, a software developed by Manish Gupta's team, can, already, convert a CAIN file to Microsoft's Visual DSD code i.e assembly level to machine level. Hence, our attempt to convert high-level code to assembly code takes us one step closer to completing the dream of making a chemical compiler.

cs.ET↗

arXiv2017

DNA Hairpin Gate: A Renewable DNA Seesaw Motif Using Hairpins

In 2011 the DNA seesaw gate motif was introduced. It is a powerful feed-forward DNA nanodevice that can perform digital logic computations. The landmark of that work managed to evaluate moderately large Boolean circuits by cascading multiple DNA seesaw gates. Although the design is robust in solution and scalable, it is designed for one time use and is not reusable. This prevents pursuing important applications such as feedback and sequential digital circuits. We present a novel design for DNA nanodevices that can perform digital logic computations and are furthermore renewable. First, we modified the prior DNA seesaw gate motif into a hairpin. We call the resulting motif a DNA hairpin-seesaw gate. We show the feed-forward digital computation reaction imitates the seesaw gate motif. Second, we added a reporting phase that provides increased scalability to our device. Third, we designed input and fuel extracting hairpins that when added, initiate a renewing process. This results in a renewed functional gate, in its original configuration, which is able to make a new logical computation with new inputs. Finally, we introduced a renewable two-input Boolean logic OR gate. After calculating the output of a certain input set, the circuit is restored and a new set of inputs is introduced to compute the new output. We provide experimental fluorescent data on three repeated rounds of executions of our hairpin gate motif and its restoration, indicating gradual loss of response. Finally, we calculated rate constants of our experimental data by fitting it to a second order reaction model using maximum likelihood estimation method.

cs.ET↗

arXiv2015

Computing Real Numbers using DNA Self-Assembly

DNA Self-Assembly has emerged as an interdisciplinary field with many intriguing applications such DNA bio-sensor, DNA circuits, DNA storage, drug delivery etc. Tile assembly model of DNA has been studied for various computational primitives such as addition, subtraction, multiplication, and division. Xuncai et. al. gave computational DNA tiles to perform division of a number but the output had integer quotient. In this work, we simply modify their method of division to improve its compatibility with further computation and this modification has found its application in computing rational numbers, both recurring and terminating, with computational tile complexity of $\mathcal{O} (1)$ and $\mathcal{O} (h)$ respectively. Additionally, we also propose a method to compute square-root of a number with computational tile complexity of $\mathcal{O} (n)$ for an n bit number. Finally, after combining tiles of division and square-root, we propose a simple way to compute the ubiquitously used irrational number, $π$, using its infinite series.

cs.ET↗

arXiv2014

DNACloud: A Potential Tool for storing Big Data on DNA

The term Big Data is usually used to describe huge amount of data that is generated by humans from digital media such as cameras, internet, phones, sensors etc. By building advanced analytics on the top of big data, one can predict many things about the user such as behavior, interest etc. However before one can use the data, one has to address many issues for big data storage. Two main issues are the need of large storage devices and the cost associated with it. Synthetic DNA storage seems to be an appropriate solution to address these issues of the big data. Recently in 2013, Goldman and his collegues from European Bioinformatics Institute demonstrated the use of the DNA as storage medium with capacity of storing 2.2 peta bytes of information on one gram of DNA and retrived the data successfully with low error rate. This significant step shows a promise for synthetic DNA storage as a useful technology for the future data storage. Motivated by this, we have developed a software called DNACloud which makes it easy to store the data on the DNA. In this work, we present detailed description of the software.

cs.ET↗